Does this skill check for statistical assumptions?

Yes, it includes a systematic assumption verification module that checks for normality, homogeneity of variance, outliers, and linearity, providing remedial recommendations when violations are detected.

Which Python libraries does this skill utilize?

It leverages industry-standard libraries including Scipy.stats, Statsmodels, and Pingouin for frequentist statistics, alongside PyMC and ArviZ for Bayesian modeling.

Can I use this for academic reporting?

Absolutely. The skill is specifically designed to generate statistical results, tables, and interpretations in standard APA format, making it ideal for research papers.

Does it support non-parametric alternatives?

Yes, it provides non-parametric alternatives such as Mann-Whitney U, Wilcoxon signed-rank, and Kruskal-Wallis tests for cases where parametric assumptions are not met.

Can it help me determine my required sample size?

Yes, the skill includes tools for a priori power analysis to determine necessary sample sizes based on expected effect size, alpha level, and desired power.

Scientific Statistical Analysis

Name: Scientific Statistical Analysis
Author: jimmc414

byjimmc414

•

324

Ciencia de Datos y ML

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

Acerca de

Scientific Statistical Analysis is a comprehensive toolkit designed to empower researchers and data scientists using Claude Code. It automates the complex workflow of statistical discovery—from selecting the appropriate test and verifying mathematical assumptions to executing regressions, ANOVA, and Bayesian analyses. By integrating robust Python libraries like SciPy, Pingouin, and PyMC, it ensures rigorous methodology and provides publication-ready outputs in APA format, making it an essential companion for academic research, experimental validation, and evidence-based decision-making.

Características Principales

Comprehensive hypothesis testing including T-tests, ANOVA, Chi-square, and non-parametric alternatives.
Bayesian statistical alternatives for providing Bayes Factors and direct probability statements.
324 GitHub stars
Automated assumption checking with diagnostic visualizations (Q-Q plots, residuals, and box plots).
Built-in power analysis for determining required sample sizes and post-study sensitivity analysis.
Advanced regression modeling with multicollinearity checks (VIF) and diagnostic plotting.

Casos de Uso

Analyzing experimental research data for academic publication with automated APA-style reporting.
Validating statistical assumptions and diagnostics before performing critical business or scientific modeling.
Planning study designs by calculating required sample sizes and detectable effect sizes.

Acerca de

Características Principales

Comprehensive hypothesis testing including T-tests, ANOVA, Chi-square, and non-parametric alternatives.
Bayesian statistical alternatives for providing Bayes Factors and direct probability statements.
324 GitHub stars
Automated assumption checking with diagnostic visualizations (Q-Q plots, residuals, and box plots).
Built-in power analysis for determining required sample sizes and post-study sensitivity analysis.
Advanced regression modeling with multicollinearity checks (VIF) and diagnostic plotting.

Casos de Uso

Analyzing experimental research data for academic publication with automated APA-style reporting.
Validating statistical assumptions and diagnostics before performing critical business or scientific modeling.
Planning study designs by calculating required sample sizes and detectable effect sizes.