Individual skill
ab-testing
Use when designing or analyzing a controlled experiment — falsifiable hypothesis, sample size from an MDE, reading significance/CI/power, CUPED, or rescuing tests that won't go significant. NOT recurring metric tracking (that is `analytics`), NOT north-star/KPI trees (that is `kpi-framework`), NOT projecting metrics forward (that is `forecasting`).
skills/ab-testing/SKILL.md
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- agent-skills Repository topic
- agentic-ai Repository topic
- ai-agents Repository topic
- ai-coding Repository topic
- claude-code Repository topic
- cli Repository topic
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- ab-testing Declared skill tag
- experimentation Declared skill tag
- statistics Declared skill tag
- cuped Declared skill tag
- sample-size Declared skill tag
- hypothesis-testing Declared skill tag
- data Derived category
- testing Derived category
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SKILL.mdcore · 9,831 bytesSKILL.mdcore · 9,831 bytesSKILL.mdcore · 9,831 bytesevals/README.mddoc · 636 bytesevals/cases.yamlasset · 3,602 bytesreferences/pitfalls.mddoc · 4,512 bytesreferences/sample-size-and-cuped.mddoc · 4,832 bytesscripts/verify.shruns · 3,916 bytes
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# A/B testing — design and read a defensible experiment
An experiment without a pre-committed sample size and a single primary metric is not an experiment.
It is a dashboard you stare at until it tells you what you wanted to hear. The discipline lives almost
entirely *before* traffic ships: a falsifiable hypothesis, one primary metric, a sample size derived
from the smallest effect worth detecting, and a stop rule you cannot renegotiate at 2pm on day four.
## Pre-test checklist — every line true before any traffic
Each one is a place experiments die silently.
- [ ] A **falsifiable hypothesis** — names the change, the direction, and the metric it moves.
- [ ] Exactly **ONE primary metric**. More than one primary = multiple comparisons = inflated false positives.
- [ ] **Guardrail metrics** — what you refuse to harm (latency, refunds, unsubscribes) even for a win.