Can I use this for Bayesian analysis?

Absolutely. It supports Bayesian t-tests, ANOVA, and regression, providing Bayes Factors and posterior distribution visualizations using libraries like PyMC.

Does it include power analysis for study planning?

Yes, it provides a priori power analysis to determine required sample sizes before data collection and sensitivity analysis to determine detectable effect sizes.

Does this skill handle non-parametric tests?

Yes, it includes alternatives like Mann-Whitney U, Wilcoxon, Kruskal-Wallis, and Friedman tests for when parametric assumptions like normality are violated.

How does it help with APA formatting?

The skill automatically formats statistical outputs to meet APA standards, including the correct reporting of t-values, degrees of freedom, p-values, and effect sizes.

Statistical Analysis Toolkit

Name: Statistical Analysis Toolkit
Author: Zehong-Wang

byZehong-Wang

0•

Data Science & ML

Performs comprehensive statistical hypothesis testing, regression analysis, and Bayesian modeling with automated assumption checking and APA-style reporting.

The Statistical Analysis skill provides a robust framework for academic and scientific research within Claude. It automates complex workflows including intelligent test selection, rigorous assumption verification (normality, homoscedasticity, and outlier detection), and the execution of both parametric and non-parametric tests. By integrating advanced Python libraries like Pingouin, Statsmodels, and PyMC, it enables users to calculate precise effect sizes, conduct power analyses for study planning, and generate publication-ready results in APA format, ensuring both statistical rigor and professional presentation.

Key Features

01Comprehensive hypothesis testing including t-tests, ANOVA, Chi-square, and non-parametric alternatives

02Automated assumption checking with diagnostic visualizations like Q-Q and residual plots

03A priori power analysis and sensitivity analysis for robust research study planning

04Standardized APA-style reporting and publication-quality figure generation

05Advanced regression modeling and Bayesian statistical analysis with Bayes Factor reporting

060 GitHub stars

Use Cases

01Calculating required sample sizes and effect sizes for grant proposals and research design

02Verifying statistical assumptions and performing data diagnostics prior to machine learning modeling

03Analyzing experimental or observational data for academic papers and scientific journals

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add zehong-wang/kosmos statistical-analysis

For use in Claude.ai and ChatGPT

Download Skill