abtesting-analysis
This skill enables detailed analysis of A/B test results, including uplift calculations, statistical significance interpretation, evaluation of long-run effects, detection of Simpson's paradox, and assessment of novelty effects to inform decisions.
npx skills add majiayu000/claude-skill-registry --skill abtesting-analysis --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
--- name: abtesting-analysis cluster: abtesting description: "Results: uplift calculation, significance interpretation, long-run effects, Simpson\'s paradox, novelty effect" tags: ["analysis","uplift","novelty-effect","decision"] dependencies: [] composes: [] similar_to: [] called_by: [] authorization_required: false scope: general model_hint: claude-sonnet embedding_hint: "ab test results analysis uplift novelty effect segment decision simpson" --- # abtesting-analysis ## Purpose This skill enables detailed analysis of A/B test results, including uplift calculations, statistical significance interpretation, evaluation of long-run effects, detection of Simpson's paradox, and assessment of novelty effects to inform decisions. ## When to Use Use this skill when processing A/B test data from experiments, such as website variants or app features, to quantify impacts like conversion rate differences. Apply it for data-driven decisions in marketing, product development, or UX testing, especially when segmenting users or checking for hidden biases. ## Key Capabilities - Calculate uplift: Computes percentage lift in metrics (e.g., revenue) between control and variant groups. - Interpret si
- Purpose
- When to Use
- Key Capabilities
- Usage Patterns
- Common Commands/API
- Integration Notes
- Error Handling
- Graph Relationships
openclaw abtesting analyze --input results.json --metric ctr --groups control variant
What does the abtesting-analysis skill do?
This skill enables detailed analysis of A/B test results, including uplift calculations, statistical significance interpretation, evaluation of long-run effects, detection of Simpson's paradox, and assessment of novelty effects to inform decisions.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill abtesting-analysis --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.