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Hypothesis Statement Prompt Template

Write testable hypotheses for product features, marketing campaigns, or scientific research in proper if-then-because format.

The Prompt

ROLE: Research strategist and experimental design specialist — you know that the quality of what you learn from an experiment is entirely determined by the clarity of the hypothesis you start with, and that a vague hypothesis produces ambiguous results that support whatever conclusion you were already inclined to draw. CONTEXT: Hypotheses are not guesses — they are falsifiable predictions grounded in a specific mechanism. A well-formed hypothesis specifies: what change is being made, what outcome is predicted, the causal mechanism (why this should produce that outcome), how the outcome will be measured, and what result would count as disconfirmation. Without all five elements, you're not running an experiment, you're running an observation. TASK: Write 5 testable hypotheses for the problem or opportunity below, formatted with all required elements for rigorous testing. RULES: • Each hypothesis must follow the structure: "If [specific action], then [specific measurable outcome], because [causal mechanism]" • The causal mechanism ("because") is the most important element — it's the theory that the test will either support or refute • Every hypothesis must include: the primary metric, the minimum detectable effect (smallest change worth caring about), sample size estimate, and recommended test method • At least one hypothesis must be a "null hypothesis" style — predicting that a change will NOT produce an effect — to guard against confirmation bias • Hypotheses must be ranked by expected learning value, not expected chance of being right CONSTRAINTS: Metrics must be specific and measurable — "engagement" is not a metric; "average session duration on the pricing page" is. Do not write hypotheses designed to confirm existing beliefs

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Why this prompt works

The explicit null hypothesis (what would count as disconfirmation) is the most important element that standard hypothesis templates omit — without it, teams unconsciously design tests they can interpret as confirmation regardless of outcome. Ranking hypotheses by learning value rather than likelihood of being correct is the discipline that prevents experimentation from becoming confirmation theatre.

Tips for best results

  • Write the analysis plan before running the test — deciding in advance exactly what result will count as 'confirmed' is the best protection against p-hacking and motivated interpretation
  • The minimum detectable effect is the most technically important input — too small an MDE requires impossibly large samples; too large an MDE means you'll miss real effects
  • Run hypotheses in parallel where possible — sequential testing in product development is too slow; prioritise independent hypotheses that can run simultaneously
  • After each test, write a one-page 'what we learned' document whether the hypothesis was confirmed or not — refuted hypotheses teach as much as confirmed ones

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