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How to Write a Discussion Section | Tips & Examples

Published on August 21, 2022 by Shona McCombes . Revised on July 18, 2023.

The discussion section is where you delve into the meaning, importance, and relevance of your results .

It should focus on explaining and evaluating what you found, showing how it relates to your literature review and paper or dissertation topic , and making an argument in support of your overall conclusion. It should not be a second results section.

There are different ways to write this section, but you can focus your writing around these key elements:

• Summary : A brief recap of your key results
• Interpretations: What do your results mean?
• Implications: Why do your results matter?
• Limitations: What can’t your results tell us?
• Recommendations: Avenues for further studies or analyses

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Table of contents

What not to include in your discussion section, step 1: summarize your key findings, step 2: give your interpretations, step 3: discuss the implications, step 4: acknowledge the limitations, step 5: share your recommendations, discussion section example, other interesting articles, frequently asked questions about discussion sections.

There are a few common mistakes to avoid when writing the discussion section of your paper.

• Don’t introduce new results: You should only discuss the data that you have already reported in your results section .
• Don’t make inflated claims: Avoid overinterpretation and speculation that isn’t directly supported by your data.
• Don’t undermine your research: The discussion of limitations should aim to strengthen your credibility, not emphasize weaknesses or failures.

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Start this section by reiterating your research problem and concisely summarizing your major findings. To speed up the process you can use a summarizer to quickly get an overview of all important findings. Don’t just repeat all the data you have already reported—aim for a clear statement of the overall result that directly answers your main research question . This should be no more than one paragraph.

Many students struggle with the differences between a discussion section and a results section . The crux of the matter is that your results sections should present your results, and your discussion section should subjectively evaluate them. Try not to blend elements of these two sections, in order to keep your paper sharp.

• The results indicate that…
• The study demonstrates a correlation between…
• This analysis supports the theory that…
• The data suggest that…

The meaning of your results may seem obvious to you, but it’s important to spell out their significance for your reader, showing exactly how they answer your research question.

The form of your interpretations will depend on the type of research, but some typical approaches to interpreting the data include:

• Identifying correlations , patterns, and relationships among the data
• Discussing whether the results met your expectations or supported your hypotheses
• Contextualizing your findings within previous research and theory
• Explaining unexpected results and evaluating their significance
• Considering possible alternative explanations and making an argument for your position

You can organize your discussion around key themes, hypotheses, or research questions, following the same structure as your results section. Alternatively, you can also begin by highlighting the most significant or unexpected results.

• In line with the hypothesis…
• Contrary to the hypothesized association…
• The results contradict the claims of Smith (2022) that…
• The results might suggest that x . However, based on the findings of similar studies, a more plausible explanation is y .

As well as giving your own interpretations, make sure to relate your results back to the scholarly work that you surveyed in the literature review . The discussion should show how your findings fit with existing knowledge, what new insights they contribute, and what consequences they have for theory or practice.

Ask yourself these questions:

• Do your results support or challenge existing theories? If they support existing theories, what new information do they contribute? If they challenge existing theories, why do you think that is?
• Are there any practical implications?

Your overall aim is to show the reader exactly what your research has contributed, and why they should care.

• These results build on existing evidence of…
• The results do not fit with the theory that…
• The experiment provides a new insight into the relationship between…
• These results should be taken into account when considering how to…
• The data contribute a clearer understanding of…
• While previous research has focused on  x , these results demonstrate that y .

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Even the best research has its limitations. Acknowledging these is important to demonstrate your credibility. Limitations aren’t about listing your errors, but about providing an accurate picture of what can and cannot be concluded from your study.

Limitations might be due to your overall research design, specific methodological choices , or unanticipated obstacles that emerged during your research process.

Here are a few common possibilities:

• If your sample size was small or limited to a specific group of people, explain how generalizability is limited.
• If you encountered problems when gathering or analyzing data, explain how these influenced the results.
• If there are potential confounding variables that you were unable to control, acknowledge the effect these may have had.

After noting the limitations, you can reiterate why the results are nonetheless valid for the purpose of answering your research question.

• The generalizability of the results is limited by…
• The reliability of these data is impacted by…
• Due to the lack of data on x , the results cannot confirm…
• The methodological choices were constrained by…
• It is beyond the scope of this study to…

Based on the discussion of your results, you can make recommendations for practical implementation or further research. Sometimes, the recommendations are saved for the conclusion .

Suggestions for further research can lead directly from the limitations. Don’t just state that more studies should be done—give concrete ideas for how future work can build on areas that your own research was unable to address.

• Further research is needed to establish…
• Future studies should take into account…
• Avenues for future research include…

If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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In the discussion , you explore the meaning and relevance of your research results , explaining how they fit with existing research and theory. Discuss:

• Your  interpretations : what do the results tell us?
• The  implications : why do the results matter?
• The  limitation s : what can’t the results tell us?

The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.

In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.

In a thesis or dissertation, the discussion is an in-depth exploration of the results, going into detail about the meaning of your findings and citing relevant sources to put them in context.

The conclusion is more shorter and more general: it concisely answers your main research question and makes recommendations based on your overall findings.

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The purpose of the discussion section is to interpret and describe the significance of your findings in relation to what was already known about the research problem being investigated and to explain any new understanding or insights that emerged as a result of your research. The discussion will always connect to the introduction by way of the research questions or hypotheses you posed and the literature you reviewed, but the discussion does not simply repeat or rearrange the first parts of your paper; the discussion clearly explains how your study advanced the reader's understanding of the research problem from where you left them at the end of your review of prior research.

Annesley, Thomas M. “The Discussion Section: Your Closing Argument.” Clinical Chemistry 56 (November 2010): 1671-1674; Peacock, Matthew. “Communicative Moves in the Discussion Section of Research Articles.” System 30 (December 2002): 479-497.

Importance of a Good Discussion

The discussion section is often considered the most important part of your research paper because it:

• Most effectively demonstrates your ability as a researcher to think critically about an issue, to develop creative solutions to problems based upon a logical synthesis of the findings, and to formulate a deeper, more profound understanding of the research problem under investigation;
• Presents the underlying meaning of your research, notes possible implications in other areas of study, and explores possible improvements that can be made in order to further develop the concerns of your research;
• Highlights the importance of your study and how it can contribute to understanding the research problem within the field of study;
• Presents how the findings from your study revealed and helped fill gaps in the literature that had not been previously exposed or adequately described; and,
• Engages the reader in thinking critically about issues based on an evidence-based interpretation of findings; it is not governed strictly by objective reporting of information.

Annesley Thomas M. “The Discussion Section: Your Closing Argument.” Clinical Chemistry 56 (November 2010): 1671-1674; Bitchener, John and Helen Basturkmen. “Perceptions of the Difficulties of Postgraduate L2 Thesis Students Writing the Discussion Section.” Journal of English for Academic Purposes 5 (January 2006): 4-18; Kretchmer, Paul. Fourteen Steps to Writing an Effective Discussion Section. San Francisco Edit, 2003-2008.

Structure and Writing Style

I.  General Rules

These are the general rules you should adopt when composing your discussion of the results :

• Do not be verbose or repetitive; be concise and make your points clearly
• Avoid the use of jargon or undefined technical language
• Follow a logical stream of thought; in general, interpret and discuss the significance of your findings in the same sequence you described them in your results section [a notable exception is to begin by highlighting an unexpected result or a finding that can grab the reader's attention]
• Use the present verb tense, especially for established facts; however, refer to specific works or prior studies in the past tense
• If needed, use subheadings to help organize your discussion or to categorize your interpretations into themes

II.  The Content

The content of the discussion section of your paper most often includes :

• Explanation of results : Comment on whether or not the results were expected for each set of findings; go into greater depth to explain findings that were unexpected or especially profound. If appropriate, note any unusual or unanticipated patterns or trends that emerged from your results and explain their meaning in relation to the research problem.
• References to previous research : Either compare your results with the findings from other studies or use the studies to support a claim. This can include re-visiting key sources already cited in your literature review section, or, save them to cite later in the discussion section if they are more important to compare with your results instead of being a part of the general literature review of prior research used to provide context and background information. Note that you can make this decision to highlight specific studies after you have begun writing the discussion section.
• Deduction : A claim for how the results can be applied more generally. For example, describing lessons learned, proposing recommendations that can help improve a situation, or highlighting best practices.
• Hypothesis : A more general claim or possible conclusion arising from the results [which may be proved or disproved in subsequent research]. This can be framed as new research questions that emerged as a consequence of your analysis.

III.  Organization and Structure

Keep the following sequential points in mind as you organize and write the discussion section of your paper:

• Think of your discussion as an inverted pyramid. Organize the discussion from the general to the specific, linking your findings to the literature, then to theory, then to practice [if appropriate].
• Use the same key terms, narrative style, and verb tense [present] that you used when describing the research problem in your introduction.
• Begin by briefly re-stating the research problem you were investigating and answer all of the research questions underpinning the problem that you posed in the introduction.
• Describe the patterns, principles, and relationships shown by each major findings and place them in proper perspective. The sequence of this information is important; first state the answer, then the relevant results, then cite the work of others. If appropriate, refer the reader to a figure or table to help enhance the interpretation of the data [either within the text or as an appendix].
• Regardless of where it's mentioned, a good discussion section includes analysis of any unexpected findings. This part of the discussion should begin with a description of the unanticipated finding, followed by a brief interpretation as to why you believe it appeared and, if necessary, its possible significance in relation to the overall study. If more than one unexpected finding emerged during the study, describe each of them in the order they appeared as you gathered or analyzed the data. As noted, the exception to discussing findings in the same order you described them in the results section would be to begin by highlighting the implications of a particularly unexpected or significant finding that emerged from the study, followed by a discussion of the remaining findings.
• Before concluding the discussion, identify potential limitations and weaknesses if you do not plan to do so in the conclusion of the paper. Comment on their relative importance in relation to your overall interpretation of the results and, if necessary, note how they may affect the validity of your findings. Avoid using an apologetic tone; however, be honest and self-critical [e.g., in retrospect, had you included a particular question in a survey instrument, additional data could have been revealed].
• The discussion section should end with a concise summary of the principal implications of the findings regardless of their significance. Give a brief explanation about why you believe the findings and conclusions of your study are important and how they support broader knowledge or understanding of the research problem. This can be followed by any recommendations for further research. However, do not offer recommendations which could have been easily addressed within the study. This would demonstrate to the reader that you have inadequately examined and interpreted the data.

IV.  Overall Objectives

The objectives of your discussion section should include the following: I.  Reiterate the Research Problem/State the Major Findings

Briefly reiterate the research problem or problems you are investigating and the methods you used to investigate them, then move quickly to describe the major findings of the study. You should write a direct, declarative, and succinct proclamation of the study results, usually in one paragraph.

II.  Explain the Meaning of the Findings and Why They are Important

No one has thought as long and hard about your study as you have. Systematically explain the underlying meaning of your findings and state why you believe they are significant. After reading the discussion section, you want the reader to think critically about the results and why they are important. You don’t want to force the reader to go through the paper multiple times to figure out what it all means. If applicable, begin this part of the section by repeating what you consider to be your most significant or unanticipated finding first, then systematically review each finding. Otherwise, follow the general order you reported the findings presented in the results section.

III.  Relate the Findings to Similar Studies

No study in the social sciences is so novel or possesses such a restricted focus that it has absolutely no relation to previously published research. The discussion section should relate your results to those found in other studies, particularly if questions raised from prior studies served as the motivation for your research. This is important because comparing and contrasting the findings of other studies helps to support the overall importance of your results and it highlights how and in what ways your study differs from other research about the topic. Note that any significant or unanticipated finding is often because there was no prior research to indicate the finding could occur. If there is prior research to indicate this, you need to explain why it was significant or unanticipated. IV.  Consider Alternative Explanations of the Findings

It is important to remember that the purpose of research in the social sciences is to discover and not to prove . When writing the discussion section, you should carefully consider all possible explanations for the study results, rather than just those that fit your hypothesis or prior assumptions and biases. This is especially important when describing the discovery of significant or unanticipated findings.

V.  Acknowledge the Study’s Limitations

It is far better for you to identify and acknowledge your study’s limitations than to have them pointed out by your professor! Note any unanswered questions or issues your study could not address and describe the generalizability of your results to other situations. If a limitation is applicable to the method chosen to gather information, then describe in detail the problems you encountered and why. VI.  Make Suggestions for Further Research

You may choose to conclude the discussion section by making suggestions for further research [as opposed to offering suggestions in the conclusion of your paper]. Although your study can offer important insights about the research problem, this is where you can address other questions related to the problem that remain unanswered or highlight hidden issues that were revealed as a result of conducting your research. You should frame your suggestions by linking the need for further research to the limitations of your study [e.g., in future studies, the survey instrument should include more questions that ask..."] or linking to critical issues revealed from the data that were not considered initially in your research.

NOTE: Besides the literature review section, the preponderance of references to sources is usually found in the discussion section . A few historical references may be helpful for perspective, but most of the references should be relatively recent and included to aid in the interpretation of your results, to support the significance of a finding, and/or to place a finding within a particular context. If a study that you cited does not support your findings, don't ignore it--clearly explain why your research findings differ from theirs.

V.  Problems to Avoid

• Do not waste time restating your results . Should you need to remind the reader of a finding to be discussed, use "bridge sentences" that relate the result to the interpretation. An example would be: “In the case of determining available housing to single women with children in rural areas of Texas, the findings suggest that access to good schools is important...," then move on to further explaining this finding and its implications.
• As noted, recommendations for further research can be included in either the discussion or conclusion of your paper, but do not repeat your recommendations in the both sections. Think about the overall narrative flow of your paper to determine where best to locate this information. However, if your findings raise a lot of new questions or issues, consider including suggestions for further research in the discussion section.
• Do not introduce new results in the discussion section. Be wary of mistaking the reiteration of a specific finding for an interpretation because it may confuse the reader. The description of findings [results section] and the interpretation of their significance [discussion section] should be distinct parts of your paper. If you choose to combine the results section and the discussion section into a single narrative, you must be clear in how you report the information discovered and your own interpretation of each finding. This approach is not recommended if you lack experience writing college-level research papers.
• Use of the first person pronoun is generally acceptable. Using first person singular pronouns can help emphasize a point or illustrate a contrasting finding. However, keep in mind that too much use of the first person can actually distract the reader from the main points [i.e., I know you're telling me this--just tell me!].

Analyzing vs. Summarizing. Department of English Writing Guide. George Mason University; Discussion. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Hess, Dean R. "How to Write an Effective Discussion." Respiratory Care 49 (October 2004); Kretchmer, Paul. Fourteen Steps to Writing to Writing an Effective Discussion Section. San Francisco Edit, 2003-2008; The Lab Report. University College Writing Centre. University of Toronto; Sauaia, A. et al. "The Anatomy of an Article: The Discussion Section: "How Does the Article I Read Today Change What I Will Recommend to my Patients Tomorrow?” The Journal of Trauma and Acute Care Surgery 74 (June 2013): 1599-1602; Research Limitations & Future Research . Lund Research Ltd., 2012; Summary: Using it Wisely. The Writing Center. University of North Carolina; Schafer, Mickey S. Writing the Discussion. Writing in Psychology course syllabus. University of Florida; Yellin, Linda L. A Sociology Writer's Guide . Boston, MA: Allyn and Bacon, 2009.

Writing Tip

Don’t Over-Interpret the Results!

Interpretation is a subjective exercise. As such, you should always approach the selection and interpretation of your findings introspectively and to think critically about the possibility of judgmental biases unintentionally entering into discussions about the significance of your work. With this in mind, be careful that you do not read more into the findings than can be supported by the evidence you have gathered. Remember that the data are the data: nothing more, nothing less.

MacCoun, Robert J. "Biases in the Interpretation and Use of Research Results." Annual Review of Psychology 49 (February 1998): 259-287; Ward, Paulet al, editors. The Oxford Handbook of Expertise . Oxford, UK: Oxford University Press, 2018.

Another Writing Tip

Don't Write Two Results Sections!

One of the most common mistakes that you can make when discussing the results of your study is to present a superficial interpretation of the findings that more or less re-states the results section of your paper. Obviously, you must refer to your results when discussing them, but focus on the interpretation of those results and their significance in relation to the research problem, not the data itself.

Azar, Beth. "Discussing Your Findings."  American Psychological Association gradPSYCH Magazine (January 2006).

Yet Another Writing Tip

Avoid Unwarranted Speculation!

The discussion section should remain focused on the findings of your study. For example, if the purpose of your research was to measure the impact of foreign aid on increasing access to education among disadvantaged children in Bangladesh, it would not be appropriate to speculate about how your findings might apply to populations in other countries without drawing from existing studies to support your claim or if analysis of other countries was not a part of your original research design. If you feel compelled to speculate, do so in the form of describing possible implications or explaining possible impacts. Be certain that you clearly identify your comments as speculation or as a suggestion for where further research is needed. Sometimes your professor will encourage you to expand your discussion of the results in this way, while others don’t care what your opinion is beyond your effort to interpret the data in relation to the research problem.

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6 Steps to Write an Excellent Discussion in Your Manuscript

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Table of Contents

The discussion section in scientific manuscripts might be the last few paragraphs, but its role goes far beyond wrapping up. It’s the part of an article where scientists talk about what they found and what it means, where raw data turns into meaningful insights. Therefore, discussion is a vital component of the article.  

An excellent discussion is well-organized. We bring to you authors a classic 6-step method for writing discussion sections, with examples to illustrate the functions and specific writing logic of each step. Take a look at how you can impress journal reviewers with a concise and focused discussion section!  

Discussion frame structure   

Conventionally, a discussion section has three parts: an introductory paragraph, a few intermediate paragraphs, and a conclusion¹.  Please follow the steps below:  

1.Introduction—mention gaps in previous research¹⁻ ²

Here, you orient the reader to your study. In the first paragraph, it is advisable to mention the research gap your paper addresses.  

Example: This study investigated the cognitive effects of a meat-only diet on adults. While earlier studies have explored the impact of a carnivorous diet on physical attributes and agility, they have not explicitly addressed its influence on cognitively intense tasks involving memory and reasoning.  

2. Summarizing key findings—let your data speak ¹⁻ ²

After you have laid out the context for your study, recapitulate some of its key findings. Also, highlight key data and evidence supporting these findings.  

Example: We found that risk-taking behavior among teenagers correlates with their tendency to invest in cryptocurrencies. Risk takers in this study, as measured by the Cambridge Gambling Task, tended to have an inordinately higher proportion of their savings invested as crypto coins.  

3. Interpreting results—compare with other papers¹⁻²    

Here, you must analyze and interpret any results concerning the research question or hypothesis. How do the key findings of your study help verify or disprove the hypothesis? What practical relevance does your discovery have?  

Example: Our study suggests that higher daily caffeine intake is not associated with poor performance in major sporting events. Athletes may benefit from the cardiovascular benefits of daily caffeine intake without adversely impacting performance.    

Remember, unlike the results section, the discussion ideally focuses on locating your findings in the larger body of existing research. Hence, compare your results with those of other peer-reviewed papers.  

Example: Although Miller et al. (2020) found evidence of such political bias in a multicultural population, our findings suggest that the bias is weak or virtually non-existent among politically active citizens.  

4. Addressing limitations—their potential impact on the results¹⁻²    

Discuss the potential impact of limitations on the results. Most studies have limitations, and it is crucial to acknowledge them in the intermediary paragraphs of the discussion section. Limitations may include low sample size, suspected interference or noise in data, low effect size, etc.  

Example: This study explored a comprehensive list of adverse effects associated with the novel drug ‘X’. However, long-term studies may be needed to confirm its safety, especially regarding major cardiac events.  

5. Implications for future research—how to explore further¹⁻²    

Locate areas of your research where more investigation is needed. Concluding paragraphs of the discussion can explain what research will likely confirm your results or identify knowledge gaps your study left unaddressed.  

Example: Our study demonstrates that roads paved with the plastic-infused compound ‘Y’ are more resilient than asphalt. Future studies may explore economically feasible ways of producing compound Y in bulk.  

6. Conclusion—summarize content¹⁻²    

A good way to wind up the discussion section is by revisiting the research question mentioned in your introduction. Sign off by expressing the main findings of your study.  

Example: Recent observations suggest that the fish ‘Z’ is moving upriver in many parts of the Amazon basin. Our findings provide conclusive evidence that this phenomenon is associated with rising sea levels and climate change, not due to elevated numbers of invasive predators.  

A rigorous and concise discussion section is one of the keys to achieving an excellent paper. It serves as a critical platform for researchers to interpret and connect their findings with the broader scientific context. By detailing the results, carefully comparing them with existing research, and explaining the limitations of this study, you can effectively help reviewers and readers understand the entire research article more comprehensively and deeply¹⁻² , thereby helping your manuscript to be successfully published and gain wider dissemination.  

In addition to keeping this writing guide, you can also use Elsevier Language Services to improve the quality of your paper more deeply and comprehensively. We have a professional editing team covering multiple disciplines. With our profound disciplinary background and rich polishing experience, we can significantly optimize all paper modules including the discussion, effectively improve the fluency and rigor of your articles, and make your scientific research results consistent, with its value reflected more clearly. We are always committed to ensuring the quality of papers according to the standards of top journals, improving the publishing efficiency of scientific researchers, and helping you on the road to academic success. Check us out here !  

Type in wordcount for Standard Total: USD EUR JPY Follow this link if your manuscript is longer than 12,000 words. Upload  

References:   

• Masic, I. (2018). How to write an efficient discussion? Medical Archives , 72(3), 306. https://doi.org/10.5455/medarh.2018.72.306-307  
• Şanlı, Ö., Erdem, S., & Tefik, T. (2014). How to write a discussion section? Urology Research & Practice , 39(1), 20–24. https://doi.org/10.5152/tud.2013.049  

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How to Write the Discussion Section of a Research Paper

The discussion section of a research paper analyzes and interprets the findings, provides context, compares them with previous studies, identifies limitations, and suggests future research directions.

Updated on September 15, 2023

Structure your discussion section right, and you’ll be cited more often while doing a greater service to the scientific community. So, what actually goes into the discussion section? And how do you write it?

The discussion section of your research paper is where you let the reader know how your study is positioned in the literature, what to take away from your paper, and how your work helps them. It can also include your conclusions and suggestions for future studies.

First, we’ll define all the parts of your discussion paper, and then look into how to write a strong, effective discussion section for your paper or manuscript.

Discussion section: what is it, what it does

The discussion section comes later in your paper, following the introduction, methods, and results. The discussion sets up your study’s conclusions. Its main goals are to present, interpret, and provide a context for your results.

What is it?

The discussion section provides an analysis and interpretation of the findings, compares them with previous studies, identifies limitations, and suggests future directions for research.

This section combines information from the preceding parts of your paper into a coherent story. By this point, the reader already knows why you did your study (introduction), how you did it (methods), and what happened (results). In the discussion, you’ll help the reader connect the ideas from these sections.

Why is it necessary?

The discussion provides context and interpretations for the results. It also answers the questions posed in the introduction. While the results section describes your findings, the discussion explains what they say. This is also where you can describe the impact or implications of your research.

Adds context for your results

Most research studies aim to answer a question, replicate a finding, or address limitations in the literature. These goals are first described in the introduction. However, in the discussion section, the author can refer back to them to explain how the study's objective was achieved. 

Shows what your results actually mean and real-world implications

The discussion can also describe the effect of your findings on research or practice. How are your results significant for readers, other researchers, or policymakers?

What to include in your discussion (in the correct order)

A complete and effective discussion section should at least touch on the points described below.

Summary of key findings

The discussion should begin with a brief factual summary of the results. Concisely overview the main results you obtained.

Begin with key findings with supporting evidence

Your results section described a list of findings, but what message do they send when you look at them all together?

Your findings were detailed in the results section, so there’s no need to repeat them here, but do provide at least a few highlights. This will help refresh the reader’s memory and help them focus on the big picture.

Read the first paragraph of the discussion section in this article (PDF) for an example of how to start this part of your paper. Notice how the authors break down their results and follow each description sentence with an explanation of why each finding is relevant. 

State clearly and concisely

Following a clear and direct writing style is especially important in the discussion section. After all, this is where you will make some of the most impactful points in your paper. While the results section often contains technical vocabulary, such as statistical terms, the discussion section lets you describe your findings more clearly. 

Interpretation of results

Once you’ve given your reader an overview of your results, you need to interpret those results. In other words, what do your results mean? Discuss the findings’ implications and significance in relation to your research question or hypothesis.

Analyze and interpret your findings

Look into your findings and explore what’s behind them or what may have caused them. If your introduction cited theories or studies that could explain your findings, use these sources as a basis to discuss your results.

For example, look at the second paragraph in the discussion section of this article on waggling honey bees. Here, the authors explore their results based on information from the literature.

Unexpected or contradictory results

Sometimes, your findings are not what you expect. Here’s where you describe this and try to find a reason for it. Could it be because of the method you used? Does it have something to do with the variables analyzed? Comparing your methods with those of other similar studies can help with this task.

Context and comparison with previous work

Refer to related studies to place your research in a larger context and the literature. Compare and contrast your findings with existing literature, highlighting similarities, differences, and/or contradictions.

How your work compares or contrasts with previous work

Studies with similar findings to yours can be cited to show the strength of your findings. Information from these studies can also be used to help explain your results. Differences between your findings and others in the literature can also be discussed here. 

How to divide this section into subsections

If you have more than one objective in your study or many key findings, you can dedicate a separate section to each of these. Here’s an example of this approach. You can see that the discussion section is divided into topics and even has a separate heading for each of them. 

Limitations

Many journals require you to include the limitations of your study in the discussion. Even if they don’t, there are good reasons to mention these in your paper.

Why limitations don’t have a negative connotation

A study’s limitations are points to be improved upon in future research. While some of these may be flaws in your method, many may be due to factors you couldn’t predict.

Examples include time constraints or small sample sizes. Pointing this out will help future researchers avoid or address these issues. This part of the discussion can also include any attempts you have made to reduce the impact of these limitations, as in this study .

How limitations add to a researcher's credibility

Pointing out the limitations of your study demonstrates transparency. It also shows that you know your methods well and can conduct a critical assessment of them.  

Implications and significance

The final paragraph of the discussion section should contain the take-home messages for your study. It can also cite the “strong points” of your study, to contrast with the limitations section.

Restate your hypothesis

Remind the reader what your hypothesis was before you conducted the study. 

How was it proven or disproven?

Identify your main findings and describe how they relate to your hypothesis.

How your results contribute to the literature

Were you able to answer your research question? Or address a gap in the literature?

Future implications of your research

Describe the impact that your results may have on the topic of study. Your results may show, for instance, that there are still limitations in the literature for future studies to address. There may be a need for studies that extend your findings in a specific way. You also may need additional research to corroborate your findings. 

Sample discussion section

This fictitious example covers all the aspects discussed above. Your actual discussion section will probably be much longer, but you can read this to get an idea of everything your discussion should cover.

Our results showed that the presence of cats in a household is associated with higher levels of perceived happiness by its human occupants. These findings support our hypothesis and demonstrate the association between pet ownership and well-being. 

The present findings align with those of Bao and Schreer (2016) and Hardie et al. (2023), who observed greater life satisfaction in pet owners relative to non-owners. Although the present study did not directly evaluate life satisfaction, this factor may explain the association between happiness and cat ownership observed in our sample.

Our findings must be interpreted in light of some limitations, such as the focus on cat ownership only rather than pets as a whole. This may limit the generalizability of our results.

Nevertheless, this study had several strengths. These include its strict exclusion criteria and use of a standardized assessment instrument to investigate the relationships between pets and owners. These attributes bolster the accuracy of our results and reduce the influence of confounding factors, increasing the strength of our conclusions. Future studies may examine the factors that mediate the association between pet ownership and happiness to better comprehend this phenomenon.

This brief discussion begins with a quick summary of the results and hypothesis. The next paragraph cites previous research and compares its findings to those of this study. Information from previous studies is also used to help interpret the findings. After discussing the results of the study, some limitations are pointed out. The paper also explains why these limitations may influence the interpretation of results. Then, final conclusions are drawn based on the study, and directions for future research are suggested.

How to make your discussion flow naturally

If you find writing in scientific English challenging, the discussion and conclusions are often the hardest parts of the paper to write. That’s because you’re not just listing up studies, methods, and outcomes. You’re actually expressing your thoughts and interpretations in words.

• How formal should it be?
• What words should you use, or not use?
• How do you meet strict word limits, or make it longer and more informative?

Always give it your best, but sometimes a helping hand can, well, help. Getting a professional edit can help clarify your work’s importance while improving the English used to explain it. When readers know the value of your work, they’ll cite it. We’ll assign your study to an expert editor knowledgeable in your area of research. Their work will clarify your discussion, helping it to tell your story. Find out more about AJE Editing.

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How to Write a Discussion Section for a Research Paper

We’ve talked about several useful writing tips that authors should consider while drafting or editing their research papers. In particular, we’ve focused on  figures and legends , as well as the Introduction ,  Methods , and  Results . Now that we’ve addressed the more technical portions of your journal manuscript, let’s turn to the analytical segments of your research article. In this article, we’ll provide tips on how to write a strong Discussion section that best portrays the significance of your research contributions.

What is the Discussion section of a research paper?

In a nutshell,  your Discussion fulfills the promise you made to readers in your Introduction . At the beginning of your paper, you tell us why we should care about your research. You then guide us through a series of intricate images and graphs that capture all the relevant data you collected during your research. We may be dazzled and impressed at first, but none of that matters if you deliver an anti-climactic conclusion in the Discussion section!

Are you feeling pressured? Don’t worry. To be honest, you will edit the Discussion section of your manuscript numerous times. After all, in as little as one to two paragraphs ( Nature ‘s suggestion  based on their 3,000-word main body text limit), you have to explain how your research moves us from point A (issues you raise in the Introduction) to point B (our new understanding of these matters). You must also recommend how we might get to point C (i.e., identify what you think is the next direction for research in this field). That’s a lot to say in two paragraphs!

So, how do you do that? Let’s take a closer look.

What should I include in the Discussion section?

As we stated above, the goal of your Discussion section is to  answer the questions you raise in your Introduction by using the results you collected during your research . The content you include in the Discussions segment should include the following information:

• Remind us why we should be interested in this research project.
• Describe the nature of the knowledge gap you were trying to fill using the results of your study.
• Don’t repeat your Introduction. Instead, focus on why  this  particular study was needed to fill the gap you noticed and why that gap needed filling in the first place.
• Mainly, you want to remind us of how your research will increase our knowledge base and inspire others to conduct further research.
• Clearly tell us what that piece of missing knowledge was.
• Answer each of the questions you asked in your Introduction and explain how your results support those conclusions.
• Make sure to factor in all results relevant to the questions (even if those results were not statistically significant).
• Focus on the significance of the most noteworthy results.
• If conflicting inferences can be drawn from your results, evaluate the merits of all of them.
• Don’t rehash what you said earlier in the Results section. Rather, discuss your findings in the context of answering your hypothesis. Instead of making statements like “[The first result] was this…,” say, “[The first result] suggests [conclusion].”
• Do your conclusions line up with existing literature?
• Discuss whether your findings agree with current knowledge and expectations.
• Keep in mind good persuasive argument skills, such as explaining the strengths of your arguments and highlighting the weaknesses of contrary opinions.
• If you discovered something unexpected, offer reasons. If your conclusions aren’t aligned with current literature, explain.
• Address any limitations of your study and how relevant they are to interpreting your results and validating your findings.
• Make sure to acknowledge any weaknesses in your conclusions and suggest room for further research concerning that aspect of your analysis.
• Make sure your suggestions aren’t ones that should have been conducted during your research! Doing so might raise questions about your initial research design and protocols.
• Similarly, maintain a critical but unapologetic tone. You want to instill confidence in your readers that you have thoroughly examined your results and have objectively assessed them in a way that would benefit the scientific community’s desire to expand our knowledge base.
• Recommend next steps.
• Your suggestions should inspire other researchers to conduct follow-up studies to build upon the knowledge you have shared with them.
• Keep the list short (no more than two).

How to Write the Discussion Section

The above list of what to include in the Discussion section gives an overall idea of what you need to focus on throughout the section. Below are some tips and general suggestions about the technical aspects of writing and organization that you might find useful as you draft or revise the contents we’ve outlined above.

Technical writing elements

• Embrace active voice because it eliminates the awkward phrasing and wordiness that accompanies passive voice.
• Use the present tense, which should also be employed in the Introduction.
• Sprinkle with first person pronouns if needed, but generally, avoid it. We want to focus on your findings.
• Maintain an objective and analytical tone.

Discussion section organization

• Keep the same flow across the Results, Methods, and Discussion sections.
• We develop a rhythm as we read and parallel structures facilitate our comprehension. When you organize information the same way in each of these related parts of your journal manuscript, we can quickly see how a certain result was interpreted and quickly verify the particular methods used to produce that result.
• Notice how using parallel structure will eliminate extra narration in the Discussion part since we can anticipate the flow of your ideas based on what we read in the Results segment. Reducing wordiness is important when you only have a few paragraphs to devote to the Discussion section!
• Within each subpart of a Discussion, the information should flow as follows: (A) conclusion first, (B) relevant results and how they relate to that conclusion and (C) relevant literature.
• End with a concise summary explaining the big-picture impact of your study on our understanding of the subject matter. At the beginning of your Discussion section, you stated why  this  particular study was needed to fill the gap you noticed and why that gap needed filling in the first place. Now, it is time to end with “how your research filled that gap.”

Discussion Part 1: Summarizing Key Findings

Begin the Discussion section by restating your  statement of the problem  and briefly summarizing the major results. Do not simply repeat your findings. Rather, try to create a concise statement of the main results that directly answer the central research question that you stated in the Introduction section . This content should not be longer than one paragraph in length.

Many researchers struggle with understanding the precise differences between a Discussion section and a Results section . The most important thing to remember here is that your Discussion section should subjectively evaluate the findings presented in the Results section, and in relatively the same order. Keep these sections distinct by making sure that you do not repeat the findings without providing an interpretation.

Phrase examples: Summarizing the results

• The findings indicate that …
• These results suggest a correlation between A and B …
• The data present here suggest that …
• An interpretation of the findings reveals a connection between…

Discussion Part 2: Interpreting the Findings

What do the results mean? It may seem obvious to you, but simply looking at the figures in the Results section will not necessarily convey to readers the importance of the findings in answering your research questions.

The exact structure of interpretations depends on the type of research being conducted. Here are some common approaches to interpreting data:

• Identifying correlations and relationships in the findings
• Explaining whether the results confirm or undermine your research hypothesis
• Giving the findings context within the history of similar research studies
• Discussing unexpected results and analyzing their significance to your study or general research
• Offering alternative explanations and arguing for your position

Organize the Discussion section around key arguments, themes, hypotheses, or research questions or problems. Again, make sure to follow the same order as you did in the Results section.

Discussion Part 3: Discussing the Implications

In addition to providing your own interpretations, show how your results fit into the wider scholarly literature you surveyed in the  literature review section. This section is called the implications of the study . Show where and how these results fit into existing knowledge, what additional insights they contribute, and any possible consequences that might arise from this knowledge, both in the specific research topic and in the wider scientific domain.

Questions to ask yourself when dealing with potential implications:

• Do your findings fall in line with existing theories, or do they challenge these theories or findings? What new information do they contribute to the literature, if any? How exactly do these findings impact or conflict with existing theories or models?
• What are the practical implications on actual subjects or demographics?
• What are the methodological implications for similar studies conducted either in the past or future?

Your purpose in giving the implications is to spell out exactly what your study has contributed and why researchers and other readers should be interested.

Phrase examples: Discussing the implications of the research

• These results confirm the existing evidence in X studies…
• The results are not in line with the foregoing theory that…
• This experiment provides new insights into the connection between…
• These findings present a more nuanced understanding of…
• While previous studies have focused on X, these results demonstrate that Y.

Step 4: Acknowledging the limitations

All research has study limitations of one sort or another. Acknowledging limitations in methodology or approach helps strengthen your credibility as a researcher. Study limitations are not simply a list of mistakes made in the study. Rather, limitations help provide a more detailed picture of what can or cannot be concluded from your findings. In essence, they help temper and qualify the study implications you listed previously.

Study limitations can relate to research design, specific methodological or material choices, or unexpected issues that emerged while you conducted the research. Mention only those limitations directly relate to your research questions, and explain what impact these limitations had on how your study was conducted and the validity of any interpretations.

Possible types of study limitations:

• Insufficient sample size for statistical measurements
• Lack of previous research studies on the topic
• Methods/instruments/techniques used to collect the data
• Limited access to data
• Time constraints in properly preparing and executing the study

After discussing the study limitations, you can also stress that your results are still valid. Give some specific reasons why the limitations do not necessarily handicap your study or narrow its scope.

Phrase examples: Limitations sentence beginners

• “There may be some possible limitations in this study.”
• “The findings of this study have to be seen in light of some limitations.”
•  “The first limitation is the…The second limitation concerns the…”
•  “The empirical results reported herein should be considered in the light of some limitations.”
• “This research, however, is subject to several limitations.”
• “The primary limitation to the generalization of these results is…”
• “Nonetheless, these results must be interpreted with caution and a number of limitations should be borne in mind.”

Discussion Part 5: Giving Recommendations for Further Research

Based on your interpretation and discussion of the findings, your recommendations can include practical changes to the study or specific further research to be conducted to clarify the research questions. Recommendations are often listed in a separate Conclusion section , but often this is just the final paragraph of the Discussion section.

Suggestions for further research often stem directly from the limitations outlined. Rather than simply stating that “further research should be conducted,” provide concrete specifics for how future can help answer questions that your research could not.

Phrase examples: Recommendation sentence beginners

• Further research is needed to establish …
• There is abundant space for further progress in analyzing…
• A further study with more focus on X should be done to investigate…
• Further studies of X that account for these variables must be undertaken.

Consider Receiving Professional Language Editing

As you edit or draft your research manuscript, we hope that you implement these guidelines to produce a more effective Discussion section. And after completing your draft, don’t forget to submit your work to a professional proofreading and English editing service like Wordvice, including our manuscript editing service for  paper editing , cover letter editing , SOP editing , and personal statement proofreading services. Language editors not only proofread and correct errors in grammar, punctuation, mechanics, and formatting but also improve terms and revise phrases so they read more naturally. Wordvice is an industry leader in providing high-quality revision for all types of academic documents.

For additional information about how to write a strong research paper, make sure to check out our full  research writing series !

Wordvice Writing Resources

• How to Write a Research Paper Introduction 
• Which Verb Tenses to Use in a Research Paper
• How to Write an Abstract for a Research Paper
• How to Write a Research Paper Title
• Useful Phrases for Academic Writing
• Common Transition Terms in Academic Papers
• Active and Passive Voice in Research Papers
• 100+ Verbs That Will Make Your Research Writing Amazing
• Tips for Paraphrasing in Research Papers

Additional Academic Resources

•   Guide for Authors.  (Elsevier)
•  How to Write the Results Section of a Research Paper.  (Bates College)
•   Structure of a Research Paper.  (University of Minnesota Biomedical Library)
•   How to Choose a Target Journal  (Springer)
•   How to Write Figures and Tables  (UNC Writing Center)

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Writing a scientific paper.

• Writing a lab report
• INTRODUCTION

Writing a "good" discussion section

"discussion and conclusions checklist" from: how to write a good scientific paper. chris a. mack. spie. 2018., peer review.

• LITERATURE CITED
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This is is usually the hardest section to write. You are trying to bring out the true meaning of your data without being too long. Do not use words to conceal your facts or reasoning. Also do not repeat your results, this is a discussion.

• Present principles, relationships and generalizations shown by the results
• Point out exceptions or lack of correlations. Define why you think this is so.
• Show how your results agree or disagree with previously published works
• Discuss the theoretical implications of your work as well as practical applications
• State your conclusions clearly. Summarize your evidence for each conclusion.
• Discuss the significance of the results
•  Evidence does not explain itself; the results must be presented and then explained.
• Typical stages in the discussion: summarizing the results, discussing whether results are expected or unexpected, comparing these results to previous work, interpreting and explaining the results (often by comparison to a theory or model), and hypothesizing about their generality.
• Discuss any problems or shortcomings encountered during the course of the work.
• Discuss possible alternate explanations for the results.
• Avoid: presenting results that are never discussed; presenting discussion that does not relate to any of the results; presenting results and discussion in chronological order rather than logical order; ignoring results that do not support the conclusions; drawing conclusions from results without logical arguments to back them up. 

CONCLUSIONS

• Provide a very brief summary of the Results and Discussion.
• Emphasize the implications of the findings, explaining how the work is significant and providing the key message(s) the author wishes to convey.
• Provide the most general claims that can be supported by the evidence.
• Provide a future perspective on the work.
• Avoid: repeating the abstract; repeating background information from the Introduction; introducing new evidence or new arguments not found in the Results and Discussion; repeating the arguments made in the Results and Discussion; failing to address all of the research questions set out in the Introduction. 

WHAT HAPPENS AFTER I COMPLETE MY PAPER?

 The peer review process is the quality control step in the publication of ideas.  Papers that are submitted to a journal for publication are sent out to several scientists (peers) who look carefully at the paper to see if it is "good science".  These reviewers then recommend to the editor of a journal whether or not a paper should be published. Most journals have publication guidelines. Ask for them and follow them exactly.    Peer reviewers examine the soundness of the materials and methods section.  Are the materials and methods used written clearly enough for another scientist to reproduce the experiment?  Other areas they look at are: originality of research, significance of research question studied, soundness of the discussion and interpretation, correct spelling and use of technical terms, and length of the article.

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General Research Paper Guidelines: Discussion

Discussion section.

The overall purpose of a research paper’s discussion section is to evaluate and interpret results, while explaining both the implications and limitations of your findings. Per APA (2020) guidelines, this section requires you to “examine, interpret, and qualify the results and draw inferences and conclusions from them” (p. 89). Discussion sections also require you to detail any new insights, think through areas for future research, highlight the work that still needs to be done to further your topic, and provide a clear conclusion to your research paper. In a good discussion section, you should do the following:

• Clearly connect the discussion of your results to your introduction, including your central argument, thesis, or problem statement.
• Provide readers with a critical thinking through of your results, answering the “so what?” question about each of your findings. In other words, why is this finding important?
• Detail how your research findings might address critical gaps or problems in your field
• Compare your results to similar studies’ findings
• Provide the possibility of alternative interpretations, as your goal as a researcher is to “discover” and “examine” and not to “prove” or “disprove.” Instead of trying to fit your results into your hypothesis, critically engage with alternative interpretations to your results.

For more specific details on your Discussion section, be sure to review Sections 3.8 (pp. 89-90) and 3.16 (pp. 103-104) of your 7 th edition APA manual

*Box content adapted from:

University of Southern California (n.d.). Organizing your social sciences research paper: 8 the discussion . https://libguides.usc.edu/writingguide/discussion

Limitations

Limitations of generalizability or utility of findings, often over which the researcher has no control, should be detailed in your Discussion section. Including limitations for your reader allows you to demonstrate you have thought critically about your given topic, understood relevant literature addressing your topic, and chosen the methodology most appropriate for your research. It also allows you an opportunity to suggest avenues for future research on your topic. An effective limitations section will include the following:

• Detail (a) sources of potential bias, (b) possible imprecision of measures, (c) other limitations or weaknesses of the study, including any methodological or researcher limitations.
• Sample size: In quantitative research, if a sample size is too small, it is more difficult to generalize results.
• Lack of available/reliable data : In some cases, data might not be available or reliable, which will ultimately affect the overall scope of your research. Use this as an opportunity to explain areas for future study.
• Lack of prior research on your study topic: In some cases, you might find that there is very little or no similar research on your study topic, which hinders the credibility and scope of your own research. If this is the case, use this limitation as an opportunity to call for future research. However, make sure you have done a thorough search of the available literature before making this claim.
• Flaws in measurement of data: Hindsight is 20/20, and you might realize after you have completed your research that the data tool you used actually limited the scope or results of your study in some way. Again, acknowledge the weakness and use it as an opportunity to highlight areas for future study.
• Limits of self-reported data: In your research, you are assuming that any participants will be honest and forthcoming with responses or information they provide to you. Simply acknowledging this assumption as a possible limitation is important in your research.
• Access: Most research requires that you have access to people, documents, organizations, etc.. However, for various reasons, access is sometimes limited or denied altogether. If this is the case, you will want to acknowledge access as a limitation to your research.
• Time: Choosing a research focus that is narrow enough in scope to finish in a given time period is important. If such limitations of time prevent you from certain forms of research, access, or study designs, acknowledging this time restraint is important. Acknowledging such limitations is important, as they can point other researchers to areas that require future study.
• Potential Bias: All researchers have some biases, so when reading and revising your draft, pay special attention to the possibilities for bias in your own work. Such bias could be in the form you organized people, places, participants, or events. They might also exist in the method you selected or the interpretation of your results. Acknowledging such bias is an important part of the research process.
• Language Fluency: On occasion, researchers or research participants might have language fluency issues, which could potentially hinder results or how effectively you interpret results. If this is an issue in your research, make sure to acknowledge it in your limitations section.

University of Southern California (n.d.). Organizing your social sciences research paper: Limitations of the study . https://libguides.usc.edu/writingguide/limitations

In many research papers, the conclusion, like the limitations section, is folded into the larger discussion section. If you are unsure whether to include the conclusion as part of your discussion or as a separate section, be sure to defer to the assignment instructions or ask your instructor.

The conclusion is important, as it is specifically designed to highlight your research’s larger importance outside of the specific results of your study. Your conclusion section allows you to reiterate the main findings of your study, highlight their importance, and point out areas for future research. Based on the scope of your paper, your conclusion could be anywhere from one to three paragraphs long. An effective conclusion section should include the following:

• Describe the possibilities for continued research on your topic, including what might be improved, adapted, or added to ensure useful and informed future research.
• Provide a detailed account of the importance of your findings
• Reiterate why your problem is important, detail how your interpretation of results impacts the subfield of study, and what larger issues both within and outside of your field might be affected from such results

University of Southern California (n.d.). Organizing your social sciences research paper: 9. the conclusion . https://libguides.usc.edu/writingguide/conclusion

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Discussion Section Examples and Writing Tips

Abstract | Introduction | Literature Review | Research question | Materials & Methods | Results | Discussion | Conclusion

In this blog, we look at how to write the discussion section of a research paper. We will go through plenty of discussion examples and understand how to construct a great discussion section for your research paper.

1. What is the purpose of the discussion section?

The discussion section is one of the most important sections of your research paper. This is where you interpret your results, highlight your contributions, and explain the value of your work to your readers.  This is one of the challenging parts to write because the author must clearly explain the significance of their results and tie everything back to the research questions.

2. How should I structure my discussion section?

Generally, the discussion section of a research paper typically contains the following parts.

Research summary It is a good idea to start this section with an overall summary of your work and highlight the main findings of your research.

Interpretation of findings You must interpret your findings clearly to your readers one by one.

Comparison with literature You must talk about how your results fit into existing research in the literature.

Implications of your work You should talk about the implications and possible benefits of your research.

Limitations You should talk about the possible limitations and shortcomings of your research

Future work And finally, you can talk about the possible future directions of your work.

3. Discussion Examples

Let’s look at some examples of the discussion section.  We will be looking at discussion examples from different fields and of different formats. We have split this section into multiple components so that it is easy for you to digest and understand.

3.1. An example of research summary in discussion

It is a good idea to start your discussion section with the summary of your work. The best way to do this will be to restate your research question, and then reminding your readers about your methods, and finally providing an overall summary of your results.

Our aims were to compare the effectiveness and user-friendliness of different storm detection software for storm tracking. On the basis of these aims, we ran multiple experiments with the same conditions using different storm detection software. Our results showed that in both speed and accuracy of data, ‘software A’ performed better than ‘software B’. _  Aims summary  _  Methodology summary  _  Results summary

This discussion example is from an engineering research paper. The authors are restating their aims first, which is to compare different types of storm-tracking software. Then, they are providing a brief summary of the methods. Here, they are testing different storm-tracking software under different conditions to see which performs the best. Then, they are finally providing their main finding which is that they found ‘software A’ better than ‘software B’.  This is a very good example of how to start the discussion section by presenting a summary of your work.

3.2. An example of result interpretation in discussion

The next step is to interpret your results. You have to explain your results clearly to your readers. Here is a discussion example that shows how to interpret your results.

The results of this study indicate significant differences between classical music and pop music in terms of their effects on memory recall and cognition. This implies that as the complexity of the music increases, so does its ability to facilitate cognitive processing. This finding aligns with the well-known “Mozart effect,” which suggests that listening to classical music can enhance cognitive function. _  Result  _  Interpretation  _   Additional evidence

The authors are saying that their results show that there is a significant difference between pop music and classical music in terms of memory recall and cognition. Now they are providing their interpretation of the findings. They think it is because there is a link between the complexity of music and cognitive processing. They are also making a reference to a well-known theory called the ‘Mozart effect’ to back up their findings. It is a nicely written passage and the author’s interpretation sounds very convincing and credible.

3.3. An example of literature comparison in discussion

The next step is to compare your results to the literature. You have to explain clearly how your findings compare with similar findings made by other researchers. Here is a discussion example where authors are providing details of papers in the literature that both support and oppose their findings.

Our analysis predicts that climate change will have a significant impact on wheat yield. This finding undermines one of the central pieces of evidence in some previous simulation studies [1-3] that suggest a negative effect of climate change on wheat yield, but the result is entirely consistent with the predictions of other research [4-5] that suggests the overall change in climate could result in increases in wheat yield. _  Result  _  Comparison with literature

The authors are saying that their results show that climate change will have a significant effect on wheat production. Then, they are saying that there are some papers in the literature that are in agreement with their findings. However, there are also many papers in the literature that disagree with their findings. This is very important. Your discussion should be two-sided, not one-sided. You should not ignore the literature that doesn’t corroborate your findings.

3.4. An example of research implications in discussion

The next step is to explain to your readers how your findings will benefit society and the research community. You have to clearly explain the value of your work to your readers. Here is a discussion example where authors explain the implications of their research.

The results contribute insights with regard to the management of wildfire events using artificial intelligence. One could easily argue that the obvious practical implication of this study is that it proposes utilizing cloud-based machine vision to detect wildfires in real-time, even before the first responders receive emergency calls. _  Your finding  _  Implications of your finding

In this paper, the authors are saying that their findings indicate that Artificial intelligence can be used to effectively manage wildfire events. Then, they are talking about the practical implications of their study. They are saying that their work has proven that machine learning can be used to detect wildfires in real-time. This is a great practical application and can save thousands of lives. As you can see, after reading this passage, you can immediately understand the value and significance of the work.

3.5. An example of limitations in discussion

It is very important that you discuss the limitations of your study. Limitations are flaws and shortcomings of your study. You have to tell your readers how your limitations might influence the outcomes and conclusions of your research. Most studies will have some form of limitation. So be honest and don’t hide your limitations. In reality, your readers and reviewers will be impressed with your paper if you are upfront about your limitations. 

Study design and small sample size are important limitations. This could have led to an overestimation of the effect. Future research should reconfirm these findings by conducting larger-scale studies. _  Limitation  _  How it might affect the results?  _   How to fix the limitation?

Here is a discussion example where the author talks about study limitations. The authors are saying that the main limitations of the study are the small sample size and weak study design. Then they explain how this might have affected their results. They are saying that it is possible that they are overestimating the actual effect they are measuring. Then finally they are telling the readers that more studies with larger sample sizes should be conducted to reconfirm the findings.

As you can see, the authors are clearly explaining three things here:

3.6. An example of future work in discussion

It is important to remember not to end your paper with limitations. Finish your paper on a positive note by telling your readers about the benefits of your research and possible future directions. Here is a discussion example where the author talks about future work.

Our study highlights useful insights about the potential of biomass as a renewable energy source. Future research can extend this research in several ways, including research on how to tackle challenges that hinder the sustainability of renewable energy sources towards climate change mitigation, such as market failures, lack of information and access to raw materials.   _  Benefits of your work  _   Future work

The authors are starting the final paragraph of the discussion section by highlighting the benefit of their work which is the use of biomass as a renewable source of energy. Then they talk about future research. They are saying that future research can focus on how to improve the sustainability of biomass production. This is a very good example of how to finish the discussion section of your paper on a positive note.

4. Frequently Asked Questions

Sometimes you will have negative or unexpected results in your paper. You have to talk about it in your discussion section. A lot of students find it difficult to write this part. The best way to handle this situation is not to look at results as either positive or negative. A result is a result, and you will always have something important and interesting to say about your findings. Just spend some time investigating what might have caused this result and tell your readers about it.

You must talk about the limitations of your work in the discussion section of the paper. One of the important qualities that the scientific community expects from a researcher is honesty and admitting when they have made a mistake. The important trick you have to learn while presenting your limitations is to present them in a constructive way rather than being too negative about them.  You must try to use positive language even when you are talking about major limitations of your work. 

If you have something exciting to say about your results or found something new that nobody else has found before, then, don’t be modest and use flat language when presenting this in the discussion. Use words like ‘break through’, ‘indisputable evidence’, ‘exciting proposition’ to increase the impact of your findings.

Important thing to remember is not to overstate your findings. If you found something really interesting but are not 100% sure, you must not mislead your readers. The best way to do this will be to use words like ‘it appears’ and ‘it seems’. This will tell the readers that there is a slight possibility that you might be wrong.

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Organizing Academic Research Papers: 8. The Discussion

• Purpose of Guide
• Design Flaws to Avoid
• Glossary of Research Terms
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Choosing a Title
• Making an Outline
• Paragraph Development
• Executive Summary
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tertiary Sources
• What Is Scholarly vs. Popular?
• Qualitative Methods
• Quantitative Methods
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Annotated Bibliography
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• How to Manage Group Projects
• Multiple Book Review Essay
• Reviewing Collected Essays
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Research Proposal
• Acknowledgements

The purpose of the discussion is to interpret and describe the significance of your findings in light of what was already known about the research problem being investigated, and to explain any new understanding or fresh insights about the problem after you've taken the findings into consideration. The discussion will always connect to the introduction by way of the research questions or hypotheses you posed and the literature you reviewed, but it does not simply repeat or rearrange the introduction; the discussion should always explain how your study has moved the reader's understanding of the research problem forward from where you left them at the end of the introduction.

Importance of a Good Discussion

This section is often considered the most important part of a research paper because it most effectively demonstrates your ability as a researcher to think critically about an issue, to develop creative solutions to problems based on the findings, and to formulate a deeper, more profound understanding of the research problem you are studying.

The discussion section is where you explore the underlying meaning of your research , its possible implications in other areas of study, and the possible improvements that can be made in order to further develop the concerns of your research.

This is the section where you need to present the importance of your study and how it may be able to contribute to and/or fill existing gaps in the field. If appropriate, the discussion section is also where you state how the findings from your study revealed new gaps in the literature that had not been previously exposed or adequately described.

This part of the paper is not strictly governed by objective reporting of information but, rather, it is where you can engage in creative thinking about issues through evidence-based interpretation of findings. This is where you infuse your results with meaning.

Kretchmer, Paul. Fourteen Steps to Writing to Writing an Effective Discussion Section . San Francisco Edit, 2003-2008.

Structure and Writing Style

I.  General Rules

These are the general rules you should adopt when composing your discussion of the results :

• Do not be verbose or repetitive.
• Be concise and make your points clearly.
• Avoid using jargon.
• Follow a logical stream of thought.
• Use the present verb tense, especially for established facts; however, refer to specific works and references in the past tense.
• If needed, use subheadings to help organize your presentation or to group your interpretations into themes.

II.  The Content

The content of the discussion section of your paper most often includes :

• Explanation of results : comment on whether or not the results were expected and present explanations for the results; go into greater depth when explaining findings that were unexpected or especially profound. If appropriate, note any unusual or unanticipated patterns or trends that emerged from your results and explain their meaning.
• References to previous research : compare your results with the findings from other studies, or use the studies to support a claim. This can include re-visiting key sources already cited in your literature review section, or, save them to cite later in the discussion section if they are more important to compare with your results than being part of the general research you cited to provide context and background information.
• Deduction : a claim for how the results can be applied more generally. For example, describing lessons learned, proposing recommendations that can help improve a situation, or recommending best practices.
• Hypothesis : a more general claim or possible conclusion arising from the results [which may be proved or disproved in subsequent research].

III. Organization and Structure

Keep the following sequential points in mind as you organize and write the discussion section of your paper:

• Think of your discussion as an inverted pyramid. Organize the discussion from the general to the specific, linking your findings to the literature, then to theory, then to practice [if appropriate].
• Use the same key terms, mode of narration, and verb tense [present] that you used when when describing the research problem in the introduction.
• Begin by briefly re-stating the research problem you were investigating and answer all of the research questions underpinning the problem that you posed in the introduction.
• Describe the patterns, principles, and relationships shown by each major findings and place them in proper perspective. The sequencing of providing this information is important; first state the answer, then the relevant results, then cite the work of others. If appropriate, refer the reader to a figure or table to help enhance the interpretation of the data. The order of interpreting each major finding should be in the same order as they were described in your results section.
• A good discussion section includes analysis of any unexpected findings. This paragraph should begin with a description of the unexpected finding, followed by a brief interpretation as to why you believe it appeared and, if necessary, its possible significance in relation to the overall study. If more than one unexpected finding emerged during the study, describe each them in the order they appeared as you gathered the data.
• Before concluding the discussion, identify potential limitations and weaknesses. Comment on their relative importance in relation to your overall interpretation of the results and, if necessary, note how they may affect the validity of the findings. Avoid using an apologetic tone; however, be honest and self-critical.
• The discussion section should end with a concise summary of the principal implications of the findings regardless of statistical significance. Give a brief explanation about why you believe the findings and conclusions of your study are important and how they support broader knowledge or understanding of the research problem. This can be followed by any recommendations for further research. However, do not offer recommendations which could have been easily addressed within the study. This demonstrates to the reader you have inadequately examined and interpreted the data.

IV.  Overall Objectives

The objectives of your discussion section should include the following: I.  Reiterate the Research Problem/State the Major Findings

Briefly reiterate for your readers the research problem or problems you are investigating and the methods you used to investigate them, then move quickly to describe the major findings of the study. You should write a direct, declarative, and succinct proclamation of the study results.

II.  Explain the Meaning of the Findings and Why They are Important

No one has thought as long and hard about your study as you have. Systematically explain the meaning of the findings and why you believe they are important. After reading the discussion section, you want the reader to think about the results [“why hadn’t I thought of that?”]. You don’t want to force the reader to go through the paper multiple times to figure out what it all means. Begin this part of the section by repeating what you consider to be your most important finding first.

III.  Relate the Findings to Similar Studies

No study is so novel or possesses such a restricted focus that it has absolutely no relation to other previously published research. The discussion section should relate your study findings to those of other studies, particularly if questions raised by previous studies served as the motivation for your study, the findings of other studies support your findings [which strengthens the importance of your study results], and/or they point out how your study differs from other similar studies. IV.  Consider Alternative Explanations of the Findings

It is important to remember that the purpose of research is to discover and not to prove . When writing the discussion section, you should carefully consider all possible explanations for the study results, rather than just those that fit your prior assumptions or biases.

V.  Acknowledge the Study’s Limitations

It is far better for you to identify and acknowledge your study’s limitations than to have them pointed out by your professor! Describe the generalizability of your results to other situations, if applicable to the method chosen, then describe in detail problems you encountered in the method(s) you used to gather information. Note any unanswered questions or issues your study did not address, and.... VI.  Make Suggestions for Further Research

Although your study may offer important insights about the research problem, other questions related to the problem likely remain unanswered. Moreover, some unanswered questions may have become more focused because of your study. You should make suggestions for further research in the discussion section.

NOTE: Besides the literature review section, the preponderance of references to sources in your research paper are usually found in the discussion section . A few historical references may be helpful for perspective but most of the references should be relatively recent and included to aid in the interpretation of your results and/or linked to similar studies. If a study that you cited disagrees with your findings, don't ignore it--clearly explain why the study's findings differ from yours.

V.  Problems to Avoid

• Do not waste entire sentences restating your results . Should you need to remind the reader of the finding to be discussed, use "bridge sentences" that relate the result to the interpretation. An example would be: “The lack of available housing to single women with children in rural areas of Texas suggests that...[then move to the interpretation of this finding].”
• Recommendations for further research can be included in either the discussion or conclusion of your paper but do not repeat your recommendations in the both sections.
• Do not introduce new results in the discussion. Be wary of mistaking the reiteration of a specific finding for an interpretation.
• Use of the first person is acceptable, but too much use of the first person may actually distract the reader from the main points.

Analyzing vs. Summarizing. Department of English Writing Guide. George Mason University; Discussion . The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Hess, Dean R. How to Write an Effective Discussion. Respiratory Care 49 (October 2004); Kretchmer, Paul. Fourteen Steps to Writing to Writing an Effective Discussion Section . San Francisco Edit, 2003-2008; The Lab Report . University College Writing Centre. University of Toronto; Summary: Using it Wisely . The Writing Center. University of North Carolina; Schafer, Mickey S. Writing the Discussion . Writing in Psychology course syllabus. University of Florida; Yellin, Linda L. A Sociology Writer's Guide. Boston, MA: Allyn and Bacon, 2009.

Writing Tip

Don’t Overinterpret the Results!

Interpretation is a subjective exercise. Therefore, be careful that you do not read more into the findings than can be supported by the evidence you've gathered. Remember that the data are the data: nothing more, nothing less.

Another Writing Tip

Don't Write Two Results Sections!

One of the most common mistakes that you can make when discussing the results of your study is to present a superficial interpretation of the findings that more or less re-states the results section of your paper. Obviously, you must refer to your results when discussing them, but focus on the interpretion of those results, not just the data itself.

Azar, Beth. Discussing Your Findings.  American Psychological Association gradPSYCH Magazine (January 2006)

Yet Another Writing Tip

Avoid Unwarranted Speculation!

The discussion section should remain focused on the findings of your study. For example, if you studied the impact of foreign aid on increasing levels of education among the poor in Bangladesh, it's generally not appropriate to speculate about how your findings might apply to populations in other countries without drawing from existing studies to support your claim. If you feel compelled to speculate, be certain that you clearly identify your comments as speculation or as a suggestion for where further research is needed. Sometimes your professor will encourage you to expand the discussion in this way, while others don’t care what your opinion is beyond your efforts to interpret the data.

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Study Skills

Writing a discussion section

In the discussion section, you will draw connections between your findings, existing theory and other research. You will have an opportunity to tell the story arising from your findings. 

This page will help you to: 

understand the purpose of the discussion section 

follow the steps required to plan your discussion section 

structure your discussion  

enhance the depth of your discussion 

use appropriate language to discuss your findings.  

Introduction to the discussion section

When you have reached this stage, you might be thinking “All I have to do now is to sum up what I have done, and then make a few remarks about what I did” (as cited in Swales & Feak, 2012, p.263). However, writing a discussion section is not that simple. Read on to learn more.

  Before you continue, reflect on your earlier writing experiences and the feedback you have received. How would you rate your ability in the following skills? Rate your ability from ‘good’ to ‘needs development’. 

Reflect on your answers. Congratulations if you feel confident about your skills. You may find it helpful to review the materials on this page to confirm your knowledge and possibly learn more. Don't worry if you don't feel confident. Work through these materials to build your skills. 

A discussion critically analyses and interprets the results of a scientific study, placing the results in the context of published literature and explaining how they affect the field . 

In this section, you will relate the specific findings of your research to the wider scientific field. This is the opposite of the introduction section, which starts with the broader context and narrows to focus on your specific research topic.  

The discussion will: 

review the findings  

put the findings into the context of the overall research  

tell readers why the research results are important and where they fit in with the current literature 

acknowledge the limitations of the study 

make recommendations for future research.

Let's review your understanding of the discussion section by identifying what makes a strong discussion.

Planning for a discussion section

Planning for a discussion section starts with analysing your data. For some kinds of research, the analysis cannot be done until your data has been collected. For others, analysing data can happen early as the data already exists in literary texts, archival documents or similar.  

Before starting to write the discussion section, it is important to:  

analyse your data (usually reported in the Results or Findings section) 

select the key issues that are the substance of your research  

relate the findings to the literature and 

plan for the process of going from your specific findings to the broader scientific field.  

Your analysis of the results will inform the Findings or Results section of your thesis or publication. It is the stage where you organise and visualise your data, and identify trends, patterns and causal relationships in the themes.

As the section discusses the key findings without restating the results, it is important to identify the key issues. For example, you should focus on four or five issues that agree or do not agree with your hypothesis or with previously published work. It is also important to include and discuss any unexpected results.

You refer to previous research in your discussion section for explaining your results, confirming how your results support the theories and previous studies, comparing your results with similar studies, or showing how your results contradict similar studies. 

Therefore, papers that you are likely to refer to in your discussion are those that led to: 

your hypothesis  

your experimental design 

your results.

In writing the discussion section, you will start with your research and then broaden your focus to the field or scientific community. This means you will go from narrowest (your specific findings) to broadest (the wider scientific community). You do this by following the six moves: 

As you can see, your discussion may follow six moves (stages) which broadens the scope of your discussion section. Watch this video to learn how to apply these moves.

Structuring your discussion

This section reviews how a discussion section can be organised.

A discussion section usually includes five parts or steps, which are illustrated in the image below. 

In some disciplines, the researcher's argument determines the structure of the presentation and discussion of findings. In other disciplines, the structure follows established conventions. Therefore, it is important for you to investigate the conventions of your own discipline, by looking at theses in your discipline and articles published in your target journals. The discussion section may be: 

in a combined section called Results and Discussion 

in a combined section called Discussion and Conclusion 

in a separate section. 

Your discussion section may be an independent chapter or it might be combined with the Findings chapter. Common chapter headings include:  

Discussion chapter 

Findings and Discussion chapter  

Discussion, Recommendations and Conclusion chapter

Discussion and Conclusion chapter 

It is important to have a good understanding of the expected content of each chapter.  Below is an example of a chapter in which discussion, recommendations and conclusion are combined.

Click on the hotspots to learn more.

This section focuses on useful language for writing your discussion.

Boosters and hedges should be used to demonstrate your confidence in your interpretation of the results. They help you to distinguish between clear and strong results and those that you feel less confident about or that may be open to different interpretations.

 Read both sentences. Which one shows more confidence in the results? 

The Dutch supervisors reported using different types of questions more frequently and deliberately than the Chinese supervisors. This difference may have its roots in the underlying educational philosophies. (Adapted from Hu, Rijst, Veen, & Verloop, 2016)  

The findings clearly demonstrate that psychological capital had considerable influence on the 10 employability skills included in the study, and especially on those related to teamwork, self-knowledge and self-management (Adapted from Harper, Bregta & Rundle, 2021) 

The writers of sentence two are more confident in the interpretation of their results.  

Test your knowledge of hedges and boosters by doing the task below. 

It is important to make it clear in your discussion: 

which research has been done by you 

which research has been done by other people 

how they complement each other.

Image 2: Note that present perfect is also used to refer to other studies when you want to emphasise that an area of research is still current and ongoing. Take a look at the example below which uses present perfect to refer to other studies 

Like other studies (e.g., Larcombe et al., 2021; Naylor, 2020) that have shown a strong connection between course experience and wellbeing, our study shows that a significant portion of international students believe that aspects of their immediate environment could be improved to better support their wellbeing.  

More information on tenses in the Discussion section is presented in Language Tip 4 below.  

Below are some useful discussion phrases that were adapted from Paltridge & Starfield (2020) and the APA Discussion phrases guide (7th edition).

You can download this APA discussion phrase guide here and visit the Academic Phrasebank for further phrases and examples. 

Let's look at these extracts and identify the functions of the paragraphs.  

Past, present and present perfect tenses are commonly used in the discussion section.  

• Past tense is used to summarise the key findings and to refer to the work of previous researchers  
• Present perfect is used to refer to the work of previous researchers (usually an area of research that is current and on-going rather than one single study) 
• Present tense is used to interpret the results or describe the significance of the findings  
• Future  is used to make recommendations for further research or providing future direction 

Below is an example of some paragraphs in a discussion section in which different tenses are used.

Test your knowledge of using the right tenses in the discussion section by doing the task below. 

Use this template to plan your discussion.  

The template is an example of a planning tool that will help you develop an overview of the key content that you are going to include in your section. You can download the draft and save it as a Word document once you have finished. 

You may have more or less than 3 key findings that you would like to discuss in your section.  

If you would like more support, visit the Language and Learning Advisors page. 

Butler, K. (2020, 7 April). Breakdown of an ideal discussion of scientific research paper. Scientific Communications . https://butlerscicomm.com/breakdown-of-ideal-discussion-section-research-paper  

Calvo, J. C. A & García, G. M. (2021). The influence of psychological capital on graduates’ perception of employability: the mediating role of employability skills. Higher Education Research & Development , 40(2), 293-308, DOI: 10.1080/07294360.2020.1738350   

Cenamor, J. (2022) To teach or not to teach? Junior academics and the teaching-research relationship. Higher Education Research & Development , 41(5), 1417-1435. DOI: 10.1080/07294360.2021.1933395  

Harper, R.,  Bretag, T & Rundle, K. (2021) Detecting contract cheating: examining the role of assessment type. Higher Education Research & Development, 40(2), 263-278, DOI: 10.1080/07294360.2020.1724899   

Hu, Y., Rijst, R. M., Veen, K & N Verloop, N. (2016) The purposes and processes of master's thesis supervision: a comparison of Chinese and Dutch supervisors. Higher Education Research & Development , 35(5), 910-924, DOI: 10.1080/07294360.2016.1139550  

Humphrey, P. (2015). English language proficiency in higher education: student conceptualisations and outcomes . [Doctoral dissertation, Griffith University]  

Marangell, S., & Baik, C. (2022). International students’ suggestions for what universities can do to better support their mental wellbeing. Journal of International Students, 12(4), 933-954.  

Merga, M., & Mason, S. (2021) Early career researchers’ perceptions of the benefits and challenges of sharing research with academic and non-academic end-users, Higher Education Research & Development , 40(7), 1482-1496, DOI: 10.1080/07294360.2020.1815662  

Paltridge, B., & Starfield, S. (2019). Thesis and Dissertation Writing in a Second Language: A Handbook for Students and their Supervisors (2nd ed.). Routledge.  

Rendle-Short, J. (2009). The Address Term Mate in Australian English: Is it Still a Masculine Term?. Australian Journal of Linguistics, 29(2), 245-268, DOI: 10.1080/07268600902823110  

Did you know CDU Language and Learning Advisors offer a range of study support options?

https://www.cdu.edu.au/library/language-and-learning-support

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Writing a discussion section: how to integrate substantive and statistical expertise

Michael höfler.

1 Institute of Clinical Psychology and Psychotherapy, Technische Universität Dresden, Dresden, Germany

5 Chair of Clinical Psychology and Behavioural Neuroscience, Institute of Clinical Psychology and Psychotherapy, Technische Universität Dresden, Dresden, Germany

2 Behavioral Epidemiology, Institute of Clinical Psychology and Psychotherapy, Technische Universität Dresden, Dresden, Germany

Sebastian Trautmann

Robert miller.

3 Faculty of Psychology, Technische Universität Dresden, Dresden, Germany

4 Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden

Associated Data

Not applicable.

When discussing results medical research articles often tear substantive and statistical (methodical) contributions apart, just as if both were independent. Consequently, reasoning on bias tends to be vague, unclear and superficial. This can lead to over-generalized, too narrow and misleading conclusions, especially for causal research questions.

To get the best possible conclusion, substantive and statistical expertise have to be integrated on the basis of reasonable assumptions. While statistics should raise questions on the mechanisms that have presumably created the data, substantive knowledge should answer them. Building on the related principle of Bayesian thinking, we make seven specific and four general proposals on writing a discussion section.

Misinterpretation could be reduced if authors explicitly discussed what can be concluded under which assumptions. Informed on the resulting conditional conclusions other researchers may, according to their knowledge and beliefs, follow a particular conclusion or, based on other conditions, arrive at another one. This could foster both an improved debate and a better understanding of the mechanisms behind the data and should therefore enable researchers to better address bias in future studies.

After a research article has presented the substantive background, the methods and the results, the discussion section assesses the validity of results and draws conclusions by interpreting them. The discussion puts the results into a broader context and reflects their implications for theoretical (e.g. etiological) and practical (e.g. interventional) purposes. As such, the discussion contains an article’s last words the reader is left with.

Common recommendations for the discussion section include general proposals for writing [ 1 ] and structuring (e.g. with a paragraph on a study’s strengths and weaknesses) [ 2 ], to avoid common statistical pitfalls (like misinterpreting non-significant findings as true null results) [ 3 ] and to “go beyond the data” when interpreting results [ 4 ]. Note that the latter includes much more than comparing an article’s results with the literature. If results and literature are consistent, this might be due to shared bias only. If they are not consistent, the question arises why inconsistency occurs – maybe because of bias acting differently across studies [ 5 – 7 ]. Recommendations like the CONSORT checklist do well in demanding all quantitative information on design, participation, compliance etc. to be reported in the methods and results section and “addressing sources of potential bias”, “limitations” and “considering other relevant evidence” in the discussion [ 8 , 9 ]. Similarly, the STROBE checklist for epidemiological research demands “a cautious overall interpretation of results” and "discussing the generalizability (external validity)" [ 10 , 11 ]. However, these guidelines do not clarify how to deal with the complex bias issue, and how to get to and report conclusions.

Consequently, suggestions on writing a discussion often remain vague by hardly addressing the role of the assumptions that have (often implicitly) been made when designing a study, analyzing the data and interpreting the results. Such assumptions involve mechanisms that have created the data and are related to sampling, measurement and treatment assignment (in observational studies common causes of factor and outcome) and, as a consequence, the bias this may produce [ 5 , 6 ]. They determine whether a result allows only an associational or a causal conclusion. Causal conclusions, if true, are of much higher relevance for etiology, prevention and intervention. However, they require much stronger assumptions. These have to be fully explicit and, therewith, essential part of the debate since they always involve subjectivity. Subjectivity is unavoidable because the mechanisms behind the data can never be fully estimated from the data themselves [ 12 ].

In this article, we argue that the conjunction of substantive and statistical (methodical) knowledge in the verbal integration of results and beliefs on mechanisms can be greatly improved in (medical) research papers. We illustrate this through the personal roles that a statistician (i.e. methods expert) and a substantive researcher should take. Doing so, we neither claim that usually just two people write a discussion, nor that one person lacks the knowledge of the other, nor that there were truly no researchers that have both kinds of expertise. As a metaphor, the division of these two roles into two persons describes the necessary integration of knowledge via the mode of a dialogue. Verbally, it addresses the finding of increased specialization of different study contributors in biomedical research. This has teared apart the two processes of statistical compilation of results and their verbal integration [ 13 ]. When this happens a statistician alone is limited to a study’s conditions (sampled population, experimental settings etc.), because he or she is unaware of the conditions’ generalizability. On the other hand, a A substantive expert alone is prone to over-generalize because he or she is not aware of the (mathematical) prerequisites for an interpretation.

The article addresses both (medical) researchers educated in basic statistics and research methods and statisticians who cooperate with them. Throughout the paper we exemplify our arguments with the finding of an association in a cross-tabulation between a binary X (factor) and a binary Y (outcome): those who are exposed to or treated with X have a statistically significantly elevated risk for Y as compared to the non-exposed or not (or otherwise) treated (for instance via the chi-squared independence test or logistic regression). Findings like this are frequent and raise the question which more profound conclusion is valid under what assumptions. Until some decades ago, statistics has largely avoided the related topic of causality and instead limited itself on describing observed distributions (here a two-by-two table between D = depression and LC = lung cancer) with well-fitting models.

We illustrate our arguments with the concrete example of the association found between the factor depression (D) and the outcome lung cancer (LC) [ 14 ]. Yet very different mechanisms could have produced such an association [ 7 ], and assumptions on these lead to the following fundamentally different conclusions (Fig. ​ (Fig.1 1 ):

• D causes LC (e.g. because smoking might constitute “self-medication” of depression symptoms)
• LC causes D (e.g. because LC patients are demoralized by their diagnosis)
• D and LC cause each other (e.g. because the arguments in both a. and b. apply)
• D and LC are the causal consequence of the same factor(s) (e.g. poor health behaviors - HB)
• D and LC only share measurement error (e.g. because a fraction of individuals that has either depression or lung cancer denies both in self-report measures).

Different conclusions about an association between D and LC. a D causes LC, b LC causes B, c D and LC cause each other, d D and LC are associated because of a shared factor (HB), e D and LC are associated because they have correlated errors

Note that we use the example purely for illustrative purposes. We do not make substantive claims on what of a. through e. is true but show how one should reflect on mechanisms in order to find the right answer. Besides, we do not consider research on the D-LC relation apart from the finding of association [ 14 ].

Assessing which of a. through e. truly applies requires substantive assumptions on mechanisms: the temporal order of D and LC (a causal effect requires that the cause occurs before the effect), shared factors, selection processes and measurement error. Questions on related mechanisms have to be brought up by statistical consideration, while substantive reasoning has to address them. Together this yields provisional assumptions for inferring that are subject to readers’ substantive consideration and refinement. In general, the integration of prior beliefs (anything beyond the data a conclusion depends on) and the results from the data themselves is formalized by Bayesian statistics [ 15 , 16 ]. This is beyond the scope of this article, still we argue that Bayesian thinking should govern the process of drawing conclusions.

Building on this idea, we provide seven specific and four general recommendations for the cooperative process of writing a discussion. The recommendations are intended to be suggestions rather than rules. They should be subject to further refinement and adjustment to specific requirements in different fields of medical and other research. Note that the order of the points is not meant to structure a discussion’s writing (besides 1.).

Recommendations for writing a discussion section

Specific recommendations.

Consider the example on the association between D and LC. Rather than starting with an in-depth (causal) interpretation a finding should firstly be taken as what it allows inferring without doubt: Under the usual assumptions that a statistical model makes (e.g. random sampling, independence or certain correlation structure between observations [ 17 ]), the association indicates that D (strictly speaking: measuring D) predicts an elevated LC risk (strictly speaking: measuring LC) in the population that one has managed to sample (source population). Assume that the sample has been randomly drawn from primary care settings. In this case the association is useful to recommend medical doctors to better look at an individual’s LC risk in case of D. If the association has been adjusted for age and gender (conveniently through a regression model), the conclusion modifies to: If the doctor knows a patient’s age and gender (what should always be the case) D has additional value in predicting an elevated LC risk.

In the above example, a substantive researcher might want to conclude that D and LC are associated in a general population instead of just inferring to patients in primary care settings (a.). Another researcher might even take the finding as evidence for D being a causal factor in the etiology of LC, meaning that prevention of D could reduce the incidence rate of LC (in whatever target population) (b.). In both cases, the substantive researcher should insist on assessing the desired interpretation that goes beyond the data [ 4 ], but the statistician immediately needs to bring up the next point.

The explanation of all the assumptions that lead from a data result to a conclusion enables a reader to assess whether he or she agrees with the authors’ inference or not. These conditions, however, often remain incomplete or unclear, in which case the reader can hardly assess whether he or she follows a path of argumentation and, thus, shares the conclusion this path leads to.

Consider conclusion a. and suppose that, instead of representative sampling in a general population (e.g. all U.S. citizens aged 18 or above), the investigators were only able to sample in primary care settings. Extrapolating the results to another population than the source population requires what is called “external validity”, “transportability” or the absence of “selection bias” [ 18 , 19 ]. No such bias occurs if the parameter of interest is equal in the source and the target population. Note that this is a weaker condition than the common belief that the sample must represent the target population in everything . If the parameter of interest is the difference in risk for LC between cases and non-cases of D, the condition translates into: the risk difference must be equal in target and source population.

For the causal conclusion b., however, sufficient assumptions are very strict. In an RCT, the conclusion is valid under random sampling from the target population, random allocation of X, perfect compliance in X, complete participation and no measurement error in outcome (for details see [ 20 ]). In practice, on the other hand, the derivations from such conditions might sometimes be modest what may produce little bias only. For instance, non-compliance in a specific drug intake (treatment) might occur only in a few individuals to little extent through a random process (e.g. sickness of a nurse being responsible for drug dispense) and yield just small (downward) bias [ 5 ]. The conclusion of downward bias might also be justified if non-compliance does not cause anything that has a larger effect on a Y than the drug itself. Another researcher, however, could believe that non-compliance leads to taking a more effective, alternative treatment. He or she could infer upward bias instead if well-informed on the line of argument.

In practice, researchers frequently use causal language yet without mentioning any assumptions. This does not imply that they truly have a causal effect in mind, often causal and associational wordings are carelessly used in synonymous way. For example, concluding “depression increases the risk of lung cancer” constitutes already causal wording because it implies that a change in the depression status would change the cancer risk. Associational language like “lung cancer risk is elevated if depression occurs”, however, would allow for an elevated lung cancer risk in depression cases just because LC and D share some causes (“inducing” or “removing” depression would not change the cancer risk here).

Often, it is unclear where the path of argumentation from assumptions to a conclusion leads when alternative assumptions are made. Consider again bias due to selection. A different effect in target and source population occurs if effect-modifying variables distribute differently in both populations. Accordingly, the statistician should ask which variables influence the effect of interest, and whether these can be assumed to distribute equally in the source population and the target population. The substantive researcher might answer that the causal risk difference between D and LC likely increases with age. Given that this is true, and if elder individuals have been oversampled (e.g. because elderly are over-represented in primary care settings), both together would conclude that sampling has led to over-estimation (despite other factors, Fig. ​ Fig.2 2 ).

If higher age is related to a larger effect (risk difference) of D on LC, a larger effect estimate is expected in an elder sample

However, the statistician might add, if effect modification is weak, or the difference in the age distributions is modest (e.g. mean 54 vs. 52 years), selection is unlikely to have produced large (here: upward) bias. In turn, another substantive researcher, who reads the resulting discussion, might instead assume a decrease of effect with increasing age and thus infer downward bias.

In practice, researchers should be extremely sensitive for bias due to selection if a sample has been drawn conditionally on a common consequence of factor and outcome or a variable associated with such a consequence [19 and references therein]. For instance, hospitalization might be influenced by both D and LC, and thus sampling from hospitals might introduce a false association or change an association’s sign; particularly D and LC may appear to be negatively associated although the association is positive in the general population (Fig. ​ (Fig.3 3 ).

If hospitalization (H) is a common cause of D and LC, sampling conditionally on H can introduce a spurious association between D and LC ("conditioning on a collider")

Usually, only some kinds of bias are discussed, while the consequences of others are ignored [ 5 ]. Besides selection the main sources of bias are often measurement and confounding. If one is only interested in association, confounding is irrelevant. For causal conclusions, however, assumptions on all three kinds of bias are necessary.

Measurement error means that the measurement of a factor and/or outcome deviates from the true value, at least in some individuals. Bias due to measurement is known under many other terms that describe the reasons why such error occurs (e.g. “recall bias” and “reporting bias”). In contrast to conventional wisdom, measurement error does not always bias association and effect estimates downwards [ 5 , 6 ]. It does, for instance, if only the factor (e.g. depression) is measured with error and the errors occur independently from the outcome (e.g. lung cancer), or vice versa (“non-differential misclassification”) [22 and references therein]. However, many lung cancer cases might falsely report depression symptoms (e.g. to express need for care). Such false positives (non-cases of depression classified as cases) may also occur in non-cases of lung cancer but to a lesser extent (a special case of “differential misclassification”). Here, bias might be upward as well. Importantly, false positives cause larger bias than false negatives (non-cases of depression falsely classified as depression cases) as long as the relative frequency of a factor is lower than 50% [ 21 ]. Therefore, they should receive more attention in discussion. If measurement error occurs in depression and lung cancer, the direction of bias also depends on the correlation between both errors [ 21 ].

Note that what is in line with common standards of “good” measurement (e.g. a Kappa value measuring validity or reliability of 0.7) might anyway produce large bias. This applies to estimates of prevalence, association and effect. The reason is that while indices of measurement are one-dimensional, bias depends on two parameters (sensitivity and specificity) [ 21 , 22 ]. Moreover, estimates of such indices are often extrapolated to different kinds of populations (typically from a clinical to general population), what may be inadequate. Note that the different kinds of bias often interact, e.g. bias due to measurement might depend on selection (e.g. measurement error might differ between a clinical and a general population) [ 5 , 6 ].

Assessment of bias due to confounding variables (roughly speaking: common causes of factor and outcome) requires assumptions on the entire system of variables that affect both factor and outcome. For example, D and LC might share several causes such as stressful life events or socioeconomic status. If these influence D and LC with the same effect direction, this leads to overestimation, otherwise (different effect directions) the causal effect is underestimated. In the medical field, many unfavorable conditions may be positively related. If this holds true for all common factors of D and LC, upward bias can be assumed. However, not all confounders have to be taken into account. Within the framework of “causal graphs”, the “backdoor criterion” [ 7 ] provides a graphical rule for sets of confounders to be sufficient when adjusted for. Practically, such a causal graph must include all factors that directly or indirectly affect both D and LC. Then, adjustment for a set of confounders that meets the “backdoor criterion” in the graph completely removes bias due to confounding. In the example of Fig. ​ Fig.4 4 it is sufficient to adjust for Z 1 and Z 2 because this “blocks” all paths that otherwise lead backwards from D to LC. Note that fully eliminating bias due to confounding also requires that the confounders have been collected without measurement error [ 5 , 6 , 23 ]. Therefore, the advice is always to concede at least some “residual” bias and reflect on the direction this might have (could be downward if such error is not stronger related to D and LC than a confounder itself).

Causal graph for the effect of D on LC and confounders Z 1 , Z 2 and Z 3

Whereas the statistician should pinpoint to the mathematical insight of the backdoor criterion, its application requires profound substantive input and literature review. Of course, there are numerous relevant factors in the medical field. Hence, one should practically focus on those with the highest prevalence (a very seldom factor can hardly cause large bias) and large assumed effects on both X and Y.

If knowledge on any of the three kinds of bias is poor or very uncertain, researchers should admit that this adds uncertainty in a conclusion: systematic error on top of random error. In the Bayesian framework, quantitative bias analysis formalizes this through the result of larger variance in an estimate. Technically, this additional variance is introduced via the variances of distributions assigned to “bias parameters”; for instance a misclassification probability (e.g. classifying a true depression case as non-case) or the prevalence of a binary confounder and its effects on X and Y. Of course, bias analysis also changes point estimates (hopefully reducing bias considerably). Note that conventional frequentist analysis, as regarded from the Bayesian perspective, assumes that all bias parameters were zero with a probability of one [ 5 , 6 , 23 ]. The only exceptions (bias addressed in conventional analyses) are adjustment on variables to hopefully reduce bias due to confounding and weighting the individuals (according to variables related to participation) to take into account bias due to selection.

If the substantive investigator understands the processes of selection, measurement and confounding only poorly, such strict analysis numerically reveals that little to nothing is known on the effect of X on Y, no matter how large an observed association and a sample (providing small random error) may be [ 5 , 6 , 23 ]). This insight has to be brought up by the statistician. Although such an analysis is complicated, itself very sensitive to how it is conducted [ 5 , 6 ] and rarely done, the Bayesian thinking behind it forces researchers to better understand the processes behind the data. Otherwise, he or she cannot make any assumptions and, in turn, no conclusion on causality.

Usually articles end with statements that only go little further than the always true but never informative statement “more research is needed”. Moreover, larger samples and better measurements are frequently proposed. If an association has been found, a RCT or other interventional study is usually proposed to investigate causality. In our example, this recommendation disregards that: (1) onset of D might have a different effect on LC risk than an intervention against D (the effect of onset cannot be investigated in any interventional study), (2) the effects of onset and intervention concern different populations (those without vs. those with depression), (3) an intervention effect depends on the mode of intervention [ 24 ], and (4) (applying the backdoor criterion) a well-designed observational study may approximatively yield the same result as a randomized study would [ 25 – 27 ]. If the effect of “removing” depression is actually of interest, one could propose an RCT that investigates the effect of treating depression in a strictly defined way and in a strictly defined population (desirably in all who meet the criteria of depression). Ideally, this population is sampled randomly, and non-participants and dropouts are investigated with respect to assumed effect-modifiers (differences in their distributions between participants and non-participants can then be addressed e.g. by weighting [ 27 ]). In a non-randomized study, one should collect variables supposed to meet the backdoor-criterion with the best instruments possible.

General recommendations

Yet when considering 1) through 7); i.e. carefully reflecting on the mechanisms that have created the data, discussions on statistical results can be very misleading, because the basic statistical methods are mis-interpreted or inadequately worded.

A common pitfall is to consider the lack of evidence for the alternative hypothesis (e.g. association between D and LC) as evidence for the null hypothesis (no association). In fact, such inference requires an a-priori calculated sample-size to ensure that the type-two error probability does not exceed a pre-specified limit (typically 20% or 10%, given the other necessary assumptions, e.g. on the true magnitude of association). Otherwise, the type-two error is unknown and in practice often large. This may put a “false negative result” into the scientific public that turns out to be “unreplicable” – what would be falsely interpreted as part of the “replication crisis”. Such results are neither positive nor negative but uninformative . In this case, the wording “there is no evidence for an association” is adequate because it does not claim that there is no association.

Frequently, it remains unclear which hypotheses have been a-priori specified and which have been brought up only after some data analysis. This, of course, is scientific malpractice because it does not enable the readership to assess the random error emerging from explorative data analysis. Accordingly, the variance of results across statistical methods is often misused to filter out the analysis that yields a significant result (“ p -hacking”, [ 28 ]). Pre-planned tests (via writing a grant) leave at least less room for p-hacking because they specify a-priori which analysis is to be conducted.

On the other hand, post-hoc analyses can be extremely useful for identifying unexpected phenomena and creating new hypotheses. Verbalization in the discussion section should therefore sharply separate between conclusions from hypothesis testing and new hypotheses created from data exploration. The distinction is profound, since a newly proposed hypothesis just makes a new claim. Suggesting new hypotheses cannot be wrong, this can only be inefficient if many hypotheses turn out to be wrong. Therefore, we suggest proposing only a limited number of new hypotheses that appear promising to stimulate further research and scientific progress. They are to be confirmed or falsified with future studies. A present discussion, however, should yet explicate the testable predictions a new hypothesis entails, and how a future study should be designed to keep bias in related analyses as small as possible.

Confidence intervals address the problem of reducing results to the dichotomy of significant and non-significant through providing a range of values that are compatible with the data at the given confidence level, usually 95% [ 29 ].

This is also addressed by Bayesian statistics that allows calculating what frequentist p -values are often misinterpreted to be: the probability that the alternative (or null) hypothesis is true [ 17 ]. Moreover, one can calculate how likely it is that the parameter lies within any specified range (e.g. the risk difference being greater than .05, a lower boundary for practical significance) [ 15 , 16 ]. To gain these benefits, one needs to specify how the parameter of interest (e.g. causal risk difference between D and LC) is distributed before inspecting the data. In Bayesian statistics (unlike frequentist statistics) a parameter is a random number that expresses prior beliefs via a “prior distribution”. Such a “prior” is combined with the data result to a “posterior distribution”. This integrates both sources of information.

Note that confidence intervals also can be interpreted from the Bayesian perspective (then called “credibility interval”). This assumes that all parameter values were equally likely (uniformly distributed, strictly speaking) before analyzing the data [ 5 , 6 , 20 ].

Testing just for a non-zero association can only yield evidence for an association deviating from zero. A better indicator for the true impact of an effect/association for clinical, economic, political, or research purposes is its magnitude. If an association between D and LC after adjusting for age and gender has been discovered, then the knowledge of D has additional value in predicting an elevated LC probability beyond age and gender. However, there may be many other factors that stronger predict LC and thus should receive higher priority in a doctor’s assessment. Besides, if an association is small, it may yet be explained by modest (upward) bias. Especially large samples often yield significant results with little practical value. The p -value does not measure strength of association [ 17 ]. For instance, in a large sample, a Pearson correlation between two dimensional variables could equal 0.1 only but with a p -value <.001. A further problem arises if the significance threshold of .05 is weakened post-hoc to allow for “statistical trends” ( p between .05 and .10) because a result has “failed to reach significance” (this wording claims that there is truly an association. If this was known, no research would be necessary).

It is usually the statistician’s job to insist not only on removing the attention from pure statistical significance to confidence intervals or even Bayesian interpretation, but also to point out the necessity of a meaningful cutoff for practical significance. The substantive researcher then has to provide this cutoff.

Researchers should not draw conclusions that have not been explicitly tested for. For example, one may have found a positive association between D and LC (e.g. p  = .049), but this association is not significant (e.g. p  = .051), when adjusting for “health behavior”. This does not imply that “health behavior” “explains” the association (yet fully). The difference in magnitude of association in both analyses compared here (without and with adjustment on HB) may be very small and the difference in p -values (“borderline significance” after adjustment) likely to emerge from random error. This often applies to larger differences in p as well.

Investigators, however, might find patterns in their results that they consider worth mentioning for creating hypotheses. In the example above, adding the words “in the sample”, would clarify that they refer just to the difference of two point estimates . By default, “association” in hypotheses testing should mean “statistically significant association” (explorative analyses should instead refer to “suggestive associations”).

Conclusions

Some issues of discussing results not mentioned yet appear to require only substantive reasoning. For instance, Bradford Hill’s consideration on “plausibility” claims that a causal effect is more likely, if it is in line with biological (substantive) knowledge, or if a dose-response relation has been found [ 30 ]. However, the application of these considerations itself depends on the trueness of assumptions. For instance, bias might act differently across the dose of exposure (e.g. larger measurement error in outcome among those with higher dosage). As a consequence, a pattern observed across dose may mask a true or pretend a wrong dose-response relation [ 30 ]. This again has to be brought up by statistical expertise.

There are, however, some practical issues that hinder the cooperation we suggest. First, substantive researchers often feel discomfort when urged to make assumptions on the mechanisms behind the data, presumably because they fear to be wrong. Here, the statistician needs to insist: “If you are unable to make any assumptions, you cannot conclude anything!” And: “As a scientist you have to understand the processes that create your data.” See [ 31 ] for practical advice on how to arrive at meaningful assumptions.

Second, statisticians have long been skeptical against causal inference. Still, most of them focus solely on describing observed data with distributional models, probably because estimating causal effects has long been regarded as unfeasible with scientific methods. Training in causality remains rather new, since strict mathematical methods have been developed only in the last decades [ 7 ].

The cooperation could be improved if education in both fields focused on the insight that one cannot succeed without the other. Academic education should demonstrate that in-depth conclusions from data unavoidably involve prior beliefs. Such education should say: Data do not “speak for themselves”, because they “speak” only ambiguously and little, since they have been filtered through various biases [ 32 ]. The subjectivity introduced by addressing bias, however, unsettles many researchers. On the other hand, conventional frequentist statistics just pretends to be objective. Instead of accepting the variety of possible assumptions, it makes the absurd assumption of “no bias with probability of one”. Or it avoids causal conclusions at all if no randomized study is possible. This limits science to investigating just associations for all factors that can never be randomized (e.g. onset of depression). However, the alternative of Bayesian statistics and thinking are themselves prone to fundamental cognitive biases which should as well be subject of interdisciplinary teaching [ 33 ].

Readers may take this article as an invitation to read further papers’ discussions differently while evaluating our claims. Rather than sharing a provided conclusion (or not) they could ask themselves whether a discussion enables them to clearly specify why they share it (or not). If the result is uncertainty, this might motivate them to write their next discussion differently. The proposals made in this article could help shifting scientific debates to where they belong. Rather than arguing on misunderstandings caused by ambiguity in a conclusion’s assumptions one should argue on the assumptions themselves.

Acknowledgements

We acknowledge support by the German Research Foundation and the Open Access Publication Funds of the TU Dresden. We wish to thank Pia Grabbe and Helen Steiner for language editing and the cited authors for their outstanding work that our proposals build on.

John Venz is funded by the German Federal Ministry of Education and Research (BMBF) project no. 01ER1303 and 01ER1703. He has contributed to this manuscript outside of time funded by these projects.

Availability of data and materials

Abbreviations.

Authors’ contributions

MH and RM had the initial idea on the article. MH has taken the lead in writing. JV has contributed to the statistical parts, especially the Bayesian aspects. RM has refined the paragraphs on statistical inference. ST joined later and has added many clarifications related to the perspective of the substantive researcher. All authors have contributed to the final wording of all sections and the article’s revision. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Consent for publication, competing interests.

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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• How to Write a Discussion Section | Tips & Examples

How to Write a Discussion Section | Tips & Examples

Published on 21 August 2022 by Shona McCombes . Revised on 25 October 2022.

The discussion section is where you delve into the meaning, importance, and relevance of your results .

It should focus on explaining and evaluating what you found, showing how it relates to your literature review , and making an argument in support of your overall conclusion . It should not be a second results section .

There are different ways to write this section, but you can focus your writing around these key elements:

• Summary: A brief recap of your key results
• Interpretations: What do your results mean?
• Implications: Why do your results matter?
• Limitations: What can’t your results tell us?
• Recommendations: Avenues for further studies or analyses

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What not to include in your discussion section, step 1: summarise your key findings, step 2: give your interpretations, step 3: discuss the implications, step 4: acknowledge the limitations, step 5: share your recommendations, discussion section example.

There are a few common mistakes to avoid when writing the discussion section of your paper.

• Don’t introduce new results: You should only discuss the data that you have already reported in your results section .
• Don’t make inflated claims: Avoid overinterpretation and speculation that isn’t directly supported by your data.
• Don’t undermine your research: The discussion of limitations should aim to strengthen your credibility, not emphasise weaknesses or failures.

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Start this section by reiterating your research problem  and concisely summarising your major findings. Don’t just repeat all the data you have already reported – aim for a clear statement of the overall result that directly answers your main  research question . This should be no more than one paragraph.

Many students struggle with the differences between a discussion section and a results section . The crux of the matter is that your results sections should present your results, and your discussion section should subjectively evaluate them. Try not to blend elements of these two sections, in order to keep your paper sharp.

• The results indicate that …
• The study demonstrates a correlation between …
• This analysis supports the theory that …
• The data suggest  that …

The meaning of your results may seem obvious to you, but it’s important to spell out their significance for your reader, showing exactly how they answer your research question.

The form of your interpretations will depend on the type of research, but some typical approaches to interpreting the data include:

• Identifying correlations , patterns, and relationships among the data
• Discussing whether the results met your expectations or supported your hypotheses
• Contextualising your findings within previous research and theory
• Explaining unexpected results and evaluating their significance
• Considering possible alternative explanations and making an argument for your position

You can organise your discussion around key themes, hypotheses, or research questions, following the same structure as your results section. Alternatively, you can also begin by highlighting the most significant or unexpected results.

• In line with the hypothesis …
• Contrary to the hypothesised association …
• The results contradict the claims of Smith (2007) that …
• The results might suggest that x . However, based on the findings of similar studies, a more plausible explanation is x .

As well as giving your own interpretations, make sure to relate your results back to the scholarly work that you surveyed in the literature review . The discussion should show how your findings fit with existing knowledge, what new insights they contribute, and what consequences they have for theory or practice.

Ask yourself these questions:

• Do your results support or challenge existing theories? If they support existing theories, what new information do they contribute? If they challenge existing theories, why do you think that is?
• Are there any practical implications?

Your overall aim is to show the reader exactly what your research has contributed, and why they should care.

• These results build on existing evidence of …
• The results do not fit with the theory that …
• The experiment provides a new insight into the relationship between …
• These results should be taken into account when considering how to …
• The data contribute a clearer understanding of …
• While previous research has focused on  x , these results demonstrate that y .

Even the best research has its limitations. Acknowledging these is important to demonstrate your credibility. Limitations aren’t about listing your errors, but about providing an accurate picture of what can and cannot be concluded from your study.

Limitations might be due to your overall research design, specific methodological choices , or unanticipated obstacles that emerged during your research process.

Here are a few common possibilities:

• If your sample size was small or limited to a specific group of people, explain how generalisability is limited.
• If you encountered problems when gathering or analysing data, explain how these influenced the results.
• If there are potential confounding variables that you were unable to control, acknowledge the effect these may have had.

After noting the limitations, you can reiterate why the results are nonetheless valid for the purpose of answering your research question.

• The generalisability of the results is limited by …
• The reliability of these data is impacted by …
• Due to the lack of data on x , the results cannot confirm …
• The methodological choices were constrained by …
• It is beyond the scope of this study to …

Based on the discussion of your results, you can make recommendations for practical implementation or further research. Sometimes, the recommendations are saved for the conclusion .

Suggestions for further research can lead directly from the limitations. Don’t just state that more studies should be done – give concrete ideas for how future work can build on areas that your own research was unable to address.

• Further research is needed to establish …
• Future studies should take into account …
• Avenues for future research include …

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Writing a Discussion Section

Writing a discussion section is where you really begin to add your interpretations to the work.

This article is a part of the guide:

• Outline Examples
• Example of a Paper
• Write a Hypothesis
• Introduction

Browse Full Outline

• 1 Write a Research Paper
• 2 Writing a Paper
• 3.1 Write an Outline
• 3.2 Outline Examples
• 4.1 Thesis Statement
• 4.2 Write a Hypothesis
• 5.2 Abstract
• 5.3 Introduction
• 5.4 Methods
• 5.5 Results
• 5.6 Discussion
• 5.7 Conclusion
• 5.8 Bibliography
• 6.1 Table of Contents
• 6.2 Acknowledgements
• 6.3 Appendix
• 7.1 In Text Citations
• 7.2 Footnotes
• 7.3.1 Floating Blocks
• 7.4 Example of a Paper
• 7.5 Example of a Paper 2
• 7.6.1 Citations
• 7.7.1 Writing Style
• 7.7.2 Citations
• 8.1.1 Sham Peer Review
• 8.1.2 Advantages
• 8.1.3 Disadvantages
• 8.2 Publication Bias
• 8.3.1 Journal Rejection
• 9.1 Article Writing
• 9.2 Ideas for Topics

In this critical part of the research paper, you start the process of explaining any links and correlations apparent in your data.

If you left few interesting leads and open questions in the results section , the discussion is simply a matter of building upon those and expanding them.

The Difficulties of Writing a Discussion Section

In an ideal world, you could simply reject your null or alternative hypotheses according to the significance levels found by the statistics.

That is the main point of your discussion section, but the process is usually a lot more complex than that. It is rarely clear-cut, and you will need to interpret your findings.

For example, one of your graphs may show a distinct trend, but not enough to reach an acceptable significance level.

Remember that no significance is not the same as no difference, and you can begin to explain this in your discussion section.

Whilst your results may not be enough to reject the null hypothesis , they may show a trend that later researchers may wish to explore, perhaps by refining the experiment .

Self-Criticism at the Heart of Writing a Discussion Section

For this purpose, you should criticize the experiment, and be honest about whether your design was good enough. If not, suggest any modifications and improvements that could be made to the design.

Maybe the reason that you did not find a significant correlation is because your sampling was not random , or you did not use sensitive enough equipment.

The discussion section is not always about what you found, but what you did not find, and how you deal with that. Stating that the results are inconclusive is the easy way out, and you must always try to pick out something of value.

Using the Discussion Section to Expand Knowledge

You should always put your findings into the context of the previous research that you found during your literature review . Do your results agree or disagree with previous research?

Do the results of the previous research help you to interpret your own findings? If your results are very different, why? Either you have uncovered something new, or you may have made a major flaw with the design of the experiment .

Finally, after saying all of this, you can make a statement about whether the experiment has contributed to knowledge in the field, or not.

Unless you made so many errors that the results are completely unreliable, you will; certainly have learned something. Try not to be too broad in your generalizations to the wider world - it is a small experiment and is unlikely to change the world.

Once writing the discussion section is complete, you can move onto the next stage, wrapping up the paper with a focused conclusion .

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Journal Article: Discussion

Criteria for success.

A strong Discussion section:

• Tells the main conclusion of the paper in one or two sentences.
• Tells how the paper’s results contribute to answering the big questions posed in the Introduction.
• Explains how (and why) this work agrees or disagrees with other, similar work.
• Explains how the limitations of this study leave the big questions unanswered.
• Tells how extensions of this paper’s results will be useful for answering the big questions.

Structure Diagram

The Discussion is the part of your paper where you can share what you think your results mean with respect to the big questions you posed in your Introduction. The Introduction and Discussion are natural partners: the Introduction tells the reader what question you are working on and why you did this experiment to investigate it; the Discussion tells the reader what the results of that experiment have to say about the bigger question.

Imagine you explained the results in the paper to a labmate who looks confused and asks you, “Sure, but so what? Why was this cool or interesting?” Your response to your labmate should be similar to the content in the Discussion.

Analyze Your Audience

Different kinds of readers will expect different things from your Discussion. Readers who are not experts in your field might read your Discussion before your Results in the hopes that they can learn what your Results mean and why your paper is important without having to learn how to interpret your experimental results. They might also be interested to know what you think the future of your field is. Readers who are more familiar with your field will generally understand what the results of your experiments say, but they will be curious about how you interpreted confusing, conflicting, or complicated results.

As you write your Discussion, decide who will find each paragraph interesting and what you want them to take away from it. Successful Discussions can simultaneously provide the specific, nuanced information that experts want to read and the broader, more general statements that non-experts can appreciate.

The balance between expert and non-expert readers in your target audience will depend on the journal to which you submit. High-profile, general readership journals will have more non-expert readers, while more technical, field-specific journals have almost exclusively expert readers.

Tell how your paper is special

Weak Discussions begin with a summary of the results or a repetition of the main points of the Introduction. Strong Discussions immediately carve out a place for themselves in the large universe of papers by saying what makes this one interesting or special. One way to do this is to start the Discussion with one or two sentences that state the main finding from the results and what that finding means for the field.

Relate your results to existing results

In the Introduction, you probably helped motivate your study by citing previous results in your field. Now that you’ve laid out your results, you should tell whether your results agree or disagree with prior work and why. You might have extended previous work, showed how apparently conflicting results are actually harmonious, or exposed a contradiction that currently has no explanation.

Tell how your study’s limitations leave open the big questions

Every study is finite: you did some things and not others, and you used methods that can explain some phenomena but not others. How do the limitations of your study leave open the bigger questions? Do you just need to do more of the same kind of work? Have you shown that current methods are inadequate for answering the big question?

Every paper is a contribution to a larger scientific conversation. Hopefully, you think your contribution is somehow useful to that conversation: it provides new information or tools that will help you or other researchers move toward answers to the big questions. To explain this contribution, many Discussions end with a forward-looking statement that tries to place the paper in an expected future of research in that field.

This content was adapted from from an article originally created by the  MIT Biological Engineering Communication Lab .

Resources and Annotated Examples

Annotated example 1.

This is the discussion for an article published in Science Translational Medicine . 6 MB

Annotated Example 2

This is the discussion for an article published in Cell . 325 KB

How to Start a Discussion Section in Research? [with Examples]

The examples below are from 72,017 full-text PubMed research papers that I analyzed in order to explore common ways to start writing the Discussion section.

Research papers included in this analysis were selected at random from those uploaded to PubMed Central between the years 2016 and 2021. Note that I used the BioC API to download the data (see the References section below).

Examples of how to start writing the Discussion section

In the Discussion section, you should explain the meaning of your results, their importance, and implications. [for more information, see: How to Write & Publish a Research Paper: Step-by-Step Guide ]

The Discussion section can:

1. Start by restating the study objective

“ The purpose of this study was to investigate the relationship between muscle synergies and motion primitives of the upper limb motions.” Taken from the Discussion section of this article on PubMed

“ The main objective of this study was to identify trajectories of autonomy.” Taken from the Discussion section of this article on PubMed

“ In the present study, we investigated the whole brain regional homogeneity in patients with melancholic MDD and non-melancholic MDD at rest . “ Taken from the Discussion section of this article on PubMed

2. Start by mentioning the main finding

“ We found that autocracy and democracy have acted as peaks in an evolutionary landscape of possible modes of institutional arrangements.” Taken from the Discussion section of this article on PubMed

“ In this study, we demonstrated that the neural mechanisms of rhythmic movements and skilled movements are similar.” Taken from the Discussion section of this article on PubMed

“ The results of this study show that older adults are a diverse group concerning their activities on the Internet.” Taken from the Discussion section of this article on PubMed

3. Start by pointing out the strength of the study

“ To our knowledge, this investigation is by far the largest epidemiological study employing real-time PCR to study periodontal pathogens in subgingival plaque.” Taken from the Discussion section of this article on PubMed

“ This is the first human subject research using the endoscopic hemoglobin oxygen saturation imaging technology for patients with aero-digestive tract cancers or adenomas.” Taken from the Discussion section of this article on PubMed

“ In this work, we introduced a new real-time flow imaging method and systematically demonstrated its effectiveness with both flow phantom experiments and in vivo experiments.” Taken from the Discussion section of this article on PubMed

Most used words at the start of the Discussion

Here are the top 10 phrases used to start a discussion section in our dataset:

• Comeau DC, Wei CH, Islamaj Doğan R, and Lu Z. PMC text mining subset in BioC: about 3 million full text articles and growing,  Bioinformatics , btz070, 2019.

Further reading

• How Long Should the Discussion Section Be? Data from 61,517 Examples
• How to Write & Publish a Research Paper: Step-by-Step Guide
• “I” & “We” in Academic Writing: Examples from 9,830 Studies

How to Start a Research Project: Choosing a Topic

• Choosing a Topic

Beginning Your Research Project

You have an assignment coming up in class. You need to write a research paper, create an annotated bibliography, or make a presentation. These are just some research projects you may need to do.

This guide will show you different ways to start a research project. When following this guide, please consider 3 concepts:

• Center your personal research interests - What are you interested in?
• Take as long on each step as you would like.
• Skip steps and repeat steps as you need.

Starting from Nothing: The Mind Map

A mind map is a visual way of building a topic into a research question .

A topic is the basic idea that interests you. This is the idea that sparks your research. A topic could be "barbeque," "The Cold War," "flightless birds," or "the common cold." If you are having trouble choosing a topic , review the class syllabus or canvas modules. Find a topic covered in class that you can see yourself spending time with.

A research question is the focus of your research project. It is the thesis of your paper or the point of your presentation.

Work with us through the mind map steps to build your own research question .

To create a mind map , you will need to be able to write or type text, and the text must also be rearrangeable.

• Start with an idea like "Kitchen Design". Place your idea in the center.

• Surround your central idea with related concepts. I wrote all the kinds of kitchens I could think of. I could have also chosen to list appliances or design themes instead.

• Out of the kitchen-types, I was most drawn to "Hospital Kitchens". I then added concepts around "Hospital Kitchens". These concepts can be moved to also combined with other ideas.

• I also thought more about "Home Kitchens". I combined, "Kitchen Safety", "Consumer Preferences", and "Advertisements."

• My final version of my mind map example is very small. Don't worry if you have many more ideas and need more time rearranging your cards and planning.

I have identified two different starting research questions by combining my concepts:

• How could hospital managers design hospital kitchens to be safer for employees?
• How do kitchen appliance manufacturers advertise the safety of their products to consumers?

Research Questions

A research question is the focus of your research project. It is the thesis of your paper or the point of your presentation. Here are some requirements of a good research question:

• Research questions cannot be answered with "yes" or "no".
• Research questions can be researched.
• A small research paper shouldn't have a research question with a giant scope: How does preventative healthcare get planned?
• A small research paper should have a research question with a manageable scope: How do preventative care programs for type II diabetes in Alabaman clinics get advertised?

In this example, we narrowed the scope of our initial research question in a few ways:

• Type: "Preventative care" was limited to - "type II diabetes"
• Place: We had no initial location limit. We limited ourselves to "Alabaman clinics"
• Action: "Planned" was defined as "advertised"

Sometimes, research questions need to change slightly after you have done some research. If you were not able to find any useful resources for the example research question, then you could try changing the scope. If you cannot find anything specific to Alabaman clinics, then you could change that part of your research question to "United States clinics" or "Alabaman healthcare providers."

Still stuck? Please check Monash University's Developing Research Questions guide .

Turning your Research Question into a Search

Useful links.

• Purdue OWL: Choosing a Topic This handout provides detailed information about how to write research papers including discussing research papers as a genre, choosing topics, and finding sources.
• UNC: Brainstorming This handout discusses techniques that will help you start writing a paper and continue writing through the challenges of the revising process. Brainstorming can help you choose a topic, develop an approach to a topic, or deepen your understanding of the topic’s potential.
• University Writing Center Schedule a session with a tutor at the University Writing Center.
• Next: JSTOR >>
• Last Updated: Aug 6, 2024 12:46 PM
• URL: https://libguides.southalabama.edu/start-research

• DOI: 10.7575/aiac.ijels.v.12n.3p.17
• Corpus ID: 271730030

The Immediate Effects of Collaborative Writing on Omani University Students

• Asila Al-Makhmari
• Published in International Journal of… 30 July 2024
• Education, Linguistics

101 References

Effects and student perceptions of collaborative writing in l2.

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Are two heads better than one? Pair work and grammatical accuracy

The effect of interaction in acquiring the grammar of a second language, collaborative writing in summary writing: student perceptions and problems, collaborative writing in pairs and small groups: learners' attitudes and perceptions, what role for collaboration in writing and writing feedback, collaborative writing tasks in the l2 classroom: comparing group, pair, and individual work, collaborative writing: product, process, and students’ reflections, conflict in the classroom: controversy and learning, related papers.

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Achieving net zero greenhouse gas emissions critical to limit climate tipping risks

• Tessa Möller   ORCID: orcid.org/0000-0001-7757-1985 1 , 2 , 3 , 4 , 5   na1 ,
• Annika Ernest Högner   ORCID: orcid.org/0000-0002-4178-9664 3 , 4 , 5   na1 ,
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• Samuel Bien   ORCID: orcid.org/0000-0002-7374-265X 3 , 4 , 5 ,
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• Jonathan F. Donges 3 , 9 , 10 ,
• Johan Rockström   ORCID: orcid.org/0000-0001-8988-2983 3 , 5 , 9 &
• Nico Wunderling   ORCID: orcid.org/0000-0002-3566-323X 3 , 10 , 11  

Nature Communications volume  15 , Article number:  6192 ( 2024 ) Cite this article

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• Climate and Earth system modelling
• Climate change
• Phase transitions and critical phenomena

Under current emission trajectories, temporarily overshooting the Paris global warming limit of 1.5 °C is a distinct possibility. Permanently exceeding this limit would substantially increase the probability of triggering climate tipping elements. Here, we investigate the tipping risks associated with several policy-relevant future emission scenarios, using a stylised Earth system model of four interconnected climate tipping elements. We show that following current policies this century would commit to a 45% tipping risk by 2300 (median, 10–90% range: 23–71%), even if temperatures are brought back to below 1.5 °C. We find that tipping risk by 2300 increases with every additional 0.1 °C of overshoot above 1.5 °C and strongly accelerates for peak warming above 2.0 °C. Achieving and maintaining at least net zero greenhouse gas emissions by 2100 is paramount to minimise tipping risk in the long term. Our results underscore that stringent emission reductions in the current decade are critical for planetary stability.

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Near-term transition and longer-term physical climate risks of greenhouse gas emissions pathways

Introduction.

Climate tipping elements are complex subsystems of the Earth system that can display non-linear, often abrupt transitions in response to anthropogenic global warming 1 , 2 . This means that a small increase in global mean temperature (GMT) can trigger a large qualitative change in these subsystems. Decreasing the forcing back to its pre-industrial value will often not reverse this change, as the transitions are driven by self-amplifying feedback mechanisms that lead to hysteresis behaviour 3 , 4 .

Core tipping elements with planetary-scale impacts on the Earth system include cryosphere subsystems such as the Greenland Ice Sheet (GIS) and the West Antarctic Ice Sheet (WAIS), large-scale oceanic and atmospheric circulation patterns such as the Atlantic Meridional Overturning Circulation (AMOC), and biosphere subsystems like the Amazon Rainforest (AMAZ), the four of which we will focus on in this study. Further tipping elements include Boreal Permafrost, extra-polar mountain glaciers, and tropical coral reefs, among others 2 . Many of these tipping elements are connected through interaction processes that can stabilise or exacerbate their individual dynamics 5 , 6 , potentially enabling tipping cascades 7 . This depends on the strength of the interactions and sensitivity to increases in GMT. Consequences of climate tipping would be severe and potentially include a global sea level rise of several metres, ecosystem collapse, widespread biodiversity loss, and substantial shifts in global heat redistribution and precipitation patterns 8 . Paleorecords, as well as observational and model-based studies, provide evidence of the multistability and hysteresis behaviour of single tipping elements 1 , 2 . In spite of this, most state-of-the-art high-dimensional earth system models (ESMs) do not yet comprehensively simulate the non-linear behaviour, feedback, and interactions between some of the tipping elements due to computational limitations and a lack of processes important for resolving tipping 9 , 10 , 11 . Most state-of-the-art ESMs do not include coupled dynamic ice sheets, which renders them unable to represent the tipping point dynamics of cryosphere tipping elements as well as their links and interactions with other tipping elements 12 . The resulting lack of freshwater forcing and sea level rise can have significant repercussions for the behaviour of ocean circulations in these models. For these reasons, these models may not be suited to fully resolve tipping dynamics and interactions 9 , 10 , 11 .

A simplified but established complementary approach that we also utilise in this study is, therefore, to model tipping with fold-bifurcation models 6 , 7 , 13 , 14 (see Fig.  1 ). These conceptual models display hysteresis properties and tipping when a critical threshold is passed. The parameters of such conceptual models are based on process-understanding of the governing feedbacks of the tipping elements, such as Stommel’s salt-advection feedback for the AMOC or the melt-elevation feedback for the GIS 3 , 8 , 10 . They can be found to produce stability landscapes for single tipping elements similar to more complex domain-specific models (see Supplementary Fig.  1 ).

a Schematic fold-bifurcation diagram of a model tipping element with global mean temperature (GMT) as a forcing parameter and two stable states separated by the unstable manifold. The red arrows indicate the feedback direction of the entire system if a forcing occurs. This means, that if the system is pushed across the unstable manifold, it will move towards the opposite stable equilibrium state. b Illustrative time-evolution of one sample model run of each tipping element: Greenland Ice Sheet (GIS), West Antarctic Ice Sheet (WAIS), Atlantic Meridional Overturning Circulation (AMOC), Amazon Rainforest (AMAZ), including the threshold for state evaluation (dashed grey line).

It has been argued that the tipping behaviour of the GIS is linked to an ice sheet volume threshold 15 , 16 . However, it has also been shown that this volume threshold can be linked to a GMT threshold 17 . Similarly, the tipping behaviour of the AMOC may be primarily linked to the rate of freshwater input into the North Atlantic and AMAZ tipping behaviour has been linked to the lack of sufficient moisture supply 18 . For consistency, we have here linked the bifurcation behaviour of the tipping elements back to a GMT threshold based on multiple lines of evidence, including Earth system modelling and paleoclimate data 2 .

The urgency to understand and minimise climate tipping risks has been recognised in international climate policy for the first time at the 27th Conference of the Parties (COP27) in Egypt 19 . While uncertainties are still considerable, current best estimates find several tipping elements at risk at 1.5 °C above pre-industrial GMT levels 2 and early warning signals of an approaching transition have been observed for a number of tipping elements 20 , 21 , 22 , 23 . This provides strong scientific support for the Paris Agreement’s Article 2.1 long-term temperature goal (LTTG) aiming to limit the global temperature increase to 1.5 °C above pre-industrial levels 24 , which evidence increasingly shows is a limit, not an aspirational goal 2 , 25 . Global warming has reached 1.2 °C 26 , and current climate policy scenarios are estimated to result in 2.6 °C warming above pre-industrial levels 27 by the end of this century (with a range of 1.7–3.0 °C). Even if GMT were to be stabilised at or below 1.5 °C in the long term, a temporary overshoot above 1.5 °C is a distinct possibility and was presented prominently as the first of the Ten New Insights in Climate Science 2023/24 28 , underlining the urgency that potential impacts and associated risks of such an overshoot, including the triggering of potential tipping processes, need to be assessed 29 .

Previous studies have schematically analysed how individual and interacting tipping elements 7 respond to idealised overshoot scenarios 13 , 30 , assessing the impacts of overshoot duration, peak temperature, and long-term stabilisation temperature on tipping risks. Uncertainties in critical temperatures and critical transition times, as well as—where applicable—interactions between tipping elements were incorporated. However, to systematically assess tipping risks (see Fig.  1 ) under a given climate policy and emission pathway, the uncertainty of the climate system in response to increasing atmospheric CO 2 levels (climate sensitivity and carbon-cycle feedbacks) must be taken into account 25 , 31 . Here, we use the PROVIDEv1.2 scenario overshoot pathways 32 —an extended version of the illustrative pathways identified in the IPCC Sixth Assessment Report 33 . The considered emission pathways span a range of different possible policies, including pathways that follow current policies and pledges, as well as pathways consistent with the climate objectives of the Paris Agreement. We study the full range of GMT outcomes for each emission pathway using multiple calibrations of the stylised Earth system model PyCascades of four interacting tipping elements 34 (GIS, WAIS, AMOC, and AMAZ), to assess tipping risks in the medium term (until 2300) and long term (in equilibrium, here after 50,000 years).

Tipping risks under overshoots

The PROVIDEv1.2 emission pathways 32 cover the time from 1850 to 2300, harmonised to 2015 emission levels. GMT trajectories were derived using FaIR v.1.6.2 35 and extended linearly beyond 2300 to analyse long-term equilibrium behaviour. The mitigation objective, as set out in Article 4.1 of the Paris Agreement, aims to support the achievement of the LTTG by establishing a global requirement to achieve net zero greenhouse gas (NZGHG) emissions (aggregated using Global Warming Potential over a 100-year horizon, or GWP100) in the second half of the 21st century 36 . This would lead to a declining GMT 37 , 38 , 39 . Scenarios that achieve net zero or negative emissions by 2100 and maintain them thereafter are classified as NZGHG emission scenarios. Table  1 contains the names and properties of all analysed scenarios. The criteria for classification are described in the “Methods” section in more detail.

A comprehensive risk assessment requires consideration of the combined risks 25 of uncertainties on future emission trajectories, uncertainties in the Earth system response to these emissions including climate sensitivity and carbon-cycle feedbacks, as well as uncertainties regarding the tipping dynamics (see Fig.  2 ). Therefore, all considered scenarios take the 10–90% emission-temperature uncertainty into account, which arises from the uncertainties in the carbon cycle and climate response (see Supplementary Fig.  2 ). The tipping-related uncertainties are propagated via a Monte Carlo ensemble approach (see the “Methods” section).

a All-sector total greenhouse gas (GHG) emissions for nine investigated scenarios (GHG emissions as considered by the Kyoto Protocol, aggregated with Global Warming Potentials over a period of 100 years, GtCO2eq/year). b Resulting temperature outcomes, including climate response uncertainty, given in °C relative to preindustrial (1850–1900 average). Shaded areas correspond to the 10–90th temperature percentiles, the median is given by the line. Scenario Ref-1p5 has been added for comparison and is only defined in temperature space. c Network of the four investigated tipping elements with interactions: Greenland Ice Sheet (GIS), West Antarctic Ice Sheet (WAIS), Atlantic Meridional Overturning Circulation (AMOC), Amazon Rainforest (AMAZ). Every arrow symbolises a physical interaction mechanism between two tipping elements, categorised as destabilising (+), stabilising (−), or uncertain (±). d Critical temperature ranges under sustained warming for at least the respective tipping timescale, given in °C relative to preindustrial. The ranges of AMOC and AMAZ extend beyond the plot up to 8.0 and 6.0 °C, respectively. Intensifying grey indicates an increasing risk that a threshold will be exceeded, with lines marking the centre estimates. e Timescales of the tipping elements, with centre estimate (dot) and estimated range, from committing the tipping until it is completed. For critical temperature ranges, timescales of tipping, and interactions between tipping elements, also see Supplementary Tables  1 and 2 .

We find that tipping risks until 2300 are substantial for several of the assessed scenarios (see Fig.  3a, b ). In the long term, an overall increase in tipping risk is observed. The five pathways that do not return warming to below 1.5 °C by 2100 (CurPol-OS-1.5C, Mod-Act-OS-1.5C, Mod-Act-OS-1.0C, SSP5-3.4-OS, GS-NZGHG) display the highest risks in the medium term (Fig.  2a ), reaching 23–71% tipping risk for the scenario following current (2020) policies (median 45%; CurPol-OS-1.5C). The two pathways with less than 0.1 °C median overshoot above 1.5 °C display the lowest tipping risks in the medium term with 0–7% tipping risk (median < 1%; SP-NZGHG, SSP1-1.9). If warming is returned to below 1.5 °C by 2100 after a high overshoot (median peak temperature exceeds 1.5 °C by more than 0.1 °C), tipping risks remain at or below 10% (median 2%; Neg-OS-0C and Neg-NZGHG). Failing to return warming below 1.5 °C by 2100, despite reaching NZGHG in this time, results in tipping risks of 0–24% (median 4%; GS-NZGHG). This confirms that the risks of overshoot can be minimised if warming is swiftly reversed. However, this would require rapid employment of appropriate mitigation measures.

a In the medium-term (until 2300) and b in the long-term (50,000 years), with the risk derived from the median temperature trajectory as centre dots and the range spanning the 10-90th temperature percentiles. IPCC likelihood ranges are given on the right 72 . c Peak temperature of the overshoot, d long-term stabilisation temperature relative to pre-industrial, with 1.5 °C as a dashed line, and e duration of the overshoot above 1.5 °C until 2300.

In the long term, stabilisation temperature is one of the decisive variables for tipping risks (Fig.  2d ). We find that a long-term temperature stabilisation at 1.5 °C even without prior overshoot (Ref1p5) results in more than 50% tipping risk.

Only the three scenarios that return median warming to below 1.5 °C by 2100 and maintain NZGHG thereafter (SP-NZGHG, Neg-NZGHG, Neg-OS-0C) retain long-term median risks in the very unlikely range, and upper risks below 12%.

Fast tipping elements determine medium-term tipping risks

In Fig.  4 , we show the medium- and long-term tipping risks for each of the four considered tipping elements. In the medium term the two faster tipping elements, AMOC (tipping time: 15–300 years) and AMAZ (tipping time: 50–200 years) display the highest risks while tipping remains below 11% for the two slow-onset tipping elements, GIS (tipping time: 1000–15,000 years) and WAIS (tipping time: 500–13,000 years). In the long term, risks are highest for AMOC and WAIS. Given the threshold ranges of both ice sheets, we would expect comparable outcomes for the GIS and WAIS; however, the tipping risk for GIS is significantly lower than for WAIS: Given its lower tipping timescale, the WAIS is anticipated to tip faster than the GIS for similar temperature overshoots. Additionally, a tipping AMOC would lead to strong cooling over the GIS and potentially stabilise it (see Fig.  2c ). Such strong stabilising effects are improbable to exist for the WAIS according to the newest literature 40 .

a Medium-term tipping risk (until 2300). b Long-term tipping risk (model equilibrium). The x -axis accounts for the uncertainties in climate response, with a 90% probability of the temperature outcome exceeding the lower bound (10th percentile), and a 10% probability of the temperature outcome exceeding the upper bound (90th percentile). The y -axis denotes the tipping risk. IPCC likelihood ranges are given on the right 72 .

As we see a comparatively little increase in tipping risk from the medium term to long term for AMAZ, we conclude that AMAZ tipping is mainly caused by the overshoot itself.

The median tipping risk for the WAIS under SSP1-1.9 increases from <1% (medium-term) to 13% (long-term), and for the upper percentile from <1% to 52%, although the temperature converges below 1.5 °C. This can be explained by the fact that the tipping threshold ranges for the ice sheets begin well below 1.5 °C 2 (see Fig.  2d ).

Ref-1p5 illustrates the tipping risks if peak temperature were limited to 1.5 °C and kept constant thereafter, excluding a temporary overshoot as the cause for tipping. Tipping risks in the medium term under Ref-1p5 are below 10% for all elements, however they significantly increase in the long term.

Tipping risk by 2300 from overshooting 1.5 °C

Due to different underlying mitigation assumptions, the scenarios included in this study cross the 1.5 °C limit at different times and follow different pathways to their peak and stabilisation temperatures (see Fig.  2b ). To consider the impact of these pathways in more detail, we treat the temperature trajectories for each scenario as individual data points, focusing the analysis on the temperature space. We assess the tipping risk per peak temperature for all trajectories that temporarily exceed 1.5 °C (Fig.  5a ).

a Increase in tipping risk (%) until 2300 per overshoot peak temperature, for all trajectories with overshoot above 1.5 °C. Each point represents one temperature percentile (10–90%) of a scenario and is coloured by the corresponding scenario information. b Acceleration in tipping risk for overshoot peak temperature. Each point represents the slope of a linear fit through a window of 25 adjacent data points of peak temperature vs. tipping risk (see panel a ), thereby denoting the increase in tipping risk for this window, against the mean peak temperature within this window. The sliding window analysis is shown for all four tipping elements separately: Greenland Ice Sheet (GIS), West Antarctic Ice Sheet (WAIS), Atlantic Meridional Overturning Circulation (AMOC), Amazon Rainforest (AMAZ), as well as for the combined risk of the four considered tipping elements (panel b , yellow points). Shaded areas represent the 95% confidence interval.

We find that tipping risk increases with peak warming above 1.5 °C (Fig.  5a ). To further investigate this increase in tipping risk, we apply a sliding window analysis across all overshoot trajectories (Fig.  5b ). Overall, the increase in tipping risk per additional 0.1 °C mean overshoot peak temperature per sliding window lies within a range of around 1.0–1.5% (Fig.  5b ) for mean peak temperatures below 2.0 °C, then notably accelerates until a mean peak temperature of about 2.5 °C, above which our analysis suggests a stabilisation of the increase in tipping risk per 0.1 °C above 3%.

The contributions of the individual tipping elements to overall tipping risk increase are resolved in Fig.  5b . We find that while AMOC is the main driver of tipping risk increase at lower mean peak temperatures, the AMAZ is the main driver of the non-linear acceleration in tipping risk above 2.0 °C mean peak temperature. This can be explained by the onset of the AMAZ tipping threshold range at 2.0 °C (see Supplementary Table  1 ). However, the non-linear acceleration at ~2.0 °C mean peak temperature is also observed for the other tipping elements to smaller degrees (see also Supplementary Fig.  6b ). As an AMAZ tipping does not drive interactions in our model (compare Fig.  2c ), network effects enhancing this behaviour are driven by ice sheet or AMOC tipping (see Supplementary Fig.  6 for the impact of interactions).

The same analysis was conducted with an alternative metric to quantify overshoot, defined by the warming during the overshoot averaged over the overshoot duration (see Supplementary Figs.  7 – 10 ). The results are similar to the use of peak temperature.

Maintaining net zero greenhouse gas emissions to limit long-term tipping risks

We evaluate the impact of the long-term adherence to achieving and maintaining at least NZGHG emissions on tipping risk for a wide range of climate outcomes per emission pathway (Fig.  6 ). We find that pathways that achieve at least NZGHG lead to substantially lower tipping probabilities compared to pathways that do not achieve NZGHG (No-NZGHG), or only do so for some time (No-long-term NZGHG, see Fig.  6 ). In addition, peak temperature appears to be indicative of tipping risk in the medium term. In the long term, stabilisation temperature, determined by long-term emission behaviour, becomes more decisive (Fig.  3d ).

Each point represents one temperature percentile (10–90%) of a scenario and is coloured by the peak temperature increase. Scenarios were grouped by their adherence to NZGHG (‘NZGHG’: reach NZGHG emissions by 2100 and maintain NZGHG emissions in the long term; ‘No-long-term-NZGHG’: reach NZGHG emissions by 2100, but do not maintain NZGHG emissions in the long term; ‘No-NZGHG’: do not reach NZGHG emissions by 2100) and assessed for both investigated timeframes. Point size is fixed. White boxes indicate the medium-term, grey boxes the long-term, with the upper and lower box edges of the boxplots corresponding to the interquartile ranges of the 25th and 75th percentiles of points per class and the line denoting the median.

All three classes of pathways display higher tipping risk ranges in the long term than in the medium term. For pathways that only achieve and maintain NZGHG temporarily, the tipping risk range in the medium term is close to the range of the pathways that maintain NZGHG. In the long-term, however, these No-long-term-NZGHG scenarios reach significantly higher tipping risks. For NZGHG temperature trajectories, the median tipping risk remains below 2%, and only for a small number of high-warming trajectories, the risk exceeds 6%.

Our results demonstrate that in order to minimise tipping risks in the long term, it is crucial to achieve at least NZGHG by 2100 as set out in Article 4.1 of the Paris Agreement and maintain it in the long term.

Our study reveals that following current climate policies until 2100 may lead to high tipping risks even if long-term temperatures return to 1.5 °C by 2300. Under such an emission pathway, we report a tipping probability of 45% (median estimate, 10–90% range: 23–71%) until 2300 and of 76% (median estimate, 10–90% range: 39–98%) in the long term. Scenarios following pledged NDCs under the UNFCCC in 2020 until 2100 fail to adhere to the Paris Agreement LTTG, and even when subsequently designed such that temperatures return to 1.5 °C (median) after overshoot, we find that they are insufficient to avoid tipping risks (median estimate: 30%, 10–90% range: 10–56% until 2300). We find that tipping risk increases with every 0.1 °C of overshoot peak temperature. Further, we find a non-linear acceleration in tipping risk for peak overshoot temperatures above 2.0 °C resulting in more than 3% tipping risk increase per additional 0.1 °C peak temperature for overshoot temperatures exceeding 2.5 °C peak warming. This underscores the importance of the Paris Agreement climate objective 24 to hold warming to ‘well below 2 °C’ even in case of a temporary overshoot above 1.5 °C.

Our results show that only achieving and maintaining net zero greenhouse gas emissions, associated with a long-term decline in global temperatures, effectively limits tipping risks over the coming centuries and beyond in line with earlier studies 2 , 8 , 13 . Our findings imply that stabilisation of global temperatures at or around 1.5 °C is insufficient to limit tipping risk in the long term. In order to effectively minimise this risk, our study suggests that temperature needs to return to below 1 °C above pre-industrial level.

There is considerable uncertainty in the response of the climate system to the decline of emissions, and it is not clear how reversible GMT is after emissions cease 41 , 42 , 43 , 44 . Regional climate responses show high variability indicating that regional climatic changes might only be partially reversible 45 , 46 . Further, we cannot exclude that reinforcing feedbacks, which will ultimately lead to tipping, have already been triggered in the slow-onset cryospheric tipping elements 4 , 22 . The transient nature of an overshoot might offer a window of opportunity to counteract anthropogenic emissions with rapid interventions and stabilise the ice sheets before tipping is locked-in 22 , 47 . Possibilities of recovery and ways to recognise when a transition becomes locked-in and thereby truly irreversible are urgent topics for future research.

While we assess the probabilities of at least one element tipping on the basis of mitigation behaviour until 2300, the implications of overshooting 1.5 °C will unfold over millennia 15 . For example, Global Mean Sea Level will continue to rise for up to 10,000 years or more after emissions have reached NZGHG, due to the slow response of the ice sheets of Greenland and Antarctica 15 . The Global Mean Sea Level Rise (GMSLR) by 2300, committed from historic and currently pledged emissions until 2030, already amounts to 0.8–1.4 m 48 . Exceeding 1.5 °C may lead to a commitment of at least 2–3 m GMSLR on a timescale of 2000 years, and 6–7 m commitment on a 10,000-year timescale 15 .

The GMT changes used to assess the tipping risk in this study are derived from emission scenarios with FaIR 35 , a simple climate model that is calibrated extensively to match observations and more complex model outputs 49 . Our risk assessment, however, neglects direct temperature feedbacks from destabilising tipping elements, e.g. from disintegrating GIS or WAIS 50 and does not include carbon releases from the AMAZ or permafrost thaw 51 , 52 , 53 , 54 . Some of these effects are implicitly accounted for via the uncertainty in the climate response included in this study.

Our stylised Earth system model is designed for risk assessment under large uncertainties on climate tipping elements. As a simplification of the complex climate system, it does not allow us to make exact predictions about the characteristics of tipping 7 , 13 , 34 . We do not account for potential multistability, complex path-dependency, or spatial pattern formation 47 , 55 , 56 . Furthermore, processes that have the potential to further amplify risks, such as rate-induced tipping as recently suggested for the AMOC 57 , are not considered in our study. Anthropogenic influences other than GMT increase, such as changes in land-use 58 , are not part of the modelled dynamics, however they enter implicitly via the assumptions of some of the scenarios used in this study (for instance SSP1 and SSP5 58 , 59 ). These limitations render our results conservative, suggesting that tipping probabilities may well be even higher than we have found. This further underscores the need for a preventive approach to minimise overshoot. The scientific community is working towards more comprehensive and physically based models for the analysis of tipping dynamics, addressing and resolving some of these concerns e.g. under the Tipping Point Modelling Intercomparison Project (TIPMIP) 60 . While this work is under development, we here provide initial results and insights into which scenarios could be interesting to analyse in comprehensive models.

The available quantifications of interactions are taken from an expert elicitation 5 and present a major uncertainty. It would be desirable to constrain this uncertainty better with further analyses, to include more tipping elements, as well as process-based dynamics. However, by including the uncertainties associated with climate sensitivity, carbon-cycle feedbacks, and emissions, and by propagating the uncertainties associated with the tipping elements, our assessment allows for robust results on the tipping risks induced by current mitigation levels and relevant policy scenarios.

All scenarios in this study that fulfil and maintain NZGHG by 2100 rely on carbon dioxide removal (CDR) to varying extents to complement emission reductions to achieve peak warming and allow for a decline in warming thereafter 61 , 62 . Large-scale deployment of CDR comes with its own concerns 63 , depending on the portfolio of CDR technologies deployed. Relying on mitigation technologies that have not yet been deployed at scale is risky 64 . Extensive reliance on land-based CDR options raises sustainability concerns, including competition for land used for food production 65 and impacts on terrestrial and marine biodiversity 66 . Some CDR techniques, such as afforestation will be threatened by climate change itself 67 . Beyond these concerns, deploying CDR at scale will lead to substantial economic costs 65 and unavoidably involve debates on fairness and equity 68 .

The lowest need for CDR in our scenario selection is assumed in the SP-NZGHG scenario 69 , which contains very stringent reductions in global GHG emissions already by 2030, through a combination of strong policy interventions across multiple dimensions together with ambitious lifestyle changes. Under this scenario, substantial progress along the social and developmental dimensions would be undertaken without further exacerbating environmental degradation. However, substantial gaps in the fulfilment of all dimensions of this scenario remain due to inertia in existing systems and lack of global action 69 .

In conclusion, our study shows that current policies and NDCs are not sufficient to minimise tipping risks, even if strong emission reductions after 2100 were to return temperatures to or below 1.5 °C in the long term. Every 0.1 °C of additional overshoot above 1.5 °C increases tipping risk, and greenhouse gas emissions need to reach net zero as early as possible and maintain it to minimise the risk of climate tipping points.

Our results emphasise the fundamental relevance of the Paris Agreement climate objectives 24 , 62 for planetary stability. To effectively limit tipping risks, holding warming well below 2 °C at all times is essential even in case of a temporary overshoot above 1.5 °C. Beyond peak warming, achieving and maintaining net zero greenhouse gas emissions is paramount to limiting long-term tipping risks by bringing temperatures back down below 1.5 °C and beyond. Our results also illustrate that a global mean temperature increase of 1.5 °C is not “safe” in terms of planetary stability but must be seen as an upper limit. Returning to levels substantially lower, in the long run, might be desirable to limit tipping risks as well as other time-lagged climate impacts such as sea-level rise 15 , 48 . Domestic policies to reduce emissions need to be adopted and implemented, not only pledged 27 , and a more significant and urgent effort is needed to mitigate the risks associated with tipping elements.

Tipping risk and interacting tipping elements

In this study, we classify an element to be tipped once it has transgressed from an untipped to a tipped state at x  > 0 (see Fig.  1 ). Further, we define as tipping risk the probability that at least one of the four interacting tipping elements (AMOC, AMAZ, GIS, WAIS) has crossed its tipping point. We obtain this probability through a large-scale Monte Carlo ensemble approach that allows us to account for all parameter uncertainties arising from the tipping thresholds, timescales, interaction strengths, and directions by running the model with a large number (here 11,000) of different parameter combinations (see Fig.  2 for parameter ranges) for every temperature trajectory (evaluating 9 trajectories per emission scenario to account for uncertainties in climate sensitivity) and analysing every ensemble run according to the above-described criteria to assess the states of the four tipping elements at the time of evaluation. The tipping risk is then the percentage of ensemble runs in which at least one tipping element is classified as tipped.

Scenario classification

We select ten emission pathways from the PROVIDEv1.2 ensemble 32 to span a range of emission reductions (see Fig.  2a ). For each of these pathways, we use the resulting probabilistic GMT trajectory (assessed using FaIR v.1.6.2 35 ) (see Fig.  2b , Supplementary Fig.  2 ) to force a model of interacting tipping elements 34 designed to explore different near-term overshoot pathways, peak warmings, and long-term behaviour. We consider the full percentile uncertainty of the PROVIDE scenarios representing equilibrium climate sensitivity of 2.01–4.22 °C (5–95% range) per CO 2 doubling 35 , resulting in temperature trajectories that may deviate by more than 0.5 °C from the median.

The scenarios were chosen with policy relevance in mind, representing different levels of mitigation and thereby leading to different magnitudes and lengths of overshoot above the LTTG of the Paris Agreement (see Table  1 ). We classify the scenarios into three groups according to whether they achieve NZGHG emissions by 2100—as set out in the Paris Agreement Article 4.1—or not, and whether they maintain NZGHG in the long term: (i) ‘No-NZGHG’, (ii) ‘No-long-term-NZGHG’, and (iii) ‘NZGHG’ scenarios. NZGHG is understood here as achieving net zero Kyoto GHG emissions, i.e. CO 2 , CH 4 , N 2 O, SF 6 , HFC, and PFC emissions, as aggregated with the GWP100 metric 36 . We classify the scenarios that reach net zero emissions by 2100, however, beyond this century return to positive emissions that lead to constant rather than declining long-term temperature as ‘No-long-term-NZGHG’. The scenarios that do not reach net zero emissions by 2100 are classified as ‘No-NZGHG’. In our selection, these scenarios all employ large amounts of negative emissions from about 2130 until temperatures have stabilised at long-term levels at 1.5 or 1 °C, respectively, meaning low positive emissions from 2300 onwards.

Temperature series extension protocol

The PROVIDEv1.2 time series were linearly extrapolated beyond the year 2300, by either continuing with the stabilisation temperature, if reached in 2300, or otherwise continuing the temperature trajectories with the average slope of the time series per scenario in the period 2290–2300 until they return to 0 °C temperature increase relative to the 1850–1900 average (‘preindustrial’), remaining stable thereafter.

Modelling and propagating uncertainties of coupled climate tipping elements

The dynamics of the four interacting tipping elements (GIS, WAIS, AMOC, AMAZ) are governed by a well-established stylised coupled statistical model 13 , 34 based on the following set of coupled ordinary differential equations:

with \(\,n\) an odd integer; we here use \({n}=3\) and perform an additional sensitivity analysis to the exponent presented in the Supplementary materials, using \(\,n=\,5,\,7\,\) (Supplementary Fig.  5 ).

In this model, the state of each of the tipping elements \(i\) is denoted by \({x}_{i}\) . \({x}_{i}\) is divided into a baseline state \({x}_{i}\simeq -1.0\) and a tipped state \({x}_{i}\simeq+1.0\) . We define an element to be tipped at time \(t\) if \({x}_{i}(t)\, > \,0\) . The tipping thresholds in terms of global mean temperature increases \(\varDelta {{\rm {GMT}}}(t)\) are represented by \({T}_{{{\rm {crit}}},i}\) (see Fig.  2d , Supplementary Table  1 ). The time-scale parameter \({\tau }_{i}\) denotes the tipping timescale that an element needs to transition from its fully functional state to its fully tipped state. The values for \({\tau }_{i}\) vary over several orders of magnitude among the four tipping elements (see Fig.  2e , Supplementary Table  1 ).

The interactions between different pairs of tipping elements are modelled by the last term of Eq. ( 1 ). The link strength values \({s}_{{ij}}\) are taken from an expert elicitation 5 , and each represents a physical mechanism (see Supplementary Table  2 ). While these link strength values are quantified as relative strengths 5 , the absolute importance of the interaction is not known for many of the interactions. Therefore, we introduce the interaction-strength parameter \(d\) , which is varied between \(0\) and \(1.0\) , where \(d=0\) means no interaction between the tipping elements and \(d=1.0\) means that the upper limit of any one interaction is of the same order as the strength of the individual dynamic of the tipping element. The prefactor 1/10 sets the coupling term to the same scale as the individual dynamics term by normalising \({s}_{{ij}}\) (where \({s}_{{ij}}\) is limited to ±10) when \(d\) is varied between \(0\) and \(1.0\) .

Setting the upper boundary of \(d=1.0\) for the maximum interaction strength has the following rationale: If interaction values go beyond \(1.0\) , this will lead to scenarios where the interactions between the tipping elements dominate the state of the climate system, i.e. to cases where the tipping of one element nearly always causes a global cascade of tipping events. Paleoclimate observations indicate that functioning ocean currents and rainforests may be present even in light of disintegrated ice sheets on Greenland and Antarctica 70 . A value of \(d > 1.0\) therefore appears implausible. We have included a sensitivity analysis of tipping probability to the parameter \(d\) per scenario in our study (see Supplementary Figs.  3 , 4 ), to estimate the relative importance of interactions for tipping probabilities in our approach.

In order to quantify tipping probabilities, we propagate all relevant uncertainties (see Supplementary Tables  1 and 2 ) in the individual tipping element parameters (tipping thresholds \({T}_{{{\rm {crit}}},i}\) , tipping timescale \({\tau }_{i}\) ) as well as in their interaction strength described by the parameters \({s}_{{ij}}\) and \(d\) . As the uncertainties are considerable, we need a substantial number of Monte Carlo simulations to capture their effects accurately. The values of the tipping element uncertainties are sampled using a Quasi-Monte Carlo approach based on a latin-hypercube sampling 71 . This reduces the number of required simulations while at the same time, the uncertainty space is covered extensively. Overall, we consider 1000 individual ensemble members that vary in their tipping thresholds \({T}_{{crit},i}\) , tipping timescales \({\tau }_{i},\) and interaction strength \({s}_{{ij}}\) . This number is multiplied by 11 for the global coupling strength ( \(d\) = 0.0, 0.1, …, 1.0). Lastly, all of these 11,000 ensemble members are run through the 10 PROVIDE scenarios, which are all separated into 9 temperature percentile trajectories. This leaves us with 990,000 simulations overall, with an additional 297,000 simulations for the sensitivity analysis to the exponent \(n\) of the individual dynamics term (see Supplementary Fig.  5 ). The analysis presented in the results section is based on the averaged probabilities across the full variation of the global coupling strength \(d\) . Scenario risk profiles for the full range of outcomes depending on \(d\) can be found in the Supplementary (see Supplementary Figs.  3 and 4 ).

Data availability

The data necessary to reproduce the findings of this study is freely available (CC-BY-4.0 license) at GitHub via Zenodo at https://doi.org/10.5281/zenodo.8233417 . In case of questions or requests, please contact T.M., A.E.H. or N.W.

Code availability

The code necessary to reproduce the findings of this study is freely available (CC-BY-4.0 license) at GitHub via Zenodo at https://doi.org/10.5281/zenodo.8233417 . In case of questions or requests, please contact T.M., A.E.H. or N.W.

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Acknowledgements

We want to thank our colleague Gaurav Ganti for his helpful comments and support. The study originated in the master course Earth System Science & Anthropocene taught by J.Roc., J.F.D., N.W., and N.H.K. at the University of Potsdam in the summer semester of 2022. T.M., C.F.S., R.D.L. and J.Rog. acknowledge support from the European Union’s Horizon 2020 research and innovation programmes under grant agreement No. 101003687 (PROVIDE). N.W., N.H.K., J.Roc. and J.F.D. acknowledge support from the European Research Council Advanced Grant project ERA (Earth Resilience in the Anthropocene, ERC-2016-ADG-743080). N.H.K. is grateful for financial support from the Geo.X Young Academy. J.F.D. is grateful for financial support from the German Federal Ministry for Education and Research (BMBF) in the project ‘PIK_Change’ (grant 01LS2001A). The authors gratefully acknowledge the European Regional Development Fund (ERDF), the German Federal Ministry of Education and Research and the Land Brandenburg for supporting this project by providing resources on the high-performance computer system at the Potsdam Institute for Climate Impact Research.

Open Access funding enabled and organized by Projekt DEAL.

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These authors contributed equally: Tessa Möller, Annika Ernest Högner.

Authors and Affiliations

Energy, Climate and Environment Program, International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria

Tessa Möller, Carl-Friedrich Schleussner & Joeri Rogelj

Climate Analytics, Berlin, Germany

Tessa Möller & Carl-Friedrich Schleussner

Earth System Analysis, Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, Potsdam, Germany

Tessa Möller, Annika Ernest Högner, Samuel Bien, Niklas H. Kitzmann, Jonathan F. Donges, Johan Rockström & Nico Wunderling

Institute of Physics and Astronomy, University of Potsdam, Potsdam, Germany

Tessa Möller, Annika Ernest Högner, Samuel Bien & Niklas H. Kitzmann

Institute of Environmental Science and Geography, University of Potsdam, Potsdam, Germany

Tessa Möller, Annika Ernest Högner, Samuel Bien & Johan Rockström

Geography Department & IRI THESys, Humboldt University of Berlin, Berlin, Germany

Carl-Friedrich Schleussner

Centre for Environmental Policy, Imperial College London, London, UK

Robin D. Lamboll & Joeri Rogelj

Grantham Institute for Climate Change and the Environment, Imperial College London, London, UK

Joeri Rogelj

Stockholm Resilience Centre, Stockholm University, Stockholm, Sweden

Jonathan F. Donges & Johan Rockström

High Meadows Environmental Institute, Princeton University, Princeton, NJ, USA

Jonathan F. Donges & Nico Wunderling

Center for Critical Computational Studies (C³S), Goethe University Frankfurt, Frankfurt am Main, Germany

Nico Wunderling

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Contributions

T.M. initiated the study. T.M., A.E.H., C.F.S. and N.W. designed the study. T.M., A.E.H. and N.W. led the writing of the manuscript with input from C.F.S., S.B., N.H.K., R.D.L, J.Rog., J.F.D. and J.Roc. N.W. provided the model code. R.D.L and J.Rog. provided scenario data. T.M., A.E.H., and S.B. implemented the model simulations. T.M. and A.E.H. conducted the analysis. T.M., A.E.H., S.B. and N.H.K. prepared the figures. T.M., A.E.H., C.F.S., S.B., N.H.K., R.D.L, J.Rog., J.F.D., J.Roc. and N.W. gave final approval for publication and agreed to be held accountable for the work performed therein. N.W. led the supervision of the study.

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Möller, T., Högner, A.E., Schleussner, CF. et al. Achieving net zero greenhouse gas emissions critical to limit climate tipping risks. Nat Commun 15 , 6192 (2024). https://doi.org/10.1038/s41467-024-49863-0

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DOI : https://doi.org/10.1038/s41467-024-49863-0

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A white paper is an informational document issued by a company or not-for-profit organization to promote or highlight the features of a solution, product, or service that it offers or plans to offer.

White papers are also used as a method of presenting government policies and legislation and gauging public opinion.

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White papers are sales and marketing documents used to entice or persuade potential customers to learn more about a particular product, service, technology, or methodology.

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The facts presented in white papers are often backed by research and statistics from reliable sources and can include charts, graphs, tables, and other ways of visualizing data. A white paper can communicate an organization’s philosophy or present research findings related to an industry.

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A startup , large corporation, or government agency will use white papers differently. There are three main types of white papers: backgrounders, numbered lists, and problem/solution white papers.

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White papers differ from other marketing materials, such as brochures. Brochures and traditional marketing materials might be flashy and obvious, but a white paper is intended to provide persuasive and factual evidence that solves a problem or challenge.

White papers are commonly at least 2,500 words in length and written in an academic style.

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All of the documents listed below, publicly available on Microsoft’s website, focus on aspects of the company’s suite of cloud services. In contrast with brochures, these white papers don’t have a clear sales pitch. Instead, they dive into relevant topics, such as cloud security, hybrid clouds, and the economic benefits of adopting cloud computing.

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Cryptocurrencies have also been known to publish white papers during initial coin offerings (ICOs) and frequently issued white papers to entice users and “investors” to their projects.

Bitcoin famously launched a few months after the pseudonymous Satoshi Nakamoto issued its famous white paper online in October 2008.

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White papers may have developed from the use of “Blue Papers” in 19th century Britain, where a Parliament report cover was blue. When a topic for the government was less serious, the blue cover was discarded and published with white covers. These reports were called White Papers. In the United States, the use of government white papers often means a background report or guidance on a specific issue.

A white paper is an informational document issued by a company, government agency, or not-for-profit organization to promote the features of a solution, product, or service that it offers or plans to offer. The facts presented in white papers are often backed by research and statistics from reliable sources and are commonly written in one of three formats: backgrounders, numbered lists, and problem/solution papers.

Bitcoin.org. " Bitcoin: A Peer-to-Peer Electronic Cash System ."

Michigan State University. " Finding British Parliamentary Papers in the M.S.U. Libraries, Collections Guide No. 6 (Advanced): Parliamentary, or Sessional Papers--Discussion ."

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