Weekend batch
Avijeet is a Senior Research Analyst at Simplilearn. Passionate about Data Analytics, Machine Learning, and Deep Learning, Avijeet is also interested in politics, cricket, and football.
Free eBook: Top Programming Languages For A Data Scientist
Normality Test in Minitab: Minitab with Statistics
Machine Learning Career Guide: A Playbook to Becoming a Machine Learning Engineer
Statistics Made Easy
A hypothesis test uses sample data to determine whether or not some claim about a population parameter is true.
Whenever we perform a hypothesis test, we always write a null hypothesis and an alternative hypothesis, which take the following forms:
H 0 (Null Hypothesis): Population parameter =, ≤, ≥ some value
H A (Alternative Hypothesis): Population parameter <, >, ≠ some value
Note that the null hypothesis always contains the equal sign .
We interpret the hypotheses as follows:
Null hypothesis: The sample data provides no evidence to support some claim being made by an individual.
Alternative hypothesis: The sample data does provide sufficient evidence to support the claim being made by an individual.
For example, suppose it’s assumed that the average height of a certain species of plant is 20 inches tall. However, one botanist claims the true average height is greater than 20 inches.
To test this claim, she may go out and collect a random sample of plants. She can then use this sample data to perform a hypothesis test using the following two hypotheses:
H 0 : μ ≤ 20 (the true mean height of plants is equal to or even less than 20 inches)
H A : μ > 20 (the true mean height of plants is greater than 20 inches)
If the sample data gathered by the botanist shows that the mean height of this species of plants is significantly greater than 20 inches, she can reject the null hypothesis and conclude that the mean height is greater than 20 inches.
Read through the following examples to gain a better understanding of how to write a null hypothesis in different situations.
A biologist wants to test whether or not the true mean weight of a certain species of turtles is 300 pounds. To test this, he goes out and measures the weight of a random sample of 40 turtles.
Here is how to write the null and alternative hypotheses for this scenario:
H 0 : μ = 300 (the true mean weight is equal to 300 pounds)
H A : μ ≠ 300 (the true mean weight is not equal to 300 pounds)
It’s assumed that the mean height of males in a certain city is 68 inches. However, an independent researcher believes the true mean height is greater than 68 inches. To test this, he goes out and collects the height of 50 males in the city.
H 0 : μ ≤ 68 (the true mean height is equal to or even less than 68 inches)
H A : μ > 68 (the true mean height is greater than 68 inches)
A university states that 80% of all students graduate on time. However, an independent researcher believes that less than 80% of all students graduate on time. To test this, she collects data on the proportion of students who graduated on time last year at the university.
H 0 : p ≥ 0.80 (the true proportion of students who graduate on time is 80% or higher)
H A : μ < 0.80 (the true proportion of students who graduate on time is less than 80%)
A food researcher wants to test whether or not the true mean weight of a burger at a certain restaurant is 7 ounces. To test this, he goes out and measures the weight of a random sample of 20 burgers from this restaurant.
H 0 : μ = 7 (the true mean weight is equal to 7 ounces)
H A : μ ≠ 7 (the true mean weight is not equal to 7 ounces)
A politician claims that less than 30% of citizens in a certain town support a certain law. To test this, he goes out and surveys 200 citizens on whether or not they support the law.
H 0 : p ≥ .30 (the true proportion of citizens who support the law is greater than or equal to 30%)
H A : μ < 0.30 (the true proportion of citizens who support the law is less than 30%)
Introduction to Hypothesis Testing Introduction to Confidence Intervals An Explanation of P-Values and Statistical Significance
Hey there. My name is Zach Bobbitt. I have a Masters of Science degree in Applied Statistics and I’ve worked on machine learning algorithms for professional businesses in both healthcare and retail. I’m passionate about statistics, machine learning, and data visualization and I created Statology to be a resource for both students and teachers alike. My goal with this site is to help you learn statistics through using simple terms, plenty of real-world examples, and helpful illustrations.
you are amazing, thank you so much
Say I am a botanist hypothesizing the average height of daisies is 20 inches, or not? Does T = (ave – 20 inches) / √ variance / (80 / 4)? … This assumes 40 real measures + 40 fake = 80 n, but that seems questionable. Please advise.
Your email address will not be published. Required fields are marked *
Sign up to receive Statology's exclusive study resource: 100 practice problems with step-by-step solutions. Plus, get our latest insights, tutorials, and data analysis tips straight to your inbox!
By subscribing you accept Statology's Privacy Policy.
Hypothesis Definition, Format, Examples, and Tips
Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."
Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk, "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.
Verywell / Alex Dos Diaz
Falsifiability of a hypothesis.
Hypotheses examples.
A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.
Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."
A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.
In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:
The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.
Unless you are creating an exploratory study, your hypothesis should always explain what you expect to happen.
In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.
Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.
In many cases, researchers may find that the results of an experiment do not support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.
In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."
In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."
So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:
Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the journal articles you read . Many authors will suggest questions that still need to be explored.
To form a hypothesis, you should take these steps:
In the scientific method , falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.
Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that if something was false, then it is possible to demonstrate that it is false.
One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.
A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.
Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.
For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.
These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.
One of the basic principles of any type of scientific research is that the results must be replicable.
Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.
Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.
To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.
The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:
A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the dependent variable if you change the independent variable .
The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."
Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.
Descriptive research such as case studies , naturalistic observations , and surveys are often used when conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.
Once a researcher has collected data using descriptive methods, a correlational study can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.
Experimental methods are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).
Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually cause another to change.
The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.
Thompson WH, Skau S. On the scope of scientific hypotheses . R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607
Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:]. Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z
Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004
Nosek BA, Errington TM. What is replication ? PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691
Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies . Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18
Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.
By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."
selected template will load here
This action is not available.
\( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)
\( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)
\( \newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\)
( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\)
\( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)
\( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\)
\( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)
\( \newcommand{\Span}{\mathrm{span}}\)
\( \newcommand{\id}{\mathrm{id}}\)
\( \newcommand{\kernel}{\mathrm{null}\,}\)
\( \newcommand{\range}{\mathrm{range}\,}\)
\( \newcommand{\RealPart}{\mathrm{Re}}\)
\( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)
\( \newcommand{\Argument}{\mathrm{Arg}}\)
\( \newcommand{\norm}[1]{\| #1 \|}\)
\( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\AA}{\unicode[.8,0]{x212B}}\)
\( \newcommand{\vectorA}[1]{\vec{#1}} % arrow\)
\( \newcommand{\vectorAt}[1]{\vec{\text{#1}}} % arrow\)
\( \newcommand{\vectorB}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)
\( \newcommand{\vectorC}[1]{\textbf{#1}} \)
\( \newcommand{\vectorD}[1]{\overrightarrow{#1}} \)
\( \newcommand{\vectorDt}[1]{\overrightarrow{\text{#1}}} \)
\( \newcommand{\vectE}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash{\mathbf {#1}}}} \)
The actual test begins by considering two hypotheses . They are called the null hypothesis and the alternative hypothesis . These hypotheses contain opposing viewpoints.
\(H_0\): The null hypothesis: It is a statement of no difference between the variables—they are not related. This can often be considered the status quo and as a result if you cannot accept the null it requires some action.
\(H_a\): The alternative hypothesis: It is a claim about the population that is contradictory to \(H_0\) and what we conclude when we reject \(H_0\). This is usually what the researcher is trying to prove.
Since the null and alternative hypotheses are contradictory, you must examine evidence to decide if you have enough evidence to reject the null hypothesis or not. The evidence is in the form of sample data.
After you have determined which hypothesis the sample supports, you make a decision. There are two options for a decision. They are "reject \(H_0\)" if the sample information favors the alternative hypothesis or "do not reject \(H_0\)" or "decline to reject \(H_0\)" if the sample information is insufficient to reject the null hypothesis.
equal (=) | not equal \((\neq)\) greater than (>) less than (<) |
greater than or equal to \((\geq)\) | less than (<) |
less than or equal to \((\geq)\) | more than (>) |
\(H_{0}\) always has a symbol with an equal in it. \(H_{a}\) never has a symbol with an equal in it. The choice of symbol depends on the wording of the hypothesis test. However, be aware that many researchers (including one of the co-authors in research work) use = in the null hypothesis, even with > or < as the symbol in the alternative hypothesis. This practice is acceptable because we only make the decision to reject or not reject the null hypothesis.
A medical trial is conducted to test whether or not a new medicine reduces cholesterol by 25%. State the null and alternative hypotheses.
We want to test whether the mean GPA of students in American colleges is different from 2.0 (out of 4.0). The null and alternative hypotheses are:
We want to test whether the mean height of eighth graders is 66 inches. State the null and alternative hypotheses. Fill in the correct symbol \((=, \neq, \geq, <, \leq, >)\) for the null and alternative hypotheses.
We want to test if college students take less than five years to graduate from college, on the average. The null and alternative hypotheses are:
We want to test if it takes fewer than 45 minutes to teach a lesson plan. State the null and alternative hypotheses. Fill in the correct symbol ( =, ≠, ≥, <, ≤, >) for the null and alternative hypotheses.
In an issue of U. S. News and World Report , an article on school standards stated that about half of all students in France, Germany, and Israel take advanced placement exams and a third pass. The same article stated that 6.6% of U.S. students take advanced placement exams and 4.4% pass. Test if the percentage of U.S. students who take advanced placement exams is more than 6.6%. State the null and alternative hypotheses.
On a state driver’s test, about 40% pass the test on the first try. We want to test if more than 40% pass on the first try. Fill in the correct symbol (\(=, \neq, \geq, <, \leq, >\)) for the null and alternative hypotheses.
Bring to class a newspaper, some news magazines, and some Internet articles . In groups, find articles from which your group can write null and alternative hypotheses. Discuss your hypotheses with the rest of the class.
In a hypothesis test , sample data is evaluated in order to arrive at a decision about some type of claim. If certain conditions about the sample are satisfied, then the claim can be evaluated for a population. In a hypothesis test, we:
\(H_{0}\) and \(H_{a}\) are contradictory.
equal \((=)\) | greater than or equal to \((\geq)\) | less than or equal to \((\leq)\) | |
has: | not equal \((\neq)\) greater than \((>)\) less than \((<)\) | less than \((<)\) | greater than \((>)\) |
\(\alpha\) is preconceived. Its value is set before the hypothesis test starts. The \(p\)-value is calculated from the data.References
Data from the National Institute of Mental Health. Available online at http://www.nimh.nih.gov/publicat/depression.cfm .
It's the initial building block in the scientific method.
What makes a hypothesis testable.
Bibliography.
A scientific hypothesis is a tentative, testable explanation for a phenomenon in the natural world. It's the initial building block in the scientific method . Many describe it as an "educated guess" based on prior knowledge and observation. While this is true, a hypothesis is more informed than a guess. While an "educated guess" suggests a random prediction based on a person's expertise, developing a hypothesis requires active observation and background research.
The basic idea of a hypothesis is that there is no predetermined outcome. For a solution to be termed a scientific hypothesis, it has to be an idea that can be supported or refuted through carefully crafted experimentation or observation. This concept, called falsifiability and testability, was advanced in the mid-20th century by Austrian-British philosopher Karl Popper in his famous book "The Logic of Scientific Discovery" (Routledge, 1959).
A key function of a hypothesis is to derive predictions about the results of future experiments and then perform those experiments to see whether they support the predictions.
A hypothesis is usually written in the form of an if-then statement, which gives a possibility (if) and explains what may happen because of the possibility (then). The statement could also include "may," according to California State University, Bakersfield .
Here are some examples of hypothesis statements:
A useful hypothesis should be testable and falsifiable. That means that it should be possible to prove it wrong. A theory that can't be proved wrong is nonscientific, according to Karl Popper's 1963 book " Conjectures and Refutations ."
An example of an untestable statement is, "Dogs are better than cats." That's because the definition of "better" is vague and subjective. However, an untestable statement can be reworded to make it testable. For example, the previous statement could be changed to this: "Owning a dog is associated with higher levels of physical fitness than owning a cat." With this statement, the researcher can take measures of physical fitness from dog and cat owners and compare the two.
In an experiment, researchers generally state their hypotheses in two ways. The null hypothesis predicts that there will be no relationship between the variables tested, or no difference between the experimental groups. The alternative hypothesis predicts the opposite: that there will be a difference between the experimental groups. This is usually the hypothesis scientists are most interested in, according to the University of Miami .
For example, a null hypothesis might state, "There will be no difference in the rate of muscle growth between people who take a protein supplement and people who don't." The alternative hypothesis would state, "There will be a difference in the rate of muscle growth between people who take a protein supplement and people who don't."
If the results of the experiment show a relationship between the variables, then the null hypothesis has been rejected in favor of the alternative hypothesis, according to the book " Research Methods in Psychology " (BCcampus, 2015).
There are other ways to describe an alternative hypothesis. The alternative hypothesis above does not specify a direction of the effect, only that there will be a difference between the two groups. That type of prediction is called a two-tailed hypothesis. If a hypothesis specifies a certain direction — for example, that people who take a protein supplement will gain more muscle than people who don't — it is called a one-tailed hypothesis, according to William M. K. Trochim , a professor of Policy Analysis and Management at Cornell University.
Sometimes, errors take place during an experiment. These errors can happen in one of two ways. A type I error is when the null hypothesis is rejected when it is true. This is also known as a false positive. A type II error occurs when the null hypothesis is not rejected when it is false. This is also known as a false negative, according to the University of California, Berkeley .
A hypothesis can be rejected or modified, but it can never be proved correct 100% of the time. For example, a scientist can form a hypothesis stating that if a certain type of tomato has a gene for red pigment, that type of tomato will be red. During research, the scientist then finds that each tomato of this type is red. Though the findings confirm the hypothesis, there may be a tomato of that type somewhere in the world that isn't red. Thus, the hypothesis is true, but it may not be true 100% of the time.
The best hypotheses are simple. They deal with a relatively narrow set of phenomena. But theories are broader; they generally combine multiple hypotheses into a general explanation for a wide range of phenomena, according to the University of California, Berkeley . For example, a hypothesis might state, "If animals adapt to suit their environments, then birds that live on islands with lots of seeds to eat will have differently shaped beaks than birds that live on islands with lots of insects to eat." After testing many hypotheses like these, Charles Darwin formulated an overarching theory: the theory of evolution by natural selection.
"Theories are the ways that we make sense of what we observe in the natural world," Tanner said. "Theories are structures of ideas that explain and interpret facts."
Encyclopedia Britannica. Scientific Hypothesis. Jan. 13, 2022. https://www.britannica.com/science/scientific-hypothesis
Karl Popper, "The Logic of Scientific Discovery," Routledge, 1959.
California State University, Bakersfield, "Formatting a testable hypothesis." https://www.csub.edu/~ddodenhoff/Bio100/Bio100sp04/formattingahypothesis.htm
Karl Popper, "Conjectures and Refutations," Routledge, 1963.
Price, P., Jhangiani, R., & Chiang, I., "Research Methods of Psychology — 2nd Canadian Edition," BCcampus, 2015.
University of Miami, "The Scientific Method" http://www.bio.miami.edu/dana/161/evolution/161app1_scimethod.pdf
William M.K. Trochim, "Research Methods Knowledge Base," https://conjointly.com/kb/hypotheses-explained/
University of California, Berkeley, "Multiple Hypothesis Testing and False Discovery Rate" https://www.stat.berkeley.edu/~hhuang/STAT141/Lecture-FDR.pdf
University of California, Berkeley, "Science at multiple levels" https://undsci.berkeley.edu/article/0_0_0/howscienceworks_19
Get the world’s most fascinating discoveries delivered straight to your inbox.
'The difference between alarming and catastrophic': Cascadia megafault has 1 especially deadly section, new map reveals
Arctic 'zombie fires' rising from the dead could unleash vicious cycle of warming
Epidurals may lower risk of complications after birth, study hints
Saul Mcleod, PhD
Editor-in-Chief for Simply Psychology
BSc (Hons) Psychology, MRes, PhD, University of Manchester
Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.
Learn about our Editorial Process
Olivia Guy-Evans, MSc
Associate Editor for Simply Psychology
BSc (Hons) Psychology, MSc Psychology of Education
Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.
On This Page:
In psychology, a lab report outlines a study’s objectives, methods, results, discussion, and conclusions, ensuring clarity and adherence to APA (or relevant) formatting guidelines.
A typical lab report would include the following sections: title, abstract, introduction, method, results, and discussion.
The title page, abstract, references, and appendices are started on separate pages (subsections from the main body of the report are not). Use double-line spacing of text, font size 12, and include page numbers.
The report should have a thread of arguments linking the prediction in the introduction to the content of the discussion.
This must indicate what the study is about. It must include the variables under investigation. It should not be written as a question.
Title pages should be formatted in APA style .
The abstract provides a concise and comprehensive summary of a research report. Your style should be brief but not use note form. Look at examples in journal articles . It should aim to explain very briefly (about 150 words) the following:
The abstract comes at the beginning of your report but is written at the end (as it summarises information from all the other sections of the report).
The purpose of the introduction is to explain where your hypothesis comes from (i.e., it should provide a rationale for your research study).
Ideally, the introduction should have a funnel structure: Start broad and then become more specific. The aims should not appear out of thin air; the preceding review of psychological literature should lead logically into the aims and hypotheses.
There should be a logical progression of ideas that aids the flow of the report. This means the studies outlined should lead logically to your aims and hypotheses.
Do be concise and selective, and avoid the temptation to include anything in case it is relevant (i.e., don’t write a shopping list of studies).
USE THE FOLLOWING SUBHEADINGS:
The reference section lists all the sources cited in the essay (alphabetically). It is not a bibliography (a list of the books you used).
In simple terms, every time you refer to a psychologist’s name (and date), you need to reference the original source of information.
If you have been using textbooks this is easy as the references are usually at the back of the book and you can just copy them down. If you have been using websites then you may have a problem as they might not provide a reference section for you to copy.
References need to be set out APA style :
Author, A. A. (year). Title of work . Location: Publisher.
Author, A. A., Author, B. B., & Author, C. C. (year). Article title. Journal Title, volume number (issue number), page numbers
A simple way to write your reference section is to use Google scholar . Just type the name and date of the psychologist in the search box and click on the “cite” link.
Next, copy and paste the APA reference into the reference section of your essay.
Once again, remember that references need to be in alphabetical order according to surname.
Quantitative paper template.
Quantitative professional paper template: Adapted from “Fake News, Fast and Slow: Deliberation Reduces Belief in False (but Not True) News Headlines,” by B. Bago, D. G. Rand, and G. Pennycook, 2020, Journal of Experimental Psychology: General , 149 (8), pp. 1608–1613 ( https://doi.org/10.1037/xge0000729 ). Copyright 2020 by the American Psychological Association.
Qualitative professional paper template: Adapted from “‘My Smartphone Is an Extension of Myself’: A Holistic Qualitative Exploration of the Impact of Using a Smartphone,” by L. J. Harkin and D. Kuss, 2020, Psychology of Popular Media , 10 (1), pp. 28–38 ( https://doi.org/10.1037/ppm0000278 ). Copyright 2020 by the American Psychological Association.
Related Articles
Student Resources
How To Cite A YouTube Video In APA Style – With Examples
How to Write an Abstract APA Format
APA References Page Formatting and Example
APA Title Page (Cover Page) Format, Example, & Templates
How do I Cite a Source with Multiple Authors in APA Style?
How to Write a Psychology Essay
Looking for a hypothesis maker? This online tool for students will help you formulate a beautiful hypothesis quickly, efficiently, and for free.
Are you looking for an effective hypothesis maker online? Worry no more; try our online tool for students and formulate your hypothesis within no time.
📄 hypothesis maker: how to use it.
Our hypothesis maker is a simple and efficient tool you can access online for free.
If you want to create a research hypothesis quickly, you should fill out the research details in the given fields on the hypothesis generator.
Below are the fields you should complete to generate your hypothesis:
Once you fill the in the fields, you can click the ‘Make a hypothesis’ tab and get your results.
A hypothesis is a statement describing an expectation or prediction of your research through observation.
It is similar to academic speculation and reasoning that discloses the outcome of your scientific test . An effective hypothesis, therefore, should be crafted carefully and with precision.
A good hypothesis should have dependent and independent variables . These variables are the elements you will test in your research method – it can be a concept, an event, or an object as long as it is observable.
You can observe the dependent variables while the independent variables keep changing during the experiment.
In a nutshell, a hypothesis directs and organizes the research methods you will use, forming a large section of research paper writing.
A hypothesis is a realistic expectation that researchers make before any investigation. It is formulated and tested to prove whether the statement is true. A theory, on the other hand, is a factual principle supported by evidence. Thus, a theory is more fact-backed compared to a hypothesis.
Another difference is that a hypothesis is presented as a single statement , while a theory can be an assortment of things . Hypotheses are based on future possibilities toward a specific projection, but the results are uncertain. Theories are verified with undisputable results because of proper substantiation.
When it comes to data, a hypothesis relies on limited information , while a theory is established on an extensive data set tested on various conditions.
You should observe the stated assumption to prove its accuracy.
Since hypotheses have observable variables, their outcome is usually based on a specific occurrence. Conversely, theories are grounded on a general principle involving multiple experiments and research tests.
This general principle can apply to many specific cases.
The primary purpose of formulating a hypothesis is to present a tentative prediction for researchers to explore further through tests and observations. Theories, in their turn, aim to explain plausible occurrences in the form of a scientific study.
It would help to rely on several criteria to establish a good hypothesis. Below are the parameters you should use to analyze the quality of your hypothesis.
Testability | You should be able to test the hypothesis to present a true or false outcome after the investigation. Apart from the logical hypothesis, ensure you can test your predictions with . |
---|---|
Variables | It should have a dependent and independent variable. Identifying the appropriate variables will help readers comprehend your prediction and what to expect at the conclusion phase. |
Cause and effect | A good hypothesis should have a cause-and-effect connection. One variable should influence others in some way. It should be written as an “if-then” statement to allow the researcher to make accurate predictions of the investigation results. However, this rule does not apply to a . |
Clear language | Writing can get complex, especially when complex research terminology is involved. So, ensure your hypothesis has expressed as a brief statement. Avoid being vague because your readers might get confused. Your hypothesis has a direct impact on your entire research paper’s quality. Thus, use simple words that are easy to understand. |
Ethics | Hypothesis generation should comply with . Don’t formulate hypotheses that contravene taboos or are questionable. Besides, your hypothesis should have correlations to published academic works to look data-based and authoritative. |
Writing a hypothesis becomes way simpler if you follow a tried-and-tested algorithm. Let’s explore how you can formulate a good hypothesis in a few steps:
The first step in hypothesis creation is asking real questions about the surrounding reality.
Why do things happen as they do? What are the causes of some occurrences?
Your curiosity will trigger great questions that you can use to formulate a stellar hypothesis. So, ensure you pick a research topic of interest to scrutinize the world’s phenomena, processes, and events.
Carry out preliminary research and gather essential background information about your topic of choice.
The extent of the information you collect will depend on what you want to prove.
Your initial research can be complete with a few academic books or a simple Internet search for quick answers with relevant statistics.
Still, keep in mind that in this phase, it is too early to prove or disapprove of your hypothesis.
Now that you have a basic understanding of the topic, choose the dependent and independent variables.
Take note that independent variables are the ones you can’t control, so understand the limitations of your test before settling on a final hypothesis.
You can write your hypothesis as an ‘if – then’ expression . Presenting any hypothesis in this format is reliable since it describes the cause-and-effect you want to test.
For instance: If I study every day, then I will get good grades.
Once you have identified your variables and formulated the hypothesis, you can start the experiment. Remember, the conclusion you make will be a proof or rebuttal of your initial assumption.
So, gather relevant information, whether for a simple or statistical hypothesis, because you need to back your statement.
Finally, write down your conclusions in a research paper .
Outline in detail whether the test has proved or disproved your hypothesis.
Edit and proofread your work, using a plagiarism checker to ensure the authenticity of your text.
We hope that the above tips will be useful for you. Note that if you need to conduct business analysis, you can use the free templates we’ve prepared: SWOT , PESTLE , VRIO , SOAR , and Porter’s 5 Forces .
Updated: Oct 25th, 2023
Use our hypothesis maker whenever you need to formulate a hypothesis for your study. We offer a very simple tool where you just need to provide basic info about your variables, subjects, and predicted outcomes. The rest is on us. Get a perfect hypothesis in no time!
Statistics By Jim
Making statistics intuitive
By Jim Frost 21 Comments
The standard deviation (SD) is a single number that summarizes the variability in a dataset. It represents the typical distance between each data point and the mean. Smaller values indicate that the data points cluster closer to the mean—the values in the dataset are relatively consistent. Conversely, higher values signify that the values spread out further from the mean. Data values become more dissimilar, and extreme values become more likely.
In this post, learn why the standard deviation is essential, work through an interpretation example, and learn how to calculate it by hand.
Understanding the standard deviation is crucial. While the mean identifies a central value in the distribution, it does not indicate how far the data points fall from the center. Higher SD values signify that more data points are further away from the mean. In other words, extreme values occur more frequently.
Variability is everywhere. When you order a favorite meal at a restaurant, it isn’t exactly the same each time. Your drive time to work varies every day. Parts from an assembly line might seem identical, but they have subtly different lengths and widths.
When variability is high, you can expect to experience extreme values more frequently, which can cause problems! If the restaurant meal differs noticeably from the usual, you might not like it at all. When your morning commute takes much longer than the average travel time, you will be late. And, manufactured parts that are too far out of spec won’t perform correctly.
Frequently, we feel distressed at the extremes more than the mean. Standard deviations help you understand the variability and provides vital information about the consistency of outcomes or lack thereof!
The standard deviation can also help you assess the sample’s heterogeneity .
Related post : What is the Mean in Statistics?
Suppose two pizza restaurants advertise a 20-minute average delivery time. We’re starving and both look equally good! However, we know the mean does not tell the entire story!
Let’s assess their standard deviations to choose the restaurant. Imagine we obtain their delivery time data. One restaurant has a SD of 10 minutes while the other has a value of 5. How does this affect deliveries?
The graphs below incorporate the SDs to answer this question. The restaurant with the larger standard deviation (10 minutes) has more variable delivery times and a broader distribution curve.
In these charts, we’ll consider a 30-minute wait or longer to be unacceptable—we’re hungry! The shaded areas represent the percentage of delivery times exceeding 30 minutes. Almost 16% of deliveries for the high variability pizza joint exceed 30 minutes compared to only 2% for the low variability restaurant. They both have a mean delivery time of 20 minutes, but I know where I’d place my order when I’m hungry!
After calculating the standard deviation, you can use various methods to evaluate it. The graphs above incorporate the SD into the normal probability distribution . Alternatively, you can use the Empirical Rule or Chebyshev’s Theorem to assess how the standard deviation relates to the distribution of values. Alternatively, you can calculate the coefficient of variation , which uses both the SD and the mean.
I always recommend graphing your data in a histogram so you can see the variability. These charts really bring the SD to life!
The formula for the standard deviation is below.
Statisticians refer to the numerator portion of the standard deviation formula as the sum of squares .
Technically, this formula is for the sample standard deviation. The population version uses N in the denominator. Read my post, Measures of Variability , to learn about the differences between the population and sample varieties.
Calculating the standard deviation involves the following steps. The numbers correspond to the column numbers.
The calculations take each observation (1), subtract the sample mean (2) to calculate the difference (3), and square that difference (4).
Then, at the bottom, sum the column of squared differences and divide it by 16 (17 – 1 = 16), which equals 201. Statisticians call this value the variance .
Calculate the square root of the variance to derive the SD.
Learn how you can use the range of a dataset to estimate the standard deviation using the range rule of thumb .
The standard deviation is similar to the mean absolute deviation. Both statistics use the original data units and they compare the data points to the mean to assess variability. However, there are differences. To learn more, read my post about the mean absolute deviation (MAD) .
People frequently mix up standard deviations vs. standard errors . Both evaluate variability, but they have vastly different purposes. To learn more, read my post, The Standard Error of the Mean .
March 3, 2024 at 12:17 am
when did you write this sir?
March 5, 2024 at 4:15 pm
When citing online resources, you typically use an “Accessed” date rather than a publication date because online content can change over time. For more information, read Purdue University’s Citing Electronic Resources .
February 10, 2024 at 5:24 pm
I am so happy I have dared to send the second post. Now it is clear for me. I have also realized that I need to add much, much more knowledge to the minium one I have to fully understand the message behind the data. That was the main question running throw my head after finishing the 6 Sigma Yellow Belt. I understand how to collect the date, but how to use them to get the correct message out of them. Thank you very much again for taking the time to answer me! Have a nice day!
February 10, 2024 at 5:25 pm
You’re very welcome! So glad to help!
February 10, 2024 at 3:55 pm
Hi Jim, My name is Marius Iacomi. Statistics is absolutely new for me. Just for my understanding, in the example above should the graph of the restaurant with a 5 min standard deviation not containing 25 min on it in place of 30 min?
February 10, 2024 at 4:06 pm
The purpose of both graphs is to show the effect on delivery times when the same mean (20 minutes) but different standard deviations (5 vs. 10 minutes). Both graphs display 30 minutes because the example defines a 30 minute wait as being unacceptable.
So, consider this example to be a word problem and you need to find the probability of waiting 30 minutes or longer for the two different distributions. That’s why they both show 30 minutes. The difference in results illustrates the effects of the larger standard deviation for the same defined time period (≥ 30 minutes).
I hope that clarifies it! 🙂
February 10, 2024 at 4:53 pm
Thank you very much for your very fast reply. I hope I am not going to annoy you with my understanding. What I think, it is not clear for me, is this: why can the waiting time be above 30 minutes if the standard deviation is just 5 for the faster restaurant, and the mean is 20 for both of them. I was aspecting that the faster restaurant always delivers in maximum 25 minutes. Otherwise, what is the point of a 5-min less standard deviation compared to 10 minutes if the mean is 20 minutes. I have followed a 6 sigma yellow belt training, and I am somehow overwhelmed about the statistic needed for the data interpretation. In the end, the better restaurant was not faster at all. If what I say makes no sense, I am in trouble 🙂
February 10, 2024 at 5:04 pm
The graphs show that the shorter standard deviation does in fact reduce delivery times. Specifically, the probability of waiting for more than 30 minutes drops from 0.1587 to 0.02275 thanks to the lower SD. To see that, take a closer look at both graphs and compare the probabilities. That’s a pretty sharp drop.
With the worse restaurant, you’ll wait at least 30 minutes about 1 out of every 6 orders while for the better restaurant it is only 2 out 100 orders.
However, you can’t say that the restaurante with the shorter standard deviation will always deliver with a maximum of 25 minutes. That is just one SD above the mean (Z-score = 1). Hence, about 84% of the deliveries will be less than 25 minutes, but yet 16% will be greater.
So, yes, one restaurant is better than the other! Or at least more consistent in its delivery times. Also, keep in mind that it’s not accurate to say one restaurant is faster because they both have the same mean delivery time. Again, one is just more consistent than the other because its standard deviation is smaller.
December 12, 2023 at 4:15 am
Dear Jim, The population standard deviation is underestiamated and thus biased if the Bessel correction is not applied to small samples. However, navigating the Internet indicates that the population variance may not be biased if estimated from the common formula based on the sum of squares and number of observations. The bias in the SD is then blaimed the Jansen’s inequality and nonlinearity of calculalting the square root of the variance. This does not make sense to me. Also, the “rule of thumb” that recommends reducing the denumerator with SQRT(2) or appr 1.5 rather than 1 gives me gray hair! Can you explain?
December 12, 2023 at 6:52 pm
There are two sources of bias in this scenario.
A sample tends to underestimate the variance in the entire population. Variance using n in the denominator is a biased estimator for this reason. Bessel’s correction of using N – 1 produces an unbiased estimate of the variance.
Sounds like the problem is solved, but not quite.
The process of taking the square root of the variance to find the standard deviation introduces some bias. That gets into the nonlinear transformation of the data. So, an unbiased variance estimate (using Bessel) can lead to a biased standard deviation estimate (due to taking the square root).
Fortunately, the bias in the SD is small compared to the bias that Bessel’s correction fixes. It is often considered negligible in practice and gets smaller with larger samples.
Unbiasing the standard deviation is possible but involves more complex adjustments that are not as straightforward or universally applicable as Bessel’s correction for variance. The rule of thumb you mention is not standard as far as I’m aware.
The standard practice is to use Bessel’s correction for an unbiased variance estimate and take the square root of that for the standard deviation estimate.
I hope that helps clarify it!
October 26, 2023 at 3:56 am
Thank you, Jim. I hadn’t thought of percentiles. Your reply was really helpful. Thanks again, Anne.
October 25, 2023 at 12:31 pm
I have used fairly basic stats in the past so am used to variability etc. However my medical consultant has told me that so far I have survived more than two SD more than would be expected. I assume by expected he is using the mean. So what does the actually signify for me as a single subject? I’d appreciate any guidance.
October 25, 2023 at 3:30 pm
If you assume that survival times are normally distributed, you can use the standard deviations to calculate your survival time percentile. If you survived 2 standard deviations more than average, your Z-score is 2. Using any online Z-score calculator, you can find that you’ve survived longer than 97.7% of those with the condition. Equivalently, you’re at the 97.7th percentile. Congratulations! May you continue to increase your survival Z-score! 🙂
Of course, I don’t actually know that the survival times follow a normal distribution. If they don’t, that value will be off somewhat. How much depends on the degree of skewness. But you’ve definitely survived much longer than average.
September 16, 2023 at 6:10 pm
I’m taking a stats class at McGill and I have no idea what the professor is talking about. I understand everything you write about! Thank goodness you’re here and I just bought your book!!
September 18, 2023 at 1:18 am
Thanks so much for your kind words. They made my day because my goal is to make statistics understandable. I’m so glad my website and now books are helpful! 🙂
September 7, 2023 at 4:27 pm
For the first time I grasped the concept of standard deviation. Thank a lot Jim
September 7, 2023 at 6:56 pm
Thanks so much, Towongo. So glad I could help!
July 7, 2023 at 8:35 am
Very clear and easy to understand. Thank you.
August 24, 2022 at 4:01 am
A helpful article. It was well-explained and easy to understand. Thank you for this!
March 24, 2022 at 2:17 am
such a clean and easy explanation .thank you Jim.
August 25, 2021 at 6:35 am
Beautiful, thank you Jim
IMAGES
VIDEO
COMMENTS
6. Write a null hypothesis. If your research involves statistical hypothesis testing, you will also have to write a null hypothesis. The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0, while the alternative hypothesis is H 1 or H a.
When writing the conclusion of a hypothesis test, we typically include: Whether we reject or fail to reject the null hypothesis. The significance level. A short explanation in the context of the hypothesis test. For example, we would write: We reject the null hypothesis at the 5% significance level.
Step 5: Phrase your hypothesis in three ways. To identify the variables, you can write a simple prediction in if … then form. The first part of the sentence states the independent variable and the second part states the dependent variable. If a first-year student starts attending more lectures, then their exam scores will improve.
5.2 - Writing Hypotheses. The first step in conducting a hypothesis test is to write the hypothesis statements that are going to be tested. For each test you will have a null hypothesis ( H 0) and an alternative hypothesis ( H a ). Null Hypothesis. The statement that there is not a difference in the population (s), denoted as H 0.
Hypothesis testing is a crucial procedure to perform when you want to make inferences about a population using a random sample. These inferences include estimating population properties such as the mean, differences between means, proportions, and the relationships between variables. This post provides an overview of statistical hypothesis testing.
The Four Steps in Hypothesis Testing. STEP 1: State the appropriate null and alternative hypotheses, Ho and Ha. STEP 2: Obtain a random sample, collect relevant data, and check whether the data meet the conditions under which the test can be used. If the conditions are met, summarize the data using a test statistic.
Simple hypothesis. A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, "Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking. 4.
In hypothesis testing, the goal is to see if there is sufficient statistical evidence to reject a presumed null hypothesis in favor of a conjectured alternative hypothesis.The null hypothesis is usually denoted \(H_0\) while the alternative hypothesis is usually denoted \(H_1\). An hypothesis test is a statistical decision; the conclusion will either be to reject the null hypothesis in favor ...
Step 2: State the Alternate Hypothesis. The claim is that the students have above average IQ scores, so: H 1: μ > 100. The fact that we are looking for scores "greater than" a certain point means that this is a one-tailed test. Step 3: Draw a picture to help you visualize the problem. Step 4: State the alpha level.
Step 7: Based on steps 5 and 6, draw a conclusion about H0. If the F\calculated from the data is larger than the Fα, then you are in the rejection region and you can reject the null hypothesis with (1 − α) level of confidence. Note that modern statistical software condenses steps 6 and 7 by providing a p -value.
A hypothesis test consists of five steps: 1. State the hypotheses. State the null and alternative hypotheses. These two hypotheses need to be mutually exclusive, so if one is true then the other must be false. 2. Determine a significance level to use for the hypothesis. Decide on a significance level.
It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis. 7.
Examples. A research hypothesis, in its plural form "hypotheses," is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.
Step 7: Based on Steps 5 and 6, draw a conclusion about H 0. If F calculated is larger than F α, then you are in the rejection region and you can reject the null hypothesis with ( 1 − α) level of confidence. Note that modern statistical software condenses Steps 6 and 7 by providing a p -value. The p -value here is the probability of getting ...
A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result. ... Before writing your hypothesis, it's essential to conduct a thorough literature review to understand what is already known about the ...
Unit 12: Significance tests (hypothesis testing) Significance tests give us a formal process for using sample data to evaluate the likelihood of some claim about a population value. Learn how to conduct significance tests and calculate p-values to see how likely a sample result is to occur by random chance. You'll also see how we use p-values ...
Hypothesis testing is a statistical method used to determine if there is enough evidence in a sample data to draw conclusions about a population. It involves formulating two competing hypotheses, the null hypothesis (H0) and the alternative hypothesis (Ha), and then collecting data to assess the evidence.
Whenever we perform a hypothesis test, we always write a null hypothesis and an alternative hypothesis, which take the following forms: H0 (Null Hypothesis): Population parameter =, ≤, ≥ some value. HA (Alternative Hypothesis): Population parameter <, >, ≠ some value. Note that the null hypothesis always contains the equal sign.
A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process. Consider a study designed to examine the relationship between sleep deprivation and test ...
Review. In a hypothesis test, sample data is evaluated in order to arrive at a decision about some type of claim.If certain conditions about the sample are satisfied, then the claim can be evaluated for a population. In a hypothesis test, we: Evaluate the null hypothesis, typically denoted with \(H_{0}\).The null is not rejected unless the hypothesis test shows otherwise.
A null hypothesis is rejected if the measured data is significantly unlikely to have occurred and a null hypothesis is accepted if the observed outcome is consistent with the position held by the null hypothesis. Rejecting the null hypothesis sets the stage for further experimentation to see if a relationship between two variables exists.
A p-value, or probability value, is a number describing how likely it is that your data would have occurred by random chance (i.e., that the null hypothesis is true). The level of statistical significance is often expressed as a p-value between 0 and 1. The smaller the p -value, the less likely the results occurred by random chance, and the ...
Bibliography. A scientific hypothesis is a tentative, testable explanation for a phenomenon in the natural world. It's the initial building block in the scientific method. Many describe it as an ...
A confidence interval (CI) is a range of values that is likely to contain the value of an unknown population parameter. These intervals represent a plausible domain for the parameter given the characteristics of your sample data. Confidence intervals are derived from sample statistics and are calculated using a specified confidence level.
Author, A. A., Author, B. B., & Author, C. C. (year). Article title. Journal Title, volume number (issue number), page numbers. A simple way to write your reference section is to use Google scholar. Just type the name and date of the psychologist in the search box and click on the "cite" link. Next, copy and paste the APA reference into the ...
You can write your hypothesis as an 'if - then' expression. Presenting any hypothesis in this format is reliable since it describes the cause-and-effect you want to test. ... So, gather relevant information, whether for a simple or statistical hypothesis, because you need to back your statement. Step #6: Record Your Findings. Finally ...
The standard deviation (SD) is a single number that summarizes the variability in a dataset. It represents the typical distance between each data point and the mean. Smaller values indicate that the data points cluster closer to the mean—the values in the dataset are relatively consistent. Conversely, higher values signify that the values ...