--- name: product-on-purpose/define-hypothesis source: https://app.decimal.ai/s/product-on-purpose-define-hypothesis@1/SKILL.md source_sha256: f7cfcba741c7 --- # Hypothesis A hypothesis is a testable prediction about how a change will affect user behavior or business outcomes. It transforms assumptions into explicit statements that can be validated or invalidated through experimentation. Well-formed hypotheses prevent teams from building features based on untested beliefs and create shared understanding of what success looks like. ## When to Use - After problem framing, before committing to a solution - When designing experiments or A/B tests - When team members have differing assumptions about user behavior - Before investing significant engineering resources in a feature - When pivoting direction and need to validate the new approach ## When NOT to Use - You are ready to design the actual A/B test (variants, sample size, duration) -> use `measure-experiment-design`; this skill frames what to test, not how - The problem itself is still unframed -> use `define-problem-statement` first - You want to organize many assumptions and ideas into a discovery structure -> use `define-opportunity-tree` - The team needs the full business-model picture, not one testable claim -> use `foundation-lean-canvas` ## Instructions When asked to create a hypothesis, follow these steps: 1. **State the Belief** Articulate what you believe will happen. Use the structured format: "We believe that [action/change] for [target user] will [expected outcome]." Be specific about the intervention - vague hypotheses can't be tested. 2. **Identify the Target User** Define who this hypothesis applies to. A hypothesis about "users" is too broad. Specify the segment: new users in their first week, power users with 10+ sessions, churned users returning, etc. 3. **Define the Expected Outcome** What behavior change or result do you expect? Frame it in terms of user actions (complete onboarding, make a purchase, return within 7 days) rather than internal metrics when possible. 4. **Set Success Metrics** Choose a primary metric that directly measures the expected outcome. Include secondary metrics that provide context and guardrail metrics that ensure you're not causing harm elsewhere. 5. **Describe Validation Approach** How will you test this hypothesis? A/B test, user interviews, prototype testing, cohort analysis? Be specific about sample size, duration, and statistical requirements. 6. **Document Risks and Assumptions** What could invalidate this hypothesis beyond the test results? What are you assuming to be true that you haven't validated? ## Output Format Use the template in `references/TEMPLATE.md` to structure the output. A complete hypothesis document fills every template section: Hypothesis Statement; Background & Rationale; Target User Segment; Success Metrics; Validation Approach; Risks & Assumptions; and Timeline. ## Quality Checklist Before finalizing, verify: - [ ] Hypothesis is falsifiable (possible to prove wrong) - [ ] Success metric has a specific numeric target - [ ] Target user segment is clearly defined - [ ] Validation approach is practical and time-bound - [ ] Pass/fail criteria are unambiguous - [ ] Hypothesis doesn't assume the solution works ## Examples See `references/EXAMPLE.md` for a completed example.