Is Bayesian analysis included?

Yes, the skill provides Bayesian versions of common tests which offer Bayes Factors and direct probability statements about your hypotheses.

Does this skill handle non-normal data?

Yes, it includes systematic assumption checking that detects non-normality and automatically recommends appropriate non-parametric alternatives like Mann-Whitney U or Kruskal-Wallis tests.

Does it support power analysis?

Yes, it provides tools for both a priori power analysis to determine sample sizes before a study and sensitivity analysis to determine detectable effect sizes post-study.

Which Python libraries are required for this skill?

The skill utilizes the standard Python scientific stack, specifically scipy.stats, statsmodels, pingouin for frequentist tests, and PyMC/ArviZ for Bayesian modeling.

Can it generate APA-style results?

Absolutely. One of the core capabilities is generating publication-ready reports that follow APA formatting guidelines, including effect sizes and confidence intervals.

Statistical Analysis

Name: Statistical Analysis
Author: K-Dense-AI

byK-Dense-AI

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3,719

Data Science & ML

Performs comprehensive statistical testing, hypothesis verification, and research data modeling using Python's scientific ecosystem.

About

The Statistical Analysis skill transforms Claude into a sophisticated research assistant capable of conducting end-to-end data investigations with academic rigor. It streamlines the analytical workflow by selecting appropriate tests—ranging from t-tests and ANOVA to complex regression models—while rigorously checking assumptions like normality and homoscedasticity. Designed for data scientists and academic researchers, the skill automates effect size calculations, power analyses, and APA-style reporting, ensuring that results are both statistically sound and ready for publication.

Key Features

Automated assumption verification including normality and variance checks
3,719 GitHub stars
Comprehensive hypothesis testing for parametric and non-parametric data
Bayesian statistical modeling using PyMC and ArviZ
Advanced regression diagnostics and correlation analysis
Professional APA-standardized reporting and power analysis

Use Cases

Running Bayesian alternatives to traditional frequentist tests for more nuanced evidence quantification
Conducting a priori power analysis to determine required sample sizes for study planning
Validating experimental data for peer-reviewed academic publications

About

Key Features

Automated assumption verification including normality and variance checks
3,719 GitHub stars
Comprehensive hypothesis testing for parametric and non-parametric data
Bayesian statistical modeling using PyMC and ArviZ
Advanced regression diagnostics and correlation analysis
Professional APA-standardized reporting and power analysis

Use Cases

Running Bayesian alternatives to traditional frequentist tests for more nuanced evidence quantification
Conducting a priori power analysis to determine required sample sizes for study planning
Validating experimental data for peer-reviewed academic publications