ab-test-readout
Analyse a finished A/B test and write the readout — the result, whether it's statistically and practically significant, what it means, and the ship/no-ship call. Use when asked to analyse experiment results, write an A/B test readout, interpret test data, or decide whether to ship a variant. Produces a clear verdict with the lift and confidence, segment cuts, the risks (peeking, novelty, sample), and a recommendation. Distinct from planning a test — this reads results.
git clone --depth 1 https://github.com/mohitagw15856/pm-claude-skills /tmp/ab-test-readout && cp -r /tmp/ab-test-readout/exports/openclaw/ab-test-readout ~/.claude/skills/ab-test-readoutSKILL.md
# A/B Test Readout Skill The hard part of an experiment is the readout: not "B won" but "is this real, is it big enough to matter, and should we ship?" This skill turns results into an honest decision — and flags the ways A/B results lie. ## Working from a brief Given results (even partial), **write the full readout anyway**. If significance isn't provided, reason about it from the numbers and flag what's needed to confirm. Mark assumed figures. Never declare a winner without addressing significance and sample. ## Required Inputs Ask for (if not already provided): - **The hypothesis** and the **primary metric** - **Results** — control vs variant: conversions/rate, sample size per arm, duration - **Guardrail metrics** (revenue, retention, latency, complaints) that mustn't regress - **Pre-registered decision rule** (what would count as a win) if one exists ## Output Format ### 1. Verdict (one line) *Ship / Don't ship / Inconclusive — keep running* — with the headline number. ### 2. The result | Metric | Control | Variant | Relative lift | Significant? | |---|---|---|---|---| | Primary | | | | p / CI | | Guardrail(s) | | | | | State **statistical** significance (p-value / confidence interval) *and* **practical** significance (is the lift big enough to matter given the cost?). ### 3. Did it really win? Address the ways A/B tests mislead: - **Sample / power** — was the test adequately powered, or under-sampled? - **Peeking** — was the call made early, inflating false positives? - **Novelty / primacy** — could the effect fade? - **Segments** — does the win hold across key segments, or is it driven by one? ### 4. Segment cuts Where the effect is strong vs flat vs negative (new vs returning, platform, geography). ### 5. Recommendation & next step Ship / iterate / re-run, plus what to monitor post-launch or what the follow-up test should isolate. ## Quality Checks - [ ] Distinguishes statistical from practical significance - [ ] Checks guardrail metrics, not just the primary - [ ] Flags peeking, power, novelty, and segment-driven wins - [ ] Recommendation follows from the evidence, with a monitoring/next-test step - [ ] Doesn't declare a winner on an underpowered or peeked result ## Anti-Patterns - "B won by 8%!" with no significance or sample size - Calling a result early (peeking) and shipping - Ignoring a guardrail regression because the primary went up - A statistically significant but practically meaningless lift treated as a win
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