Does this skill perform calculations or just provide guidance?

It does both. The skill provides decision logic for choosing the correct tests and generates Python code using libraries like pingouin and statsmodels to execute the actual analysis.

How does it help with academic publishing?

It generates APA-formatted statistical summaries and creates publication-ready visualizations such as Q-Q plots, residual diagrams, and distribution plots.

Can it handle non-parametric data?

Yes, it includes built-in guidance and implementation for non-parametric alternatives like Mann-Whitney U and Kruskal-Wallis tests when normality assumptions are violated.

Does it support Bayesian statistics?

Yes, the skill includes patterns and code snippets for Bayesian t-tests, ANOVA, and regression using PyMC and ArviZ.

Statistical Analysis Guide

Name: Statistical Analysis Guide
Author: Weiwei-Mao

byWeiwei-Mao

0•

Data Science & ML

Guides users through statistical test selection, assumption verification, and APA-formatted research reporting.

The Statistical Analysis skill transforms Claude into a specialized research assistant for quantitative data analysis and academic reporting. It provides end-to-end guidance for hypothesis testing, helping users select the correct statistical tests—such as t-tests, ANOVA, or regression—based on their specific data characteristics. Beyond execution, it automates rigorous methodology by performing assumption checks, power analyses, and Bayesian alternatives. By leveraging powerful Python libraries like pingouin and statsmodels, it ensures that every analysis is statistically sound and produces publication-ready results in standardized APA format.

Key Features

010 GitHub stars

02Comprehensive Bayesian alternatives for hypothesis testing with Bayes Factors

03Effect size calculation and power analysis for study planning

04Automated assumption checking with diagnostic visualizations

05Professional APA-style reporting for academic and research contexts

06Intelligent statistical test selection for parametric and non-parametric data

Use Cases

01Graduate students requiring guidance on statistical methodology and diagnostic diagnostics

02Data scientists validating experimental results through rigorous hypothesis testing

03Academic researchers preparing data for peer-reviewed journal submissions

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add weiwei-mao/hydrology-skills statistical-analysis

For use in Claude.ai and ChatGPT

Download Skill