What is a hypothesis?

A hypothesis is a concise, testable statement that predicts a relationship between variables. In empirical research it guides study design, data collection, and analysis — in short, it’s the foundation of a scientific argument. A good hypothesis is specific, measurable, and falsifiable.

Types of hypotheses (quick overview)

Step-by-step process to write your hypothesis

1. Identify the research question. Turn your topic into a clear question: “Does X affect Y?”
2. Define variables. Specify independent (predictor) and dependent (outcome) variables and how you will measure them.
3. Choose hypothesis type. Decide whether you need a null/alternative pair and whether the prediction should be directional.
4. Draft the hypothesis. Write a concise statement that links variables and predicts a relationship.
5. Make it testable and falsifiable. Ensure you can measure the variables and that data could in principle disprove the hypothesis.
6. Refine with context. Add scope (population, conditions, timeframe) so the hypothesis is neither too broad nor too narrow.

How to structure hypotheses (templates)

Null hypothesis (H0): There is no difference in [dependent variable] between [group A] and [group B].  
Alternative (directional): [Group A] will have higher [dependent variable] than [Group B].  
Non-directional: There will be a difference in [dependent variable] between [group A] and [group B].

Templates keep your statements consistent with statistical testing conventions.

Examples — experimental & observational

H0: There will be no difference in test scores between students who use active-retrieval practice and students who use passive review.  
HA: Students who use active-retrieval practice will score higher on the test than students who use passive review.

H0: There is no association between daily screen time and self-reported sleep quality among adolescents.  
HA: There is an association between daily screen time and self-reported sleep quality among adolescents.

H0: There is no correlation between household income and frequency of preventive health visits.  
HA: Household income is positively correlated with frequency of preventive health visits.

Operationalize your variables

Operationalization turns abstract concepts into measurable items. If your dependent variable is “stress,” decide whether you’ll use cortisol levels, a validated questionnaire score, or time-to-task-failure. Clear operational definitions make your hypothesis testable and your results interpretable.

Common mistakes and how to avoid them

• Too vague: “Technology affects learning” → specify which technology, which learning outcome, and how you measure it.
• Not testable: Avoid purely philosophical claims that cannot be measured.
• Confusing correlation with causation: Use experimental design to support causal claims or be explicit that your claim is associative.
• Overly complex hypotheses: A single clear prediction is better than a paragraph of intertwined claims.

Tips for stronger hypotheses

1. Be concise — one sentence is ideal.
2. Specify population and context (e.g., “among first-year college students”).
3. Link to theory or prior findings to justify the prediction.
4. Prepare the null hypothesis explicitly if you’ll run statistical tests.
5. Pre-register your hypothesis for transparency in confirmatory research.

Quick checklist before you finalize

• Is the hypothesis clear and specific?
• Are variables defined and measurable?
• Is it falsifiable — could data disprove it?
• Does it match your study design (experimental vs observational)?
• Is the scope appropriate for the paper?