experiment-readout
GitHub分析A/B测试结果，计算提升率、P值和置信区间，检查统计显著性与实际意义、护栏指标及实验有效性，生成诚实的读报并给出上线/不上线建议。
触发场景
安装
npx skills add mohitagw15856/pm-claude-skills --skill experiment-readout -g -y
SKILL.md
Frontmatter
{
"name": "experiment-readout",
"description": "Analyse a finished A\/B test and write an honest results readout with real statistics. Use when asked to read out an A\/B test, analyse experiment results, check if a result is statistically significant, or decide ship\/no-ship from test data. Produces a readout — the computed lift, p-value & confidence interval, a significance verdict, guardrail check, and a clear ship \/ no-ship \/ iterate recommendation. Includes a stdlib significance calculator."
}
Experiment Readout Skill
A test result is only a decision if the statistics are sound — and "variant looks higher" is not a result. This skill computes the lift, the p-value, and a confidence interval from the raw counts, checks the guardrails, and writes an honest readout with a clear ship/no-ship call — flagging the traps (peeking, underpowered, novelty, a significant but tiny effect) that make teams ship noise.
Required Inputs
Ask for these only if they aren't already provided:
- The metric & data — for a conversion test: users and conversions per variant (control vs. treatment). For a continuous metric: mean, SD, and n per variant.
- The hypothesis — what you expected and the minimum effect that matters.
- Guardrail metrics — what shouldn't get worse (revenue, latency, retention).
- Test setup — planned sample size/duration, and whether it ran to plan (for the peeking check).
Output Format
Experiment Readout: [test name]
1. Result — computed (use the helper): control vs. treatment rate, absolute & relative lift, p-value, and the confidence interval on the difference.
| Variant | N | Conversions | Rate |
|---|---|---|---|
| Control | |||
| Treatment |
→ Lift: X% (CI: [a%, b%]) · p = 0.0xx
2. Verdict — significant at the stated bar or not, and whether the effect is big enough to matter (a significant +0.2% may not be worth the complexity). Distinguish statistical from practical significance.
3. Guardrails — did anything you promised not to harm move? A win that tanks a guardrail isn't a win.
4. Validity checks — was it run to the planned sample (no peeking/early-stopping)? Sample-ratio mismatch? Novelty/seasonality? Call out anything that undermines the result.
5. Recommendation — ship / no-ship / iterate / re-run, with the reason. If inconclusive, say so — "no significant difference" is a valid, useful result, not a failure to spin.
Programmatic Helper
scripts/ab_significance.py (stdlib only) computes the two-proportion z-test, p-value, lift, and CI:
# python3 ab_significance.py <control_n> <control_conv> <treat_n> <treat_conv>
python3 scripts/ab_significance.py 10000 800 10000 880
python3 scripts/ab_significance.py 10000 800 10000 880 --json
Quality Checks
- Lift, p-value, and a confidence interval are computed (not just "higher")
- Statistical significance AND practical significance are both assessed
- Guardrail metrics are checked, not just the primary
- Validity is checked: ran to planned n, no peeking, no sample-ratio mismatch
- An inconclusive result is reported honestly, not spun into a win
- The recommendation is explicit (ship/no-ship/iterate/re-run)
Anti-Patterns
- Do not call significance by eye — compute the p-value and CI; a higher number isn't a result
- Do not ignore the confidence interval — a CI spanning zero (or huge) means you don't actually know the effect
- Do not confuse statistical with practical significance — a tiny significant lift may not be worth shipping
- Do not trust a peeked/early-stopped test — stopping when it looks good inflates false positives massively
- Do not spin a null result — "no detectable difference" is honest and often the right call
Based On
Frequentist A/B analysis — two-proportion z-test, confidence intervals, guardrails, and the peeking/practical-significance pitfalls.
版本历史
- a38bc30 当前 2026-07-05 11:15
