Which Python libraries does this skill use for analysis?

The skill primarily utilizes scipy.stats, statsmodels, and pingouin for frequentist tests, while using pymc and arviz for advanced Bayesian modeling and diagnostics.

Is the output formatted for academic journals?

Absolutely. One of the core capabilities is generating results in standard APA format, including all necessary statistics such as degrees of freedom, p-values, and effect sizes.

Can this skill handle non-parametric data distributions?

Yes, it includes guidance and implementation for non-parametric alternatives such as Mann-Whitney U, Kruskal-Wallis, Wilcoxon signed-rank, and Spearman correlation.

Does it provide diagnostic visualizations?

Yes, the skill generates essential diagnostic plots including Q-Q plots, residual plots, and distribution histograms to help verify statistical assumptions.

Statistical Analysis & Reporting

Name: Statistical Analysis & Reporting
Author: youyinnn

byyouyinnn

0•

Data Science & ML

Perform comprehensive statistical testing, assumption verification, and APA-style result reporting for research and experimental data.

This skill provides an end-to-end framework for rigorous statistical analysis within Claude Code, guiding users through the entire research lifecycle from study planning to final reporting. It assists in selecting the appropriate statistical tests based on data characteristics, automatically verifies underlying assumptions like normality and homogeneity, performs complex calculations using industry-standard libraries, and formats the final output into publication-ready APA results. Whether you are conducting a-priori power analysis or implementing Bayesian alternatives to frequentist methods, this skill ensures statistical accuracy and methodological rigor in every analysis.

Key Features

01Guided statistical test selection for frequentist and Bayesian methods

02Standardized APA-formatted reporting for academic and professional use

03Comprehensive support for t-tests, ANOVA, regression, and Chi-square

04Automated assumption checking with diagnostic visualizations and Q-Q plots

05A-priori power analysis and effect size calculations (Cohen's d, Eta-squared)

060 GitHub stars

Use Cases

01Calculating required sample sizes for new studies using power analysis

02Validating statistical assumptions before running complex predictive regression models

03Analyzing experimental research data for academic publication and peer review

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add youyinnn/skills-collection statistical-analysis

For use in Claude.ai and ChatGPT

Key Features

01Guided statistical test selection for frequentist and Bayesian methods

02Standardized APA-formatted reporting for academic and professional use

03Comprehensive support for t-tests, ANOVA, regression, and Chi-square

04Automated assumption checking with diagnostic visualizations and Q-Q plots

05A-priori power analysis and effect size calculations (Cohen's d, Eta-squared)

060 GitHub stars

Use Cases

01Calculating required sample sizes for new studies using power analysis

02Validating statistical assumptions before running complex predictive regression models

03Analyzing experimental research data for academic publication and peer review