statistical-analysis

Perform statistical hypothesis testing with assumption checks and APA-formatted reporting.

Updated Feb 1, 2026

Search Tags:#data analysis #hypothesis testing #statistics #regression #bayesian #apa reporting

Ready to run

npx skills add https://github.com/nferrer-dev/claude-dotfiles --skill statistical-analysis-nferrer-dev

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/nferrer-dev/claude-dotfiles/tree/main/skills/statistical-analysis
Command: npx skills add https://github.com/nferrer-dev/claude-dotfiles --skill statistical-analysis-nferrer-dev

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scipy, pandas, numpy, matplotlib, seaborn, statsmodels, pingouin, pymc, arviz, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This skill removes the guesswork from statistical analysis by providing a structured, automated path for test selection, assumption verification, and professional-grade reporting.

Core Features & Use Cases

Automated Assumption Checking: Instantly verify normality, homogeneity of variance, and detect outliers using built-in diagnostic scripts.
Guided Test Selection: Navigate complex research questions to identify the correct statistical test, from t-tests to Bayesian models.
Professional Reporting: Generate APA-formatted results and publication-ready visualizations, ensuring your research meets academic standards.
Use Case: A researcher needs to compare three experimental groups but is unsure if the data meets ANOVA assumptions; this skill automates the normality and variance checks and provides the correct post-hoc analysis path.

Quick Start

Use the statistical-analysis skill to perform a comprehensive assumption check on the 'score' column grouped by 'treatment' in my current dataframe.

Frequently Asked Questions about statistical-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I perform hypothesis testing and check assumptions like normality before running an ANOVA?▼

Hypothesis testing requires verifying assumptions like normality and variance homogeneity first. This skill automates these diagnostic checks using scipy and pingouin, then guides you to the correct parametric or non-parametric test and post-hoc analysis path.

How do I generate APA-formatted reports and publication-ready visualizations from statistical test results?▼

APA-formatted reporting translates raw statistical output into academic standard text. This skill generates APA-formatted results and publication-ready visualizations automatically using matplotlib and seaborn after completing your hypothesis tests or regression diagnostics.

Can I run Bayesian analysis workflows using pymc alongside traditional frequentist regression diagnostics?▼

Bayesian analysis workflows are fully supported alongside frequentist methods. The skill uses pymc and arviz for Bayesian modeling while leveraging statsmodels for comprehensive regression diagnostics, allowing you to compare both approaches for your research data.

What is the best way to select the correct statistical test for complex experimental research data?▼

Selecting the correct statistical test involves matching your research question and data structure to the appropriate method. This skill provides guided test selection, navigating from t-tests to advanced models by evaluating your dataframe columns and grouping variables.

Do I need pandas and numpy installed to run statistical analysis on my experimental data?▼

Pandas and numpy are required dependencies for data manipulation and numerical computation. You also need scipy, statsmodels, pingouin, and pymc installed to execute the full range of parametric, non-parametric, and Bayesian analysis workflows.

When should I use non-parametric tests instead of parametric t-tests for my data analysis?▼

Non-parametric tests are necessary when your data fails normality or variance assumptions. This skill automates assumption checking and seamlessly transitions to non-parametric alternatives if your diagnostic checks indicate parametric tests are inappropriate.