define-hypothesis

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Defines a testable hypothesis with clear success metrics and validation approach. Use when forming assumptions to test, designing experiments, or aligning team on what success looks like.

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Sourceproduct-on-purpose/pm-skills

Added on2026-04-29

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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

Instructions

When asked to create a hypothesis, follow these steps:

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.
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.
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.
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.
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.
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.

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.