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-hxk622-tokendance --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
Guides an AI agent to assist with statistical analysis tasks, including test selection, assumption checking, running analyses, calculating effect sizes, conducting power analyses, and producing APA-formatted reports. It covers t-tests, ANOVA, chi-square, regression, correlations, and Bayesian methods, with emphasis on diagnostics, reporting, and interpretation.
How it works
- Provides a Test Selection Guide to match research questions and data to appropriate tests.
- Directs use of an assumption-checking workflow with automated checks (outliers, normality, homogeneity of variance) and visualizations, plus recommended remedies.
- Details running statistical tests using Python libraries (scipy.stats, statsmodels, pingouin, pymc, arviz) and supplies example code for t-tests, ANOVA, regression, Bayesian tests, and post-hoc analyses.
- Includes sections on effect sizes, confidence intervals, and power analysis (a priori and post-hoc/sensitivity) with concrete code snippets.
- Outlines APA-style reporting elements and provides example report templates for different analyses.
- Covers both frequentist and Bayesian approaches, including how to compute and interpret Bayes factors and posterior probabilities.
When to use it
Use when you need help choosing appropriate statistical tests, performing assumption checks, planning or conducting analyses, computing effect sizes, performing power analyses, and generating APA-formatted reports.
What it can touch
- Tools: code_execute, web_search, read_url, create_document
- Libraries: scipy.stats, statsmodels, pingouin, pymc, arviz
- Scripts and data examples are referenced to run assumption checks and visualizations, and to produce reporting outputs.
Caveats
- Content centers on guidance and example code; actual results depend on data input.
- Includes specific function calls and file paths as written in the skill (e.g.,
scripts/assumption_checks.py,references/...?,from statsmodels.stats.power import (...)). - License indicated as MIT; adheres to tool and library licenses in examples.
# 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 --- ## Core Capabilities ### 1. Test Selection and Planning - Choose appropriate statistical tests based on research questions and data characteristics - Conduct a priori power analyses to determine required sample sizes - Plan analysis strategies including multiple comparison corrections ### 2. Assumption Checking - Automatically verify all relevant assumptions before running tests - Provide diagnostic visualizations (Q-Q plots, residual plots, bo
- Overview
- When to Use This Skill
- 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
- Individual Assumption Checks
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-hxk622-tokendance --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.