statistical-analysis
Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.
npx skills add majiayu000/claude-skill-registry --skill statistical-analysis-k-dense-ai-scientific-agent-ski-2 --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.
What it does
Statistical analysis guides the user through selecting appropriate statistical tests, planning analyses, checking assumptions, performing analyses, interpreting results, and producing APA-formatted reports. It covers hypothesis tests (t-test, ANOVA, chi-square), regression, correlations, and Bayesian alternatives, plus effect sizes, power analyses, and reporting.
How it works
The skill provides structured sections that instruct the agent to: choose tests based on data and research questions; perform power analyses to determine sample sizes; automatically verify assumptions with diagnostics and visualizations; run tests (e.g., t-tests, ANOVA, chi-square, regression) and compute diagnostics; calculate and interpret effect sizes; generate APA-style reports and publication-ready figures/tables; and conduct Bayesian analyses with examples using PyMC, Bayes Factors, and related tooling. It includes code snippets and tool references (e.g., pingouin, scipy.stats, statsmodels, pymc, arviz) illustrating how to execute analyses and extract statistics (e.g., t, p_val, cohen_d, CI95, eta_p2).
When to use it
Use when conducting statistical hypothesis tests, regression/correlation analyses, Bayesian analyses, assumption checks, power analyses, effect sizes, and APA-formatted reporting for research data.
What it can touch
The skill references tools and libraries for computation and plotting, including pingouin, scipy.stats, statsmodels, pymc, arviz, and standard Python plotting (matplotlib, seaborn). It also mentions scripts such as scripts/assumption_checks.py for automated checks and file interactions in examples (e.g., references/ guides, references/reporting_standards.md).
Caveats
License is MIT. The skill provides code examples and workflows but does not guarantee specific outcomes; it presents methods and reporting formats. It emphasizes that effect sizes and confidence intervals accompany p-values and that reporting should follow APA guidelines. It notes compatibility considerations for package versions and Python requirements.
# Statistical Analysis ## Overview Statistical analysis is a systematic process for testing hypotheses and quantifying relationships. Conduct hypothesis tests (t-test, ANOVA, chi-square), regression, correlation, and Bayesian analyses with assumption checks and APA reporting. Apply this skill for academic research. ## When to Use This Skill This skill should be used when: - Conducting statistical hypothesis tests (t-tests, ANOVA, chi-square) - Performing regression or correlation analyses - Running Bayesian statistical analyses - Checking statistical assumptions and diagnostics - Calculating effect sizes and conducting power analyses - Reporting statistical results in APA format - Analyzing experimental or observational data for research --- ## Installation Use **uv** to install the libraries used in this skill. Pin versions in production; unpinned installs are fine for exploration. ```bash # Core frequentist stack (Python 3.10+; 3.12+ recommended for latest SciPy/ArviZ) uv pip install "pingouin>=0.6" "scipy>=1.11" "statsmodels>=0.14.6" pandas matplotlib seaborn # Bayesian modeling (PyMC 5 + ArviZ; ArviZ 0.23+ requires Python 3.12+) uv pip install "pymc>=5.0" "arviz>=0.17" ``` **Co
- Overview
- When to Use This Skill
- Installation
- Core Capabilities
- 1. Test Selection and Planning
- 2. Assumption Checking
- 3. Statistical Testing
- 4. Effect Sizes and Interpretation
- 5. Professional Reporting
- Workflow Decision Tree
- Test Selection Guide
- Quick Reference: Choosing the Right Test
- Assumption Checking
- Systematic Assumption Verification
Core frequentist stack (Python 3.10+; 3.12+ recommended for latest SciPy/ArviZ) uv pip install "pingouin>=0.6" "scipy>=1.11" "statsmodels>=0.14.6" pandas matplotlib seaborn Bayesian modeling (PyMC 5 + ArviZ; ArviZ 0.23+ requires Python 3.12+) uv pip install "pymc>=5.0" "arviz>=0.17"
What does the statistical-analysis skill do?
Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill statistical-analysis-k-dense-ai-scientific-agent-ski-2 --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.