What it is and how it's used in sociology
A hypothesis is a prediction of what will be found at the outcome of a research project and is typically focused on the relationship between two different variables studied in the research. It is usually based on both theoretical expectations about how things work and already existing scientific evidence.
Within social science, a hypothesis can take two forms. It can predict that there is no relationship between two variables, in which case it is a null hypothesis . Or, it can predict the existence of a relationship between variables, which is known as an alternative hypothesis.
In either case, the variable that is thought to either affect or not affect the outcome is known as the independent variable, and the variable that is thought to either be affected or not is the dependent variable.
Researchers seek to determine whether or not their hypothesis, or hypotheses if they have more than one, will prove true. Sometimes they do, and sometimes they do not. Either way, the research is considered successful if one can conclude whether or not a hypothesis is true.
A researcher has a null hypothesis when she or he believes, based on theory and existing scientific evidence, that there will not be a relationship between two variables. For example, when examining what factors influence a person's highest level of education within the U.S., a researcher might expect that place of birth, number of siblings, and religion would not have an impact on the level of education. This would mean the researcher has stated three null hypotheses.
Taking the same example, a researcher might expect that the economic class and educational attainment of one's parents, and the race of the person in question are likely to have an effect on one's educational attainment. Existing evidence and social theories that recognize the connections between wealth and cultural resources , and how race affects access to rights and resources in the U.S. , would suggest that both economic class and educational attainment of the one's parents would have a positive effect on educational attainment. In this case, economic class and educational attainment of one's parents are independent variables, and one's educational attainment is the dependent variable—it is hypothesized to be dependent on the other two.
Conversely, an informed researcher would expect that being a race other than white in the U.S. is likely to have a negative impact on a person's educational attainment. This would be characterized as a negative relationship, wherein being a person of color has a negative effect on one's educational attainment. In reality, this hypothesis proves true, with the exception of Asian Americans , who go to college at a higher rate than whites do. However, Blacks and Hispanics and Latinos are far less likely than whites and Asian Americans to go to college.
Formulating a hypothesis can take place at the very beginning of a research project , or after a bit of research has already been done. Sometimes a researcher knows right from the start which variables she is interested in studying, and she may already have a hunch about their relationships. Other times, a researcher may have an interest in a particular topic, trend, or phenomenon, but he may not know enough about it to identify variables or formulate a hypothesis.
Whenever a hypothesis is formulated, the most important thing is to be precise about what one's variables are, what the nature of the relationship between them might be, and how one can go about conducting a study of them.
Updated by Nicki Lisa Cole, Ph.D
Hypothesis is a hypothesis is fundamental concept in the world of research and statistics. It is a testable statement that explains what is happening or observed. It proposes the relation between the various participating variables.
Hypothesis is also called Theory, Thesis, Guess, Assumption, or Suggestion . Hypothesis creates a structure that guides the search for knowledge.
In this article, we will learn what hypothesis is, its characteristics, types, and examples. We will also learn how hypothesis helps in scientific research.
Table of Content
Characteristics of hypothesis, sources of hypothesis, types of hypothesis, functions of hypothesis, how hypothesis help in scientific research.
Hypothesis is a suggested idea or an educated guess or a proposed explanation made based on limited evidence, serving as a starting point for further study. They are meant to lead to more investigation.
It’s mainly a smart guess or suggested answer to a problem that can be checked through study and trial. In science work, we make guesses called hypotheses to try and figure out what will happen in tests or watching. These are not sure things but rather ideas that can be proved or disproved based on real-life proofs. A good theory is clear and can be tested and found wrong if the proof doesn’t support it.
A hypothesis is a proposed statement that is testable and is given for something that happens or observed.
Here are some key characteristics of a hypothesis:
Hypotheses can come from different places based on what you’re studying and the kind of research. Here are some common sources from which hypotheses may originate:
Here are some common types of hypotheses:
Complex hypothesis, directional hypothesis.
Alternative hypothesis (h1 or ha), statistical hypothesis, research hypothesis, associative hypothesis, causal hypothesis.
Simple Hypothesis guesses a connection between two things. It says that there is a connection or difference between variables, but it doesn’t tell us which way the relationship goes. Example: Studying more can help you do better on tests. Getting more sun makes people have higher amounts of vitamin D.
Complex Hypothesis tells us what will happen when more than two things are connected. It looks at how different things interact and may be linked together. Example: How rich you are, how easy it is to get education and healthcare greatly affects the number of years people live. A new medicine’s success relies on the amount used, how old a person is who takes it and their genes.
Directional Hypothesis says how one thing is related to another. For example, it guesses that one thing will help or hurt another thing. Example: Drinking more sweet drinks is linked to a higher body weight score. Too much stress makes people less productive at work.
Non-Directional Hypothesis are the one that don’t say how the relationship between things will be. They just say that there is a connection, without telling which way it goes. Example: Drinking caffeine can affect how well you sleep. People often like different kinds of music based on their gender.
Null hypothesis is a statement that says there’s no connection or difference between different things. It implies that any seen impacts are because of luck or random changes in the information. Example: The average test scores of Group A and Group B are not much different. There is no connection between using a certain fertilizer and how much it helps crops grow.
Alternative Hypothesis is different from the null hypothesis and shows that there’s a big connection or gap between variables. Scientists want to say no to the null hypothesis and choose the alternative one. Example: Patients on Diet A have much different cholesterol levels than those following Diet B. Exposure to a certain type of light can change how plants grow compared to normal sunlight.
Statistical Hypothesis are used in math testing and include making ideas about what groups or bits of them look like. You aim to get information or test certain things using these top-level, common words only. Example: The average smarts score of kids in a certain school area is 100. The usual time it takes to finish a job using Method A is the same as with Method B.
Research Hypothesis comes from the research question and tells what link is expected between things or factors. It leads the study and chooses where to look more closely. Example: Having more kids go to early learning classes helps them do better in school when they get older. Using specific ways of talking affects how much customers get involved in marketing activities.
Associative Hypothesis guesses that there is a link or connection between things without really saying it caused them. It means that when one thing changes, it is connected to another thing changing. Example: Regular exercise helps to lower the chances of heart disease. Going to school more can help people make more money.
Causal Hypothesis are different from other ideas because they say that one thing causes another. This means there’s a cause and effect relationship between variables involved in the situation. They say that when one thing changes, it directly makes another thing change. Example: Playing violent video games makes teens more likely to act aggressively. Less clean air directly impacts breathing health in city populations.
Hypotheses have many important jobs in the process of scientific research. Here are the key functions of hypotheses:
Researchers use hypotheses to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method:
Mathematics Maths Formulas Branches of Mathematics
Hypothesis is a testable statement serving as an initial explanation for phenomena, based on observations, theories, or existing knowledge . It acts as a guiding light for scientific research, proposing potential relationships between variables that can be empirically tested through experiments and observations.
The hypothesis must be specific, testable, falsifiable, and grounded in prior research or observation, laying out a predictive, if-then scenario that details a cause-and-effect relationship. It originates from various sources including existing theories, observations, previous research, and even personal curiosity, leading to different types, such as simple, complex, directional, non-directional, null, and alternative hypotheses, each serving distinct roles in research methodology .
The hypothesis not only guides the research process by shaping objectives and designing experiments but also facilitates objective analysis and interpretation of data , ultimately driving scientific progress through a cycle of testing, validation, and refinement.
What is a hypothesis.
A guess is a possible explanation or forecast that can be checked by doing research and experiments.
The components of a Hypothesis are Independent Variable, Dependent Variable, Relationship between Variables, Directionality etc.
Testability, Falsifiability, Clarity and Precision, Relevance are some parameters that makes a Good Hypothesis
You cannot prove conclusively that most hypotheses are true because it’s generally impossible to examine all possible cases for exceptions that would disprove them.
Hypothesis testing is used to assess the plausibility of a hypothesis by using sample data
Yes, you can change or improve your ideas based on new information discovered during the research process.
Hypotheses are used to support scientific research and bring about advancements in knowledge.
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A title page is required for all APA Style papers. There are both student and professional versions of the title page. Students should use the student version of the title page unless their instructor or institution has requested they use the professional version. APA provides a student title page guide (PDF, 199KB) to assist students in creating their title pages.
The student title page includes the paper title, author names (the byline), author affiliation, course number and name for which the paper is being submitted, instructor name, assignment due date, and page number, as shown in this example.
Title page setup is covered in the seventh edition APA Style manuals in the Publication Manual Section 2.3 and the Concise Guide Section 1.6
Student papers do not include a running head unless requested by the instructor or institution.
Follow the guidelines described next to format each element of the student title page.
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Paper title | Place the title three to four lines down from the top of the title page. Center it and type it in bold font. Capitalize of the title. Place the main title and any subtitle on separate double-spaced lines if desired. There is no maximum length for titles; however, keep titles focused and include key terms. |
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Author names | Place one double-spaced blank line between the paper title and the author names. Center author names on their own line. If there are two authors, use the word “and” between authors; if there are three or more authors, place a comma between author names and use the word “and” before the final author name. | Cecily J. Sinclair and Adam Gonzaga |
Author affiliation | For a student paper, the affiliation is the institution where the student attends school. Include both the name of any department and the name of the college, university, or other institution, separated by a comma. Center the affiliation on the next double-spaced line after the author name(s). | Department of Psychology, University of Georgia |
Course number and name | Provide the course number as shown on instructional materials, followed by a colon and the course name. Center the course number and name on the next double-spaced line after the author affiliation. | PSY 201: Introduction to Psychology |
Instructor name | Provide the name of the instructor for the course using the format shown on instructional materials. Center the instructor name on the next double-spaced line after the course number and name. | Dr. Rowan J. Estes |
Assignment due date | Provide the due date for the assignment. Center the due date on the next double-spaced line after the instructor name. Use the date format commonly used in your country. | October 18, 2020 |
| Use the page number 1 on the title page. Use the automatic page-numbering function of your word processing program to insert page numbers in the top right corner of the page header. | 1 |
The professional title page includes the paper title, author names (the byline), author affiliation(s), author note, running head, and page number, as shown in the following example.
Follow the guidelines described next to format each element of the professional title page.
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Paper title | Place the title three to four lines down from the top of the title page. Center it and type it in bold font. Capitalize of the title. Place the main title and any subtitle on separate double-spaced lines if desired. There is no maximum length for titles; however, keep titles focused and include key terms. |
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Author names
| Place one double-spaced blank line between the paper title and the author names. Center author names on their own line. If there are two authors, use the word “and” between authors; if there are three or more authors, place a comma between author names and use the word “and” before the final author name. | Francesca Humboldt |
When different authors have different affiliations, use superscript numerals after author names to connect the names to the appropriate affiliation(s). If all authors have the same affiliation, superscript numerals are not used (see Section 2.3 of the for more on how to set up bylines and affiliations). | Tracy Reuter , Arielle Borovsky , and Casey Lew-Williams | |
Author affiliation
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| Department of Nursing, Morrigan University |
When different authors have different affiliations, use superscript numerals before affiliations to connect the affiliations to the appropriate author(s). Do not use superscript numerals if all authors share the same affiliations (see Section 2.3 of the for more). | Department of Psychology, Princeton University | |
Author note | Place the author note in the bottom half of the title page. Center and bold the label “Author Note.” Align the paragraphs of the author note to the left. For further information on the contents of the author note, see Section 2.7 of the . | n/a |
| The running head appears in all-capital letters in the page header of all pages, including the title page. Align the running head to the left margin. Do not use the label “Running head:” before the running head. | Prediction errors support children’s word learning |
| Use the page number 1 on the title page. Use the automatic page-numbering function of your word processing program to insert page numbers in the top right corner of the page header. | 1 |
Table of contents.
Hypothesis testing is one of the major methods in the grand scheme of statistics. It assists in deriving data-driven decisions, evaluating theories, and inferring meaningful conclusions from data. But what is hypothesis testing, and why is it so important in statistics? For those who want to learn and enhance their skills, the IIM Nagpur PG Certificate Programme in Data Science for Business Excellence and Innovation offers the perfect platform for learning from professionals, gaining hands-on experience, and moving up the value chain.
Hypothesis testing is a means to make decisions about population properties based on the information derived from a sample. It simply provides a basis for the testing of assumptions or hypotheses made about a certain parameter in a population. Statement of the whole process involves a null hypothesis denoted by H0 and an alternative hypothesis denoted by H1, followed by the collection of data and subsequent analysis to see whether it favours the rejection of the null hypothesis in favour of the alternative.
In other words, hypothesis testing gives the answers to such questions as:
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The hypothesis testing process is quite logical and it contains several critical steps:
Within hypothesis testing, there are varieties that best fit different sorts of data and research questions. The main kinds include:
Let us see how it works in practice with some examples.
A company would like to know whether the new layout of a website, version B, provides more user engagement than the old layout, version A. The null hypothesis is that both versions have no difference in engagement. The alternative hypothesis is that the new layout leads to higher engagement. Performing an A/B test and examining the data will enable the company to either accept the new layout or not.
A new medication in healthcare is tested in a clinical trial. Researchers want to establish if it can reduce blood pressure better than a previously existing one. The null hypothesis, H0, assumes no difference in blood pressure reduction between the new and the existing medications. The alternative hypothesis, H1, is that the new medication is more potent. The researchers can make an informed inference after administering different groups with the medications, measuring changes in blood pressure, and comparing such changes using a t-test.
A manufacturer wants to be certain that the true mean diameter for bolts manufactured by a machine is 5 mm. The null hypothesis is that the mean diameter is 5 mm. The alternative hypothesis is that the mean diameter is not 5 mm. The manufacturer conducts a hypothesis test based on a sample of bolts, measuring their diameters.
Hypothesis testing is not merely an exercise alma mater; rather, it is something much needed for making a business decision. It may support enhancing or optimizing business strategies, operations, and customer satisfaction by rigorous testing of assumptions based on data-driven decisions. For instance, this would include areas such as product development, market research, or quality assurance.
This nuance in business matters a lot to decisions. It is useful in finding chances for innovation and optimizing marketing strategies to assure product quality. Hypothesis testing is used by businesses to test new ideas and concepts before full-scale implementation to avoid loss and risk, hence maximizing the chances of return maximization.
For instance, in the retail sector, one can use hypothesis testing to determine if the promotional campaign is effective. Comparing the sales data before and after a promotion will help the business learn if the promotion worked. In manufacturing, hypothesis testing can be used to ensure that production processes are within specified limits, to maintain the quality and consistency of the product.
Moreover, hypothesis testing is at the core of any financial analysis and forecasting . It helps the financial analyst measure investment performance, identify anomalies in financial data, and graph the future trends. It provides a firm statistical method to assist an informed decision that turns on empirical evidence rather than gut feelings or assumptions.
Let us consider in greater detail some of the important hypothesis testing types enumerated earlier, and when and how they are used.
The Z-test is very useful in large samples, n > 30, with known population variances. It is frequently used in quality control and survey analysis. For example, suppose a company wants to compare the average customer satisfaction score from a large sample with the national average. A Z-test will tell whether their customer service passes or fails the national standard.
The T-test is useful when sample sizes are small and the population variance is unknown. There are three variants of the T-test, which makes it versatile in application:
Chi-square tests are used in categorical data. They are aimed at establishing whether the relationship between the two variables is significant. Chi-square tests may be applied in market research to establish whether some relationships exist, say between customer demographics and buying behavior. For example, it would tell if age groups have varying preferences for a given product or not.
ANOVA is used to compare three or more group means. The application of this statistical tool is thus very appropriate, especially in multiple treatment experiments. For instance, to establish which type of crop variety yields the highest under similar conditions, an agricultural scientist will apply the ANOVA to compare the yields of different crop varieties.
The F-test is carried out between the two variances to check if they are significantly different. One of the areas this test is commonly applied includes quality control processes, especially when it comes to comparing the variability of product measurements from different machines.
While hypothesis testing is an invaluable tool, there are some practical considerations which have to be borne in mind for effective and reliable results.
Hypothesis testing is one of the critical parts of a statistical analysis and hence sets a clearly defined framework within which data-driven decisions can be made. Once you have grasped how the process and types of hypothesis testing work, you will be able to apply such methods to real-world problems in ways that can drive business excellence and innovation.
It isn’t a theoretical concept but a practical tool that enables better decision-making across many domains. Be it healthcare, marketing, or manufacturing, it is understanding hypothesis testing that, with the application of this understanding, leads toward more informed and data-driven decision-making to propel the organization in the proper direction.
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Research on the application of pruning algorithm based on local linear embedding method in traffic sign recognition, 1. introduction.
VGG [ ] | Based on the VGG network, the channel pruning method based on Lasso regression is used to reduce the convolutional channel and compress the width of the network horizontally. | This method compresses the scale of the traffic sign recognition network, which is conducive to the real-time application of traffic sign systems in urban road conditions. | If there is a set of highly correlated features, Lasso regression tends to choose one of the features and ignore all the others, which leads to instability in the results. |
Knowledge-based distillation framework [ ] | Two lightweight convolutional neural network structures are designed; the first deep network model is used as the teacher model, and the second shallow model is used as the student model. Through knowledge distillation, the student model can learn the function mapping similar to the teacher model and then trim the redundant feature channels according to the scale factor that tends to zero in the Batch Normalization layer. | According to the experimental data in this paper, the compressed network model has a more compact structure and better performance than the mainstream traffic sign recognition methods. | The experimental data in this paper indicate that compared with the mainstream traffic sign recognition methods, the compressed network model has a more compact structure and better performance. |
A lightweight YOLOv3 traffic sign detection algorithm [ ] | Use both shallow and deep feature extraction in convolutional neural networks. | This method prunes the model while maintaining the accuracy of the model, thus establishing a lightweight traffic sign detection model with stronger robustness. |
ADMM-based pruning algorithm |
Input: dataset D, ADMM iteration number K, N filters to be pruned layer by layer, pruning threshold β , augmented hyperparameter ρ, the weight set S of different pruning methods. Output: Weight parameter W. 1: for k = 1, 2,..., K do 2: Sample from D(x, y), solve for Z in Equation (7). 4: Applying the Adam optimizer to the outer minimum of Equation (9) yields {Z } 5: Solve Equation (11) using Formula (10) to obtain {W } 6: 7: end for |
Similar filter pruning algorithm based on JS_ divergence |
Input: N: filters to be pruned layer by layer. β : pruning threshold. 1: Obtain the absolute value of the weight parameter of N filters in layer i in the pre-trained model. 2: Calculate the JS_ divergence value between N filters in step 1 and between each filter and other filters, and sort these filters according to the JS_ divergence value. 3: Select the pre-Nβ pair filter sorted in step 2 as the filter pair to be pruned. 4: Calculate the information entropy value of each filter, remove the filter with a small information entropy value from the filters to be pruned in step 3, and retain the filter with a larger information entropy value. |
Similar filter pruning algorithm based on local linear embedding |
Input: dataset D, the number of pre-training iterations K′, the number of model weight optimization iterations K″, the number of pruning iterations K, N filters to be pruned layer by layer, pruning threshold β , augmented hyperparameter ρ, the weight set S of different pruning methods. //Output: Weight parameter W 1: for k = 1, 2, ..., K′ do 2: Sample from D (x, y) 3: Data normalization and data enhancement preprocessing of (x, y) yields (x′, y′) 4: Take (x′, y′) as input to initial training for iterative training 5: end for //Get pre-trained model 6: for k = 1, 2, ..., K″ do 7: Take samples from D (x, y) 8: Data normalization and data enhancement preprocessing of (x, y) yields (x′, y′) 9: Take (x′, y′) as input for model weight optimization training. 10: Apply Algorithm 2 to obtain the pruned weight parameter Z by using the weight parameter W of the pre-trained model 11: Take the weight parameters W and Z as inputs to Algorithm 1 and solve for Z in Equation (7). 12: Applying the Adam optimizer to the outer minimum of Equation (11) yields {Z } 13: 14: end for 15: //Get the weight optimization model 16: for k = 1, 2, ..., K do 17: Take samples from D (x, y) 18: Data normalization and data enhancement preprocessing of (x, y) yields (x′, y′) 19: Take (x′, y′) as input for model weight optimization training. 20: Optimize the weight parameter of the model W′ using Algorithm 2 to obtain the pruned weight parameter Z’ 21: end for 22: //Get weight model after pruning |
3.1. dataset, 3.2. image preprocessing methods, 4. experiments, 4.1. experimental details, 4.2. experimental evaluation index, 4.3. experimental results and analysis, 4.3.1. experimental results and analysis of hypothesis proofs, 4.3.2. experimental results and analysis of sj_lle method pruning, 5. conclusions and future works, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.
Click here to enlarge figure
Pruning Method\Pruning Rate | Baseline | 60% | 70% | 80% | 90% | 95% |
---|---|---|---|---|---|---|
ASFP_LLE | 67.27 | 64.82 | 63.54 | 60.98 | 45.64 | 37.93 |
ASFP | 67.27 | 64.06 | 62.32 | 59.67 | 43.15 | 35.45 |
PFEC_LLE | 67.27 | 66.26 | 65.43 | 61.56 | 48.46 | 36.98 |
PFEC | 67.27 | 65.37 | 63.83 | 60.43 | 46.27 | 34.27 |
LWCE_LLE | 67.27 | 64.38 | 63.16 | 60.44 | 48.73 | 33.10 |
LWCE | 67.27 | 63.84 | 61.78 | 59.32 | 46.35 | 30.46 |
FPGM_LLE | 67.27 | 65.32 | 63.41 | 60.85 | 43.96 | 33.52 |
FPGM | 67.27 | 64.63 | 62.25 | 59.41 | 42.25 | 31.24 |
Pruning Method\Pruning Rate | Baseline | 60% | 70% | 80% | 90% | 95% |
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ASFP_LLE | 94.47 | 90.77 | 90.24 | 83.98 | 63.54 | 50.93 |
ASFP | 94.47 | 90.23 | 89.82 | 82.19 | 61.57 | 48.66 |
PFEC_LLE | 94.47 | 91.74 | 91.03 | 85.91 | 65.34 | 49.76 |
PFEC | 94.47 | 91.74 | 91.05 | 90.14 | 84.85 | 48.12 |
LWCE_LLE | 94.47 | 90.72 | 89.97 | 83.06 | 66.45 | 49.75 |
LWCE | 94.47 | 90.16 | 89.05 | 81.85 | 64.66 | 47.14 |
FPGM_LLE | 94.47 | 91.37 | 89.64 | 86.13 | 86.13 | 47.32 |
FPGM | 94.47 | 90.7 | 88.73 | 85.07 | 59.74 | 45.44 |
Pruning Method\Pruning Rate | Baseline | 10% | 20% | 40% | 50% | 70% |
---|---|---|---|---|---|---|
SJ-LLE | 98.78 | 98.72 | 98.13 | 97.95 | 96.88 | 96.09 |
ASFP | 98.78 | 98.06 | 96.67 | 95.85 | 94.75 | 93.19 |
HYDRA | 98.78 | 98.13 | 97.10 | 95.42 | 94.84 | 94.15 |
Filter | 98.78 | 97.96 | 95.64 | 93.82 | 92.96 | 92.12 |
FPGM | 98.78 | 98.03 | 97.15 | 95.39 | 95.04 | 94.51 |
Pruning Method\Pruning Rate | Baseline | 10% | 20% | 40% | 50% | 70% |
---|---|---|---|---|---|---|
SJ-LLE | 99.03 | 99.09 | 98.72 | 98.05 | 97.58 | 96.04 |
ASFP | 99.03 | 98.26 | 96.82 | 95.94 | 94.48 | 93.84 |
HYDRA | 99.03 | 98.28 | 97.72 | 96.12 | 95.51 | 94.66 |
Filter | 99.03 | 97.22 | 95.81 | 95.95 | 93.47 | 92.95 |
FPGM | 99.03 | 98.53 | 98.01 | 96.95 | 96.03 | 95.18 |
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Wang, W.; Liu, X. Research on the Application of Pruning Algorithm Based on Local Linear Embedding Method in Traffic Sign Recognition. Appl. Sci. 2024 , 14 , 7184. https://doi.org/10.3390/app14167184
Wang W, Liu X. Research on the Application of Pruning Algorithm Based on Local Linear Embedding Method in Traffic Sign Recognition. Applied Sciences . 2024; 14(16):7184. https://doi.org/10.3390/app14167184
Wang, Wei, and Xiaorui Liu. 2024. "Research on the Application of Pruning Algorithm Based on Local Linear Embedding Method in Traffic Sign Recognition" Applied Sciences 14, no. 16: 7184. https://doi.org/10.3390/app14167184
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Definition: Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation. Hypothesis is often used in scientific research to guide the design of experiments ...
Research begins with a research question and a research hypothesis. But what are the characteristics of a good hypothesis? In this article, we dive into the types of research hypothesis, explain how to write a research hypothesis, offer research hypothesis examples and answer top FAQs on research hypothesis. Read more!
A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection.
A research hypothesis is an assumption or a tentative explanation for a specific process observed during research. Unlike a guess, research hypothesis is a calculated, educated guess proven or disproven through research methods.
A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes - specificity, clarity and testability.
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.
A hypothesis is a tentative statement about the relationship between two or more variables. Explore examples and learn how to format your research hypothesis.
A research hypothesis is a statement that brings up a question and predicts what might happen. It's really important in the scientific method and is used in experiments to figure things out. Essentially, it's an educated guess about how things are connected in the research.
The hypothesis is an essential step of the scientific method. But what makes an effective research hypothesis, how do you create one, and what types of hypotheses are there?
A research hypothesis defines the theory or problem your research intends to test. It is the "educated guess" of what the final results of your research or experiment will be. Before you can write the research hypothesis and its alternative, you must focus and define your research problem.
The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development ...
How to Write a Hypothesis: A Step-by-Step Guide The hypothesis is a common component in scientific research. But what is it and what role does it play in the scientific method? This article will look at some examples of hypotheses, the use of independent and dependent variables in hypotheses, and considerations for crafting hypotheses in your research. Lauren Stewart Qualitative Data Analysis ...
The story of a research study begins by asking a question. Researchers all around the globe are asking curious questions and formulating research hypothesis. However, whether the research study provides an effective conclusion depends on how well one develops a good research hypothesis. Research hypothesis examples could help researchers get an idea as to how to write a good research hypothesis.
A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question. A hypothesis is not just a guess — it should be based on ...
Essentially, a hypothesis is a tentative statement that predicts the relationship between two or more variables in a research study. It is usually derived from a theoretical framework or previous ...
Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is most often used by scientists to test specific predictions, called hypotheses, that arise from theories.
A research hypothesis provides a clear, testable statement that guides the direction and focus of a study. The benefit is that the hypothesis makes selecting appropriate research methods or statistical means possible, making the analysis more effective and achieving a result. Above all, the idea selected for the research also makes the study ...
An effective hypothesis in research is clearly and concisely written, and any terms or definitions clarified and defined. Specific language must also be used to avoid any generalities or assumptions. Use the following points as a checklist to evaluate the effectiveness of your research hypothesis: Predicts the relationship and outcome.
An hypothesis is a specific statement of prediction. It describes in concrete (rather than theoretical) terms what you expect will happen in your study. Not all studies have hypotheses. Sometimes a study is designed to be exploratory (see inductive research ). There is no formal hypothesis, and perhaps the purpose of the study is to explore ...
Though you may hear the terms "theory" and "hypothesis" used interchangeably, these two scientific terms have drastically different meanings in the world of science.
Hypothesis is a prediction of the outcome of a study. Hypotheses are drawn from theories and research questions or from direct observations. In fact, a research problem can be formulated as a hypothesis. To test the hypothesis we need to formulate it in terms that can actually be analysed with statistical tools.
A hypothesis is a prediction of what will be found at the outcome of a research project and is typically focused on the relationship between two different variables studied in the research. It is usually based on both theoretical expectations about how things work and already existing scientific evidence. Within social science, a hypothesis can ...
In this article, we will learn what is hypothesis, its characteristics, types, and examples. We will also learn how hypothesis helps in scientific research.
This list of quiz questions based on Statistical Methods for Research Workers will test your knowledge of Ronald Fisher's groundbreaking approach to data analysis
The student title page includes the paper title, author names (the byline), author affiliation, course number and name for which the paper is being submitted, instructor name, assignment due date, and page number, as shown in this example.
Discover the fundamentals of hypothesis testing in statistics, its various types, and real-world examples to develop data-driven strategies.
Using the alternating direction multiplier method and the loss of the locally linear embedding method for the model training function, the proposed pruning method prunes the operation model mainly by evaluating the similarity of each layer in the network layer filters. ... this paper proves the hypothesis through experiments that "using the ...
Download Citation | Using Real Options and Geometric Brownian Motion Methods to Evaluate Petroleum Projects in Indonesia | There are several methods for evaluating the value of a project. The most ...