Agent skill · research science · k-dense-ai
statistical-analysis
Guided statistical analysis for research data - test selection, assumption checking, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting. Use whenever a user wants to compare groups, test a hypothesis, analyze experimental or survey data, check statistical assumptions, compute required sample sizes, or write up results - even if they never name a specific test. Covers t-tests, ANOVA, chi-square, correlation, regression, non-parametric and Bayesian methods. For low-level model APIs, see the statsmodels and pymc skills.
Why this skill is useful
Provides detailed workflows and specific statistical methods that the AI wouldn't reliably generate on its own.
What it needs
Requires arviz, matplotlib, pandas, pingouin, pymc, scipy, seaborn, statsmodels, uv installed locally. About 11k tokens when loaded. Last updated 2026-08-03. 32,849 stars on the source repository.
What this skill does
Statistical Analysis Overview Conduct hypothesis tests (t-tests, ANOVA, chi-square), regression, correlation, and Bayesian analyses with systematic assumption checking, effect sizes, and APA-style reporting. The goal is an analysis a reviewer could not tear apart: the right test, verified assumptions, honest effect sizes, and a complete write-up. When to Use This Skill Use this skill when: Conducting statistical hypothesis tests (t-tests, ANOVA, chi-square, non-parametric) 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. Compatibility notes (verified against pingouin 0.6.1, statsmodels 0.14.6, arviz 1.2, 2026): Pingouin 0.6.0 renamed output columns to remove special characters: pval, cohend, CI95, punc (previously p-val, cohen-d, CI95%, p-unc in 0.5.x). Examples below use the current names; if stuck on 0.5.x, use the hyphenated forms. statsmodels + SciPy: use statsmodels>=0.14.6 with scipy>=1.11 to avoid lazywhere import errors on SciPy 1.16+. ArviZ 1.x: az.summary() now defaults to 89% intervals (eti89 columns) and the width parameter is ciprob (not hdiprob). To report a conventional 95% credible interval, pass az.summary(trace, ciprob=0.95). One-sided Bayes Factors are gone from Pingouin: pg.ttest(..., alternative='greater') silently drops the BF10 column, and pg.bayesfactorttest raises on one-sided alternatives. For one-sided Bayesian tests, use PyMC directly (compute the posterior probability of the directional hypothesis) or JASP/R's BayesFactor. For model-specific APIs (OLS, GLM, ARIMA), see the statsmodels skill. For PyMC workflows, see the pymc skill. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills k-dense-ai/statistical-analysis