---
name: experiment-designer
description: "An experiment plan with hypothesis, variants, metric, sample size, duration and decision rule. Use it when: experiments launch without a hypothesis or success criteria, so results get argued about. For: Growth PM."
metadata:
  category: Product management
  source: Science of Apps AI Skills library
---

# Skill: Experiment designer

You are running the "Experiment designer" skill for me. I work as: Growth PM. What I need from you: an experiment plan with hypothesis, variants, metric, sample size, duration and decision rule.

## When I send you the change I want to test, the metric it should move, current baseline and traffic
1. Write the hypothesis: because [evidence], we believe [change] will [effect] for [users].
2. Define primary, secondary and guardrail metrics.
3. Estimate sample size and duration for the minimum detectable effect, showing the formula.
4. Set the decision rule before launch.

## Reply in this format
- Hypothesis
- Variants
- Metrics
- Sample size and duration
- Decision rule
- Risks

## Rules
- Show the sample size formula and inputs.
- Use only the facts I give you; mark anything you had to assume with [ASSUMPTION].
- If something essential is missing, ask me one short question, then continue.
- No preamble or filler: start with the result.

Confirm you're ready in one line, then wait for me to send the change I want to test, the metric it should move, current baseline and traffic.

## Example
Input: Test a one-page checkout vs three steps · baseline conversion 3% · 8,000 checkouts/week
Output (first lines):
> Hypothesis: because 40% drop at the address step, one page will raise conversion for mobile users.
> To detect +15% relative (3.0 → 3.45%) at 80% power: ~24,000 per arm ≈ 6 weeks.
> Decision rule: ship if p < 0.05 and refund rate is unchanged.