Frame an epic as a testable hypothesis with target user, expected outcome, and validation method. Use when defining a major initiative before roadmap, discovery, or delivery planning.
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Frame an epic as a testable hypothesis with target user, expected outcome, and validation method. Use when defining a major initiative before roadmap, discovery, or delivery planning.
intent
Frame epics as testable hypotheses using an if/then structure that articulates the action or solution, the target beneficiary, the expected outcome, and how you'll validate success. Use this to manage uncertainty in product development by making assumptions explicit, defining lightweight experiments ("tiny acts of discovery"), and establishing measurable success criteria before committing to full build-out.
type
component
theme
pm-artifacts
best_for
["Framing a major initiative as something that can be proven wrong","Forcing an outcome and a validation method before build starts","Giving a team a shared bet to test instead of a scope to deliver"]
scenarios
["Leadership approved a big initiative and nobody can say what would prove it wrong","I need to frame this epic as a bet with a real validation method, not a delivery plan"]
estimated_time
15-20 min
Purpose
Frame epics as testable hypotheses using an if/then structure that articulates the action or solution, the target beneficiary, the expected outcome, and how you'll validate success. Use this to manage uncertainty in product development by making assumptions explicit, defining lightweight experiments ("tiny acts of discovery"), and establishing measurable success criteria before committing to full build-out.
This is not a requirements spec—it's a hypothesis you're testing, not a feature you're committed to shipping.
Input
Works best with: The initiative or epic idea, in whatever form it currently exists — a sentence is enough.
Also useful: The target user, the outcome you expect, and how you might measure it (the skill helps you sharpen all three).
Anything supplied with the invocation itself — text after the skill name, a pasted context dump, or an appended ARGUMENTS: line — counts as answers already given. Use it and skip whatever it covers; don't re-ask.
Arriving empty-handed? That works too. The skill asks what the initiative is and who it's for, then builds the if/then hypothesis with you.
Example invocation:Frame as an epic hypothesis: adding usage-based alerts so account admins catch overages before invoice shock.
Key Concepts
The Epic Hypothesis Framework
Inspired by Tim Herbig's Lean UX hypothesis format, the structure is:
If/Then Hypothesis:
If we [action or solution on behalf of target persona]
for [target persona]
Then we will [attain or achieve a desirable outcome or job-to-be-done]
Tiny Acts of Discovery Experiments:
We will test our assumption by:
[Experiment 1]
[Experiment 2]
[Add more as necessary]
Validation Measures:
We know our hypothesis is valid if within [timeframe]
we observe:
[Quantitative measurable outcome]
[Qualitative measurable outcome]
[Add more as necessary]
Why This Structure Works
Hypothesis-driven: Forces you to state what you believe (and could be wrong about)
Outcome-focused: "Then we will" emphasizes user benefit, not feature output
Experiment-first: Encourages lightweight validation before full build
Falsifiable: Clear success criteria make it possible to kill bad ideas early
Risk management: Treats epics as bets, not commitments
Anti-Patterns (What This Is NOT)
Not a feature spec: "Build a dashboard with 5 charts" is a feature, not a hypothesis
Not a guaranteed commitment: Hypotheses can (and should) be invalidated
Not output-focused: "Ship feature X by Q2" misses the point—did it achieve the outcome?
Not experiment-free: If you skip experiments and go straight to build, you're not testing a hypothesis
When to Use This
Early-stage feature exploration (before committing to full roadmap)
Validating product-market fit for new capabilities
Prioritizing backlog (epics with validated hypotheses get higher priority)
Managing stakeholder expectations (frame work as experiments, not promises)
When NOT to Use This
For well-validated features (if you've already proven demand, skip straight to user stories)
For trivial features (don't over-engineer small tweaks)
When experiments aren't feasible (rare, but sometimes you must commit before testing)
Application
Use template.md for the full fill-in structure.
Step 1: Gather Context
Before drafting an epic hypothesis, ensure you have:
Problem understanding: What user problem does this address? (reference skills/problem-statement/SKILL.md)
Target persona: Who benefits? (reference skills/proto-persona/SKILL.md)
Jobs-to-be-Done: What outcome are they trying to achieve? (reference skills/jobs-to-be-done/SKILL.md)
Current alternatives: What do users do today? (competitors, workarounds, doing nothing)
If missing context: Run discovery interviews or problem validation work first.
Step 2: Draft the If/Then Hypothesis
Fill in the template:
### If/Then Hypothesis**If we** [action or solution on behalf of the target persona]
**for** [target persona]
**Then we will** [attain or achieve a desirable outcome or job-to-be-done for the persona]
Quality checks:
"If we" is specific: Not "improve the product" but "add one-click Slack notifications when tasks are assigned"
"For" is a clear persona: Not "users" but "remote project managers juggling 3+ distributed teams" (reference skills/proto-persona/SKILL.md)
"Then we will" is an outcome: Not "users will have notifications" but "users will respond to task assignments 50% faster"
Examples:
✅ "If we add one-click Google Calendar integration for trial users, then we will increase activation rates by 20% within 30 days"
✅ "If we provide bulk delete functionality for power users managing 1000+ items, then we will reduce time spent on cleanup tasks by 70%"
❌ "If we build a dashboard, then users will use it" (vague, not measurable)
Step 3: Design Tiny Acts of Discovery Experiments
Before building the full epic, define lightweight experiments to test the hypothesis:
### Tiny Acts of Discovery Experiments**We will test our assumption by:**- [Experiment 1: low-cost, fast test]
- [Experiment 2: another low-cost, fast test]
- [Add more as necessary]
Experiment types:
Prototype + user testing: Fake the feature with a clickable prototype, test with 5-10 users
Concierge test: Manually perform the feature for a few users, see if they value it
Landing page test: Describe the feature, measure sign-ups or interest
Wizard of Oz test: Present the feature as if it's automated, but do it manually behind the scenes
A/B test (if feasible): Test a lightweight version vs. control
Quality checks:
Fast: Experiments should take days/weeks, not months
Cheap: Avoid full engineering builds—use prototypes, manual processes, or existing tools
Falsifiable: Design experiments that could prove you wrong
Examples:
"Create a Figma prototype of the bulk delete flow and test with 5 power users"
"Manually send Slack notifications to 10 trial users and track response time"
"Add a 'Request this feature' button to the UI and measure click-through rate"
Step 4: Define Validation Measures
Specify what success looks like and the timeframe for evaluation:
### Validation Measures**We know our hypothesis is valid if within** [timeframe in days or weeks]
**we observe:**- [Desirable quantitative, measurable outcome]
- [Desirable qualitative, measurable outcome]
- [Add more as necessary]
Quality checks:
Timeframe is realistic: Not "within 6 months" (too slow) or "within 3 days" (too fast)
Quantitative measures are specific: Not "more users" but "20% increase in activation rate"
Qualitative measures are observable: Not "users like it" but "8 out of 10 users say they'd pay for this feature"
Examples:
✅ "Within 4 weeks, we observe:"
"Activation rate increases from 40% to 50% (quantitative)"
"75% of surveyed trial users say the integration saved them time (qualitative)"
❌ "Within 1 year, we observe:"
"Revenue goes up" (too vague, too long)
Step 5: Run Experiments and Evaluate
Execute experiments: Build prototypes, run tests, gather data
Measure results: Did you hit the validation measures?
Decision point:
✅ Hypothesis validated: Proceed to building user stories and adding to roadmap
❌ Hypothesis invalidated: Kill the epic or pivot to a different hypothesis
⚠️ Inconclusive: Run additional experiments or tighten validation measures
Step 6: Convert to User Stories (If Validated)
Once the hypothesis is validated, break the epic into user stories:
### Epic: [Epic Name]**Stories:**1. [User Story 1 - reference `skills/user-story/SKILL.md`]
2. [User Story 2]
3. [User Story 3]
Examples
See examples/sample.md for full epic hypothesis examples.
Mini example excerpt:
**If we** provide one-click Google Calendar integration
**for** trial users managing multiple meetings
**Then we will** increase activation rate from 40% to 50%
Common Pitfalls
Pitfall 1: Hypothesis is a Feature, Not an Outcome
Symptom: "If we build a dashboard, then we will have a dashboard"
Consequence: You're describing output, not outcome. This doesn't test anything.
Fix: Focus on the user outcome: "If we build a dashboard showing real-time task status, then PMs will spend 50% less time asking for status updates."
Pitfall 2: Skipping Experiments
Symptom: "We'll test our assumption by building the full feature"
Consequence: You've committed to building before validating. Not a hypothesis—it's a feature commitment.
Fix: Design lightweight experiments (prototypes, concierge tests, landing pages) that take days/weeks, not months.
Pitfall 3: Vague Validation Measures
Symptom: "We know it's valid if users are happy"
Consequence: Success criteria are subjective and unmeasurable.
Fix: Define specific, falsifiable metrics: "80% of surveyed users rate the feature 4+ out of 5" or "Response time drops by 50%."
Pitfall 4: Unrealistic Timeframes
Symptom: "We know it's valid if within 6 months revenue increases"
Consequence: Too slow to inform decisions. By then, you've already built it.
Fix: Aim for 2-4 week validation cycles. If you can't measure in that timeframe, choose a leading indicator (e.g., activation rate, not annual revenue).
Pitfall 5: Treating Epics as Commitments
Symptom: "We already told the CEO we're shipping this, so we have to validate it"
Consequence: Experiments are theater—you're going to build it regardless of results.
Fix: Frame epics as hypotheses before making commitments. If stakeholders need certainty, explain the risk of building unvalidated features.
References
Related Skills
skills/problem-statement/SKILL.md — Hypothesis should address a validated problem
skills/proto-persona/SKILL.md — Defines the "for [persona]" section
skills/jobs-to-be-done/SKILL.md — Informs the "then we will" outcome
skills/user-story/SKILL.md — Validated epics decompose into user stories
skills/user-story-splitting/SKILL.md — How to break validated epics into stories
External Frameworks
Tim Herbig, Lean UX Hypothesis Statement — Origin of if/then hypothesis format
Jeff Gothelf & Josh Seiden, Lean UX (2013) — Hypothesis-driven product development
Alberto Savoia, Pretotype It (2011) — Lightweight experiments to validate ideas
Eric Ries, The Lean Startup (2011) — Build-Measure-Learn cycle
Dean's Work
Backlog Epic Hypothesis Prompt (inspired by Tim Herbig's framework)
Provenance
Adapted from prompts/backlog-epic-hypothesis.md in the https://github.com/deanpeters/product-manager-prompts repo.
