How does it handle non-normal data distributions?

The skill includes automated normality checks (Shapiro-Wilk) and provides specific recommendations for non-parametric alternatives such as Mann-Whitney U or Kruskal-Wallis tests.

Can this skill help with study planning?

Yes, it includes a priori power analysis tools to help researchers determine the required sample size and effect size detection thresholds before data collection begins.

Will the output be ready for academic publication?

Yes, it is specifically programmed to generate statistical interpretations in APA format, including the required degrees of freedom, p-values, and effect sizes.

Which Python libraries does this skill support?

The skill primarily utilizes scipy.stats, statsmodels, and pingouin for frequentist tests, while using PyMC and ArviZ for advanced Bayesian analysis.

Statistical Analysis Toolkit

Name: Statistical Analysis Toolkit
Author: BbgnsurfTech

byBbgnsurfTech

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Data Science & ML

Performs comprehensive statistical analysis including hypothesis testing, regression modeling, and Bayesian inference with APA-compliant reporting.

About

The Statistical Analysis skill transforms Claude into a sophisticated research assistant capable of planning studies, validating statistical assumptions, and executing complex data analyses. It automates the selection of appropriate tests—ranging from t-tests and ANOVA to advanced Bayesian models—while ensuring rigorous diagnostics through automated scripts. Designed for academic research and data-driven decision-making, it provides publication-ready results, complete with effect sizes, confidence intervals, and APA-formatted interpretations using industry-standard Python libraries like SciPy, Pingouin, and Statsmodels.

Key Features

3 GitHub stars
Standardized APA-style reporting and publication-ready figure generation
Bayesian statistical alternatives using PyMC and ArviZ for probabilistic inference
Advanced regression modeling with multi-collinearity diagnostics and VIF analysis
Comprehensive hypothesis testing suite including t-tests, ANOVA, and Chi-square
Automated assumption checking with diagnostic visualizations (Q-Q plots, residuals)

Use Cases

Conducting a priori power analysis to determine required sample sizes for new studies
Analyzing experimental or observational data for academic research and journals
Validating data assumptions and performing remedial transformations on datasets

About

Key Features

3 GitHub stars
Standardized APA-style reporting and publication-ready figure generation
Bayesian statistical alternatives using PyMC and ArviZ for probabilistic inference
Advanced regression modeling with multi-collinearity diagnostics and VIF analysis
Comprehensive hypothesis testing suite including t-tests, ANOVA, and Chi-square
Automated assumption checking with diagnostic visualizations (Q-Q plots, residuals)

Use Cases

Conducting a priori power analysis to determine required sample sizes for new studies
Analyzing experimental or observational data for academic research and journals
Validating data assumptions and performing remedial transformations on datasets