statistical-analysis
Statistical analysis toolkit. Hypothesis tests (t-test, ANOVA, chi-square), regression, correlation, Bayesian stats, power analysis, assumption checks, APA reporting, for academic research.
npx skills add foryourhealth111-pixel/Vibe-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
Statistical analysis is a toolkit that guides users through selecting appropriate statistical tests, checking assumptions, running analyses (including t-tests, ANOVA, chi-square, regression, correlations, and Bayesian methods), computing effect sizes, performing power analyses, and producing APA-style reporting.
How it works
- It offers a decision-driven workflow to determine analysis paths (test selection, assumption checking, running analyses, reporting).
- It provides a Test Selection Guide to map data characteristics to the proper tests.
- It includes an Assumption Checking module with a Python function comprehensive_assumption_check and individual checks (normality, homogeneity of variance, linearity, outliers) and recommended remedial actions.
- It supports Running Statistical Tests using libraries such as scipy.stats, statsmodels, pingouin, pymc, and arviz, with concrete code examples for t-tests, ANOVA, linear regression, and Bayesian t-tests.
- It standardizes Effect Sizes and Confidence Intervals, offering quick references for common tests (Cohen's d, partial eta-squared, r, R²) and guidance on reporting.
- It covers Power Analysis for a priori planning and sensitivity analyses, with code snippets using statsmodels and related power functions.
- It provides APA-style Reporting templates and example outputs for t-tests, ANOVA, regression, and Bayesian analyses, including descriptive stats, test statistics, effect sizes, and assumptions.
When to use it
Use when: you need to conduct statistical hypothesis tests (t-tests, ANOVA, chi-square), perform regression or correlation analyses, run Bayesian analyses, check assumptions, calculate effect sizes and power, and report results in APA format for academic research.
What it can touch
- Python libraries: scipy.stats, statsmodels, pingouin, pymc, arviz
- Example code blocks and scripts located under the bundled/skills/statistical-analysis/ directory, including scripts/assumption_checks.py and various code snippets for analyses.
Caveats
- License is Apache-2.0.
- Examples assume data in specific column names (e.g., 'score', 'group') and workflow steps as shown; users should adapt to their data schema.
- Bayesian analyses require Bayesian modeling with pymc and related tooling; interpretations rely on posterior summaries and credible intervals as demonstrated.
# 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
What does the statistical-analysis skill do?
Statistical analysis toolkit. Hypothesis tests (t-test, ANOVA, chi-square), regression, correlation, Bayesian stats, power analysis, assumption checks, APA reporting, for academic research.
How do I install it?
Run `npx skills add foryourhealth111-pixel/Vibe-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 foryourhealth111-pixel/Vibe-Skills, a repository with 2,593 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.