9 added, 2 removed. Audit A to A.
---
name: define-hypothesis
description: Defines a testable hypothesis with clear success metrics and a validation approach. Use when forming assumptions to test or aligning a team on what success looks like, before any experiment is designed. To design the A/B test or experiment that will validate the hypothesis, use measure-experiment-design.
license: Apache-2.0
metadata:
phase: define
- version: "2.0.1"
+ version: "2.1.0"
updated: 2026-06-10
category: ideation
frameworks: [triple-diamond, lean-startup, design-thinking]
author: product-on-purpose
---
<!-- PM-Skills | https://github.com/product-on-purpose/pm-skills | Apache 2.0 -->
# 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.
+ 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.