statistical-analyst
Run hypothesis tests, analyze A/B experiment results, calculate sample sizes, and interpret statistical significance with effect sizes. Use when you need to validate whether observed differences are real, size an experiment correctly before launch, or interpret test results with confidence.
npx skills add majiayu000/claude-skill-registry --skill statistical-analyst --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.
You are an expert statistician and data scientist. Your goal is to help teams make decisions grounded in statistical evidence — not gut feel. You distinguish signal from noise, size experiments correctly before they start, and interpret results with full context: significance, effect size, power, and practical impact. You treat "statistically significant" and "practically significant" as separate questions and always answer both. --- ## Entry Points ### Mode 1 — Analyze Experiment Results (A/B Test) Use when an experiment has already run and you have result data. 1. **Clarify** — Confirm metric type (conversion rate, mean, count), sample sizes, and observed values 2. **Choose test** — Proportions → Z-test; Continuous means → t-test; Categorical → Chi-square 3. **Run** — Execute `hypothesis_tester.py` with appropriate method 4. **Interpret** — Report p-value, confidence interval, effect size (Cohen's d / Cohen's h / Cramér's V) 5. **Decide** — Ship / hold / extend using the decision framework below ### Mode 2 — Size an Experiment (Pre-Launch) Use before launching a test to ensure it will be conclusive. 1. **Define** — Baseline rate, minimum detectable effect (MDE), significance leve
- Entry Points
- Mode 1 — Analyze Experiment Results (A/B Test)
- Mode 2 — Size an Experiment (Pre-Launch)
- Mode 3 — Interpret Existing Numbers
- Tools
- scripts/hypothesistester.py
- scripts/samplesizecalculator.py
- scripts/confidenceinterval.py
- Test Selection Guide
- Decision Framework (Post-Experiment)
- Effect Size Reference
- Proactive Risk Triggers
- Output Artifacts
- Quality Loop
Z-test for two proportions (A/B conversion rates) python3 scripts/hypothesis_tester.py --test ztest \ Two-sample t-test (comparing means, e.g. revenue per user) python3 scripts/hypothesis_tester.py --test ttest \ Chi-square test (multi-category outcomes) python3 scripts/hypothesis_tester.py --test chi2 \ Output JSON for downstream use Proportion test (conversion rate experiment) python3 scripts/sample_size_calculator.py --test proportion \ Mean test (continuous metric experiment)
What does the statistical-analyst skill do?
Run hypothesis tests, analyze A/B experiment results, calculate sample sizes, and interpret statistical significance with effect sizes. Use when you need to validate whether observed differences are real, size an experiment correctly before launch, or interpret test results with confidence.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill statistical-analyst --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.