Hypothesis Generator
Enter your independent and dependent variables and get your hypothesis in the four standard research formats — if/then, directional, non-directional, and null (H0/H1). The tool slots your variables into guided frames; the scientific thinking is still yours. No AI involved.
Predicted direction of the dependent variable
What makes a hypothesis testable
Both variables must be measurable
Replace vague terms with things you can count or score. 'Better focus' is not measurable; 'score on a 10-minute attention task' is. If you cannot name the instrument or unit, the hypothesis is not ready to test.
It must be falsifiable
A good hypothesis can be proven wrong by data. If no possible result would count against your claim, it is not a scientific hypothesis — it is an opinion. The null hypothesis (H0) exists precisely so your test has something to reject.
Define the population
An effect found in college students may not hold for children or older adults. Naming the population keeps your claim honest about who your results apply to, and tells readers how far the findings generalize.
Change one variable at a time
If you manipulate two things at once, you cannot tell which caused the effect. Keep one independent variable per hypothesis and hold everything else constant — that is what makes the comparison fair.
Frequently Asked Questions
- What is the difference between the independent and dependent variable?
- The independent variable is what you change or manipulate in the study — the suspected cause. The dependent variable is what you measure to see the effect. In 'Does sleep affect test scores?', hours of sleep is the independent variable and test score is the dependent variable.
- What are H0 and H1?
- H0 is the null hypothesis: it states there is no effect or no relationship between your variables, and it is what statistical tests actually test against. H1 (the alternative hypothesis) states there is an effect. Research never proves H1 directly — it collects evidence that lets you reject or fail to reject H0.
- What is the difference between a directional and non-directional hypothesis?
- A directional hypothesis predicts which way the effect goes — for example, that scores will increase. A non-directional hypothesis predicts only that a relationship exists, without specifying the direction. Use a directional hypothesis when prior research gives you a reason to expect a particular direction; otherwise, non-directional is the safer claim.
- What makes a hypothesis testable?
- A testable hypothesis names variables you can actually measure, applies to a defined population, and could be proven wrong by data — that is, it is falsifiable. 'Listening to music while studying reduces recall accuracy in college students' is testable; 'music is bad for studying' is not, because 'bad' is not measurable.
- Does this tool use AI to write my hypothesis?
- No. It slots your variables into the standard hypothesis formats used in research methods courses — if/then, directional, non-directional, and null. The scientific thinking is still yours: the tool only handles the phrasing conventions, and you should refine the wording so each variable is precisely defined.