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 LeonChaoX/qinyan-academic-skills --skill statistical-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.
What it does
Guides statistical analysis tasks: test selection, assumption checking, statistical testing, effect sizes and interpretation, power analysis, and APA-style reporting for academic research.
How it works
- Recommends tests based on research questions and data characteristics.
- Performs assumption checks with automated diagnostics and visualizations via scripts/assumption_checks.py.
- Supports a range of tests: t-tests, ANOVA, chi-square, regression (linear, multiple, logistic), correlations, and Bayesian alternatives.
- Calculates and reports effect sizes with confidence intervals.
- Produces APA-formatted reports and publication-ready figures/tables.
- Provides example code snippets using libraries like scipy.stats, statsmodels, pingouin, pymc, and arviz for running analyses and diagnostics.
- Includes a workflow decision tree to guide users from test selection to reporting.
When to use it
- When conducting statistical hypothesis tests (t-tests, ANOVA, chi-square)
- When performing regression or correlation analyses
- When running Bayesian statistical analyses
- When checking statistical assumptions and diagnostics
- When calculating effect sizes and conducting power analyses
- When reporting statistical results in APA format
- When analyzing experimental or observational data for research
What it can touch
- Uses Python libraries: scipy.stats, statsmodels, pingouin, pymc, arviz
- Includes scripts: scripts/assumption_checks.py and functions like comprehensive_assumption_check, check_normality, check_homogeneity_of_variance, etc.
- Demonstrates code snippets that import and utilize these libraries and modules
Caveats
- License: MIT
- Requires interpretation of outputs and adherence to APA guidelines as per references/reporting_standards.md
- Some advanced analyses rely on external packages; ensure compatibility and correct installation
# 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 LeonChaoX/qinyan-academic-skills --skill statistical-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 LeonChaoX/qinyan-academic-skills, a repository with 759 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.