What is a hypothesis? A hypothesis is a statement that explains the predictions and logic of your study—a “educated guess” about how your scientific research will come out. Hypotheses must be tested. Hypothesis testing, or just “testing a hypothesis,” involves an experiment that may either prove or disprove the accuracy of your hypothesis.
What is a Hypothesis?
A hypothesis is a testable “educated guess.” Hypotheses are derived from prior relevant research and experience, but must be considered tentative for they are subject to change or refutation with the acquisition of new knowledge. Hypotheses are not “facts.” Hypothesis testing (or hypothesis validation or falsification) refers to experiments designed to test hypotheses. Hypothesis testing involves an experiment that either proves the hypothesis correct, if possible, or disproves it, showing that it is wrong and false; this forms part of scientific method .
How Do You Write a Hypothesis?
A Hypothesis is a short statement of what you think your research project will prove. It is stated as the opposite of what you are testing for or as an “if-then” statement. Hypotheses must be experimentally testable by the methods stated in the experimental design section. A Hypothesis has three parts:
1) The first word should always be capitalized and underlined, Hypothesis .
2) Hypotheses should not contain acronyms or abbreviations unless they are well known to the scientific community.
3) Hypotheses should contain a single idea that can be tested between subjects (between groups), repeated measures, or matched pairs designs.
Types of Hypothesis
There are three types of Hypotheses:
Hypotheses (fill in the blank for what Hypothesis is trying to prove), Alternative Hypotheses (two Hypotheses that are both trying to be proved by your experiment), and Null Hypotheses (this states what you think will happen). You must state two or more Hypotheses for an experiment.
Procedure of Hypothesis Testing
Hypothesis testing is used to test theories, accounts, explanations, predictions, statements about reality against empirical data. This process involves making systematic observations under controlled conditions, documenting your observations quantitatively. We then compare these numerical results with theoretical probabilities or expectations that are based on our hypotheses or other past research findings. Hypothesis testing includes several steps:
1) write what you think the hypothesis is.
2) Determine probability or chance of sample result occurring by chance.
3) Calculate probability using an alpha level, the p value, or a critical t score.
4) State your decision about whether or not to reject H0 based on your calculated odds.
5) Repeat experiment with independent data samples.
6) Make additional statistical comparisons until all relevant hypotheses are tested.
7) Place results in appropriate context for different applications including theory building and practical significance.
Good Read: What Is Direct Characterization
Tips to Write Hypothesis
* Hypotheses must be experimentally testable by the methods stated in your experimental design section.
* Hypotheses contain a single idea that can be tested between subjects (between groups), repeated measures, or matched pairs.
* Hypotheses should begin with a capital letter and be underlined.
* Hypotheses should contain no acronyms or abbreviations unless they are well known to the scientific community.
Do’s and Don’ts of Hypothesis
1) Hypothesize what you think will happen in your experiment and record it as an “if-then” statement. For example: If we rearrange the items on this desk, then we will see less clutter and find more important documents. 2) Be specific about what you believe will happen in your study by stating that there is a difference or no difference between two groups (e.g., bald men will have bald wives). 3) Hypothesize differences between groups of people, things, and ideas. 4) Hypothesize what you think the outcome will be if two variables are related.
1) State a hypothesis in the form of “I believe…” or “I hope…” 2) Make claims about the world in general (e.g., children learn faster than adults). 3) Hypothesize too many results for your study. 4) Hypothesize only one single result-you should always need to compare your results with another accepted truth from previous research. 5) Have more than three hypotheses-if there is more than one hypothesis, list them all but change variables around so that they are independent from one another.
Validity, Reliability, and Hypothesis Testing
Validity is the extent to which you can trust what you are measuring in your study. For instance, if you design an experiment to see how changes in diet affect weight loss, but then stop taking measurements after eight weeks because participants were “sick” of coming into your office every week – this would not be a very reliable measure of weight loss!
Reliability refers to whether or not the same result occurs when someone repeats your experiment. If they do it again exactly as before, they should get the same answer over and over again-this is reliability! Hypothesis testing allows us to make accurate predictions about how our experiments will turn out before we collect data. Hypothesis testing also helps us determine when our results might be due to chance and requires us to conduct follow up experiments using the same methods and procedures with different participants in order to increase validity and reliability of findings.
If done correctly, Hypothesis Testing will help you design a more accurate experiment in the future! It will also help you know how your conclusions can be trusted by others in the research community.
Examples of Hypotheses
1) If I increase my aerobic activity (e.g., running), then I will lose weight because my body stores excess fat as reserves for times when I am not eating enough calories (calorie deficit).
2) If students study with music on, they will have a lower test score than those who study without music on
3) If I teach my children to say please and thank you, they will have better social skills as adults
4) If women are more selective about their romantic partners, then they will value physical attractiveness less for short-term vs. long-term relationships.
5) There is no difference in the performance of men and women on math tests when they are given time to study for it after being divided into groups by ability level during an experimental session where stress levels were manipulated.