Does it automatically handle assumption violations?

It performs systematic checks for normality and variance. If assumptions are violated, it provides specific recommendations such as using Welch's t-test or non-parametric alternatives.

Can this skill help me choose the right statistical test?

Yes, it includes a comprehensive Workflow Decision Tree and Test Selection Guide to help you choose between parametric, non-parametric, and Bayesian options based on your data.

Is the reporting output suitable for academic journals?

Absolutely. The skill is specifically designed to generate statistical interpretations and result summaries in standard APA format, including all necessary test statistics and effect sizes.

Which Python libraries does this statistical skill use?

The skill utilizes SciPy for core tests, Statsmodels for regression, Pingouin for user-friendly testing and effect sizes, and PyMC for Bayesian modeling.

Does it support Bayesian analysis?

Yes, it provides Bayesian alternatives for t-tests, ANOVA, and regression, offering Bayes Factors and posterior probability statements instead of just p-values.

Statistical Analysis

Name: Statistical Analysis
Author: K-Dense-AI

byK-Dense-AI

•

2,188

데이터 과학 및 ML

Performs professional statistical modeling, hypothesis testing, and rigorous assumption verification with publication-ready APA reporting.

소개

The Statistical Analysis skill transforms Claude into a specialized research assistant capable of executing complex end-to-end data analysis workflows. It provides a structured framework for selecting the correct statistical tests, performing a priori power analyses, and conducting automated assumption checks with diagnostic visualizations. By integrating industry-standard Python libraries like SciPy, Statsmodels, and Pingouin, the skill ensures that research findings are both statistically sound and formatted according to APA standards, making it an essential tool for academic researchers and data scientists using Claude Code.

주요 기능

Advanced regression modeling with built-in diagnostics for multicollinearity and residual analysis.
2,188 GitHub stars
Bayesian statistical modules for calculating Bayes Factors and posterior probability distributions.
Standardized APA-style reporting with effect sizes, confidence intervals, and publication-quality figures.
Comprehensive frequentist testing suite covering t-tests, ANOVA, chi-square, and non-parametric alternatives.
Automated assumption verification including normality testing and homogeneity of variance checks.

사용 사례

Analyzing experimental or observational research data for academic publication.
Validating statistical model assumptions and performing remedial data transformations.
Conducting power analyses to determine optimal sample sizes during study planning phases.

소개

주요 기능

Advanced regression modeling with built-in diagnostics for multicollinearity and residual analysis.
2,188 GitHub stars
Bayesian statistical modules for calculating Bayes Factors and posterior probability distributions.
Standardized APA-style reporting with effect sizes, confidence intervals, and publication-quality figures.
Comprehensive frequentist testing suite covering t-tests, ANOVA, chi-square, and non-parametric alternatives.
Automated assumption verification including normality testing and homogeneity of variance checks.

사용 사례

Analyzing experimental or observational research data for academic publication.
Validating statistical model assumptions and performing remedial data transformations.
Conducting power analyses to determine optimal sample sizes during study planning phases.