mohitagw15856
96 lines
3.9 KiB
Markdown
96 lines
3.9 KiB
Markdown
---
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name: ab-test-planner
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description: Designs statistically rigorous A/B tests for product features, UI changes, onboarding flows, and pricing experiments. Use when asked to set up an experiment, run an A/B test, calculate sample size, or interpret test results. Triggers on "A/B test", "experiment", "split test", "statistical significance", "sample size".
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---
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# A/B Test Planner Skill
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Design experiments that produce trustworthy results — not just directional signals. Every test output includes hypothesis, success metrics, sample size, duration, and a results interpretation guide.
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## Experiment Design Checklist
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Before running any test, confirm:
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- [ ] Clear hypothesis with predicted direction
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- [ ] Single primary metric (plus up to 2 guardrail metrics)
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- [ ] Minimum detectable effect (MDE) defined
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- [ ] Sample size calculated
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- [ ] Test duration estimated
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- [ ] Segment isolated (no overlap with other running tests)
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- [ ] Rollback plan defined
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## Hypothesis Template
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> "We believe that [change] will cause [primary metric] to [increase/decrease] by [X%] for [user segment], because [rationale based on data or insight]."
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Never run a test without a directional hypothesis. "Let's just see what happens" is not a hypothesis.
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## Sample Size Calculator Logic
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Use this formula (provide the output, not the formula, to the user):
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- **Baseline conversion rate:** Current rate of primary metric
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- **MDE:** Smallest change worth detecting (recommend 10–20% relative lift for most features)
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- **Statistical power:** 80% (standard)
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- **Significance level:** 95% (p < 0.05)
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For common scenarios, provide pre-calculated estimates:
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| Baseline Rate | MDE (Relative) | Required Sample per Variant |
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|---|---|---|
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| 5% | 20% | ~19,000 |
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| 10% | 15% | ~14,000 |
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| 20% | 10% | ~15,000 |
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| 40% | 10% | ~9,500 |
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| 60% | 5% | ~42,000 |
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Always warn: "These are estimates. Use a tool like Evan Miller's calculator or Statsig for precision."
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## Test Duration Guidance
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Minimum: 2 full weeks (to capture weekly seasonality)
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Maximum: 4 weeks (novelty effect distorts results beyond this)
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`Duration = Required sample ÷ (Daily traffic × % exposed)`
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Flag if traffic is too low to reach significance in under 8 weeks — recommend a different approach (e.g., holdout test, qualitative research).
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## Output Format
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### A/B Test Plan — [Test Name] — [Date]
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**Hypothesis:**
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> [Filled hypothesis template]
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**Variants:**
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- Control (A): [Current experience]
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- Treatment (B): [Changed experience — be specific]
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**Primary Metric:** [Metric name + how measured]
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**Guardrail Metrics:** [Metrics that must not degrade]
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**Target Segment:** [Who sees the test — % of traffic, user type]
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**Traffic Split:** [50/50 recommended unless ramp-up needed]
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**Sample Size Required:** ~[N] users per variant
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**Estimated Duration:** [X] weeks (based on [Y] daily eligible users)
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**Significance Threshold:** 95% confidence, 80% power
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**Exclusions:** [Any user segments to exclude and why]
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**Rollback Trigger:** If [guardrail metric] degrades by [X%], stop the test immediately.
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**Results Interpretation Guide:**
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- ✅ Ship if: Treatment shows [X%]+ lift on primary metric at 95% confidence AND guardrail metrics are stable
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- 🔄 Iterate if: Direction is positive but not significant — consider extending or redesigning
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- ❌ Reject if: No lift or negative direction at significance
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- ⚠️ Inconclusive: Do not ship. Do not call it a win.
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---
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## Guidelines
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- Always recommend against peeking at results before the test reaches planned sample size — explain p-hacking risk
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- If user wants to test multiple variants, explain the multiple comparisons problem and recommend a Bonferroni correction or a Bayesian approach
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- If traffic is very low (<1,000 users/day), recommend qualitative alternatives: moderated testing, 5-second tests, or user interviews
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- Never approve a test with no guardrail metrics — always protect revenue, retention, or core engagement
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