| name | hypothesis-framing |
| description | Write or refine a research hypothesis. Use when the user says "write a hypothesis", "frame my hypothesis", "is my hypothesis testable", "null and alternative hypothesis", "research hypothesis", "refine my hypothesis", "hypothesis statement", "H0 and H1", "operationalize my hypothesis", "turn my research question into a hypothesis", or needs to convert a research question or intuition into a falsifiable, testable hypothesis with clear variables and predictions - even if they don't explicitly say "hypothesis".
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Overview
Based on Research Design (Creswell & Creswell) and Research Methodology: A Step-by-Step Guide (Ranjit Kumar). A hypothesis is a falsifiable prediction about the relationship between variables. Kumar's rule: a hypothesis that cannot be proven wrong is not a hypothesis - it is a belief. Creswell's standard: every hypothesis must specify the population, the variables, and the predicted direction of the relationship.
The test: can a different researcher, given only the hypothesis, design a study to test it?
Workflow
Step 1: Identify the research question
Start with the question before writing the hypothesis. The hypothesis is the answer you predict the study will find.
Research question: [what are you trying to find out?]
Domain: [field of study]
Context: [setting, population, conditions]
Prior evidence: [what makes this prediction plausible? what is it based on?]
A good research question has:
- A specific population (not "people" but "adults over 65 with Type 2 diabetes")
- A specific phenomenon or variable being studied
- A clear "compared to what" or "under what conditions"
Step 2: Identify and operationalize variables
Variables must be measurable. For each variable, define how it is measured:
Independent variable (IV): [what you manipulate or group by]
Operationalization: [how exactly it is measured or defined]
Levels/values: [if categorical: list levels. if continuous: range and units]
Dependent variable (DV): [what you measure as an outcome]
Operationalization: [specific measure, scale, or instrument]
Units: [e.g. seconds, score 1-7 on Likert scale, binary yes/no]
Control variables: [variables held constant or statistically controlled]
[variable]: [how controlled]
Potential confounds: [variables that could explain results without being your IV]
[confound]: [how you plan to address it]
Step 3: Write the directional hypothesis
A well-formed hypothesis has three components:
- Population: who or what
- Predicted relationship: direction and nature
- Variables: IV and DV named precisely
Format: "In [population], [IV manipulation or condition] will result in [predicted change in DV direction] compared to [control or comparison condition]."
Examples:
- "Participants who receive spaced-repetition training will score significantly higher on a 30-item vocabulary test at 2-week follow-up than participants who receive massed-practice training."
- "Remote engineering teams using structured async standup tools will report lower meeting fatigue (measured by the Meeting Load Index) than teams using synchronous daily standups."
Step 4: Write null and alternative hypotheses
H₀ (Null hypothesis): There is no significant difference/relationship between [IV] and [DV]
in [population].
H₁ (Alternative hypothesis - directional): [IV] is associated with [higher/lower/more/less]
[DV] in [population].
Or (non-directional):
H₁ (Alternative hypothesis): There is a significant difference in [DV] between [IV
conditions] in [population].
Use directional H₁ when: prior theory or evidence strongly predicts the direction
Use non-directional H₁ when: this is exploratory or direction is genuinely uncertain
Step 5: State the theoretical basis
Explain what mechanism you expect to be driving the predicted effect:
Theoretical basis: [What theory or prior finding predicts this result?]
Mechanism: [Why would X cause Y? What is the causal pathway?]
Prior evidence: [1-3 citations showing related findings that support this prediction]
Boundary conditions: [Under what conditions do you expect this to hold? Under what conditions might it not?]
Step 6: Check falsifiability and precision
Run through these questions before finalizing:
- Falsifiable: What result would prove this hypothesis wrong? (If you can't answer, it's not falsifiable)
- Specific: Does it name the population, IV, DV, and direction?
- Testable: Can the variables be measured with available instruments?
- Grounded: Is there a theoretical or empirical reason to predict this relationship?
- Appropriately scoped: Is it narrow enough to test in one study?
If any answer is "no" or "I'm not sure," revise before proceeding to study design.
Anti-Patterns
1. Research question disguised as hypothesis
Bad: "Does social media use affect mental health?"
Good: "Adolescents aged 13-17 who use social media for more than 3 hours/day will report significantly higher levels of anxiety (GAD-7 scale) than those who use social media for less than 1 hour/day."
2. Unmeasurable variables
Bad: "Students who feel more motivated will learn better."
Good: "Students in the gamified learning condition (IV) will score higher on the Unit 4 end-of-module test (DV, 20 items, % correct) than students in the standard condition."
3. No stated direction
Bad: "There will be a relationship between exercise and mood."
Good: "Adults who engage in 30 minutes of aerobic exercise 5 days per week will report lower depressive symptoms (PHQ-9) than sedentary controls after 8 weeks."
4. Hypothesis without mechanism
Bad: Hypothesis states a prediction with no theory behind it.
Good: Include the theoretical mechanism (e.g., cognitive load theory predicts..., self-determination theory suggests...) so reviewers can evaluate the rationale.
Quality Checklist
