Does it handle assumption checking?

Yes, it automatically verifies normality, homogeneity of variance, outliers, and linearity, providing visualizations and remedial advice when violations occur.

Which Python libraries are required?

The skill utilizes industry-standard libraries such as Scipy, Statsmodels, Pingouin for frequentist stats, and PyMC for Bayesian modeling.

What statistical tests can this skill perform?

It supports a wide range of tests including t-tests, ANOVA, Chi-square, correlation, linear/logistic regression, and their Bayesian counterparts.

Can I use this for academic writing?

Absolutely. The skill is designed to generate statistical results and interpretations in professional APA format, suitable for research papers and thesis work.

Does it support power analysis?

Yes, it includes tools for a priori power analysis to determine required sample sizes and sensitivity analysis to determine detectable effect sizes.

Statistical Analysis for Research

Name: Statistical Analysis for Research
Author: ricable

byricable

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データサイエンスとML

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

概要

This skill provides a robust toolkit for academic and scientific researchers using Claude. It automates complex statistical workflows, including intelligent test selection, rigorous assumption verification (normality, homogeneity, etc.), and execution of parametric, non-parametric, and Bayesian analyses. By integrating popular Python libraries like Pingouin and Statsmodels, it ensures high-precision calculations while generating publication-ready results and visualizations formatted to APA standards, making it an essential companion for data-driven research and experimental validation.

主な機能

A priori power analysis and sensitivity analysis for robust experimental design
Generation of publication-ready APA-style reports and statistical figures
Bayesian statistical alternatives providing direct probability statements and Bayes Factors
1 GitHub stars
Automated assumption checking with diagnostic visualizations and remedial recommendations
Comprehensive test suite including t-tests, ANOVA, Chi-square, and advanced regression models

ユースケース

Conducting power analyses to determine sample size requirements for new studies
Analyzing experimental research data for academic publication and peer review
Validating statistical assumptions before performing high-stakes data modeling

概要

主な機能

A priori power analysis and sensitivity analysis for robust experimental design
Generation of publication-ready APA-style reports and statistical figures
Bayesian statistical alternatives providing direct probability statements and Bayes Factors
1 GitHub stars
Automated assumption checking with diagnostic visualizations and remedial recommendations
Comprehensive test suite including t-tests, ANOVA, Chi-square, and advanced regression models

ユースケース

Conducting power analyses to determine sample size requirements for new studies
Analyzing experimental research data for academic publication and peer review
Validating statistical assumptions before performing high-stakes data modeling