| name | experiment-designer |
| description | Help PMs design rigorous experiments (A/B tests, feature rollouts). Use before any A/B test, feature experiment, pricing test, or phased rollout. |
Experiment Designer
Help PMs design rigorous experiments with clear hypotheses, variants, metrics, and decision criteria. Ensures experiments are statistically sound and actionable.
When to Use
- Before any A/B test
- Feature experiment design
- Pricing or packaging test
- Phased rollout planning
- When asked "how do we test this?"
- Before setting up an experiment in an experimentation platform
Process / Template
1. Capture the Hypothesis
Use format: "We believe [action] will [outcome] for [segment] because [rationale]"
- Action — what you're changing (feature, copy, flow)
- Outcome — expected metric movement
- Segment — who you're testing with
- Rationale — why you believe this (research, prior data)
2. Define Control vs. Treatment
- Control — baseline experience (no change)
- Treatment — the change being tested
- Be explicit about what differs between variants
3. Define Success Metrics
- Primary metric — main success signal (one metric)
- Secondary metrics — supporting signals
- Guardrail metrics — must not regress (e.g., revenue, retention)
4. Determine Experiment Type
- A/B test — two variants, single change
- Multivariate — multiple factors
- Phased rollout — gradual % rollout
- Holdout — long-term impact test
5. Calculate Sample Size and Duration
- Power — typically 80%
- MDE (Minimum Detectable Effect) — smallest effect you care about
- Traffic — daily eligible users
- Duration — run until sufficient sample size
6. Define Decision Criteria
| Result | Action |
|---|
| Primary metric improves, guardrails hold | Ship |
| Primary metric flat, secondary improves | Iterate (refine and re-test) |
| Primary metric declines | Kill |
| Inconclusive (low power) | Extend or increase traffic |
7. Document Risks
- What could invalidate results? (seasonality, external events)
- Confounding factors?
- Technical implementation risks?
Output
An Experiment Design Doc with:
| Section | Content |
|---|
| Hypothesis | Full hypothesis statement |
| Variants | Control and treatment definitions |
| Primary Metric | Main success metric |
| Secondary Metrics | Supporting metrics |
| Guardrail Metrics | Metrics that must not regress |
| Sample Size | Required sample per variant |
| Duration | Estimated run length |
| Decision Criteria | Ship / iterate / kill thresholds |
| Risks | What could invalidate results |
