Can it generate reports suitable for academic publishing?

Absolutely. The skill is specifically designed to output results in APA format, including required statistics like effect sizes (Cohen's d, Eta-squared), confidence intervals, and exact p-values.

How does the skill handle assumption violations?

The toolkit automatically checks for normality, outliers, and homogeneity of variance. If assumptions are violated, it provides specific remedial actions, such as recommending Welch's t-test or data transformations.

Which Python libraries are required for this skill?

This skill primarily utilizes Scipy, Statsmodels, Pingouin for frequentist stats, and PyMC/ArviZ for Bayesian modeling, along with Matplotlib and Seaborn for visualizations.

Does this skill support non-parametric statistical tests?

Yes, it includes standard non-parametric alternatives such as the Mann-Whitney U, Wilcoxon signed-rank, Kruskal-Wallis, and Friedman tests for data that does not meet normality assumptions.

Statistical Analysis Toolkit

Name: Statistical Analysis Toolkit
Author: x-cmd

byx-cmd

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数据科学与机器学习

Conducts rigorous statistical hypothesis tests, regression models, and Bayesian analyses with APA-style reporting.

关于

The Statistical Analysis Toolkit is a comprehensive suite designed for academic research and data-driven decision-making. It automates the complex process of selecting appropriate statistical tests, verifying critical assumptions like normality and homoscedasticity, and calculating essential effect sizes. By integrating industry-standard Python libraries such as Pingouin, Scipy, and PyMC, it enables users to perform everything from standard t-tests and ANOVA to advanced Bayesian modeling and power analyses, while generating publication-ready outputs in APA format.

主要功能

Comprehensive hypothesis testing suite including t-tests, ANOVA, and non-parametric alternatives.
Bayesian statistical modeling providing direct probability statements and Bayes Factors.
Advanced regression modeling with multicollinearity (VIF) and diagnostic checks.
A priori and sensitivity power analysis for precise study planning and sample size determination.
8 GitHub stars
Automated assumption verification with diagnostic visualizations like Q-Q and residual plots.

使用场景

Performing diagnostic checks on raw data to ensure statistical model validity and robustness.
Validating experimental research hypotheses for academic publications and journals.
Calculating required sample sizes for future research studies through rigorous power analysis.

关于

主要功能

Comprehensive hypothesis testing suite including t-tests, ANOVA, and non-parametric alternatives.
Bayesian statistical modeling providing direct probability statements and Bayes Factors.
Advanced regression modeling with multicollinearity (VIF) and diagnostic checks.
A priori and sensitivity power analysis for precise study planning and sample size determination.
8 GitHub stars
Automated assumption verification with diagnostic visualizations like Q-Q and residual plots.

使用场景

Performing diagnostic checks on raw data to ensure statistical model validity and robustness.
Validating experimental research hypotheses for academic publications and journals.
Calculating required sample sizes for future research studies through rigorous power analysis.