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statistical-analysis

Run statistical tests, verify assumptions, and generate APA-style reports.

1|Updated Mar 20, 2026
Search Tags:#hypothesis testing#statistics#regression#bayesian#assumption checking#effect sizes
One-click install
npx skills add https://github.com/jadzoghaib/Sabadell_Capstone --skill statistical-analysis-jadzoghaib
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/jadzoghaib/Sabadell_Capstone/tree/main/.claude/skills/statistical-analysis
Command: npx skills add https://github.com/jadzoghaib/Sabadell_Capstone --skill statistical-analysis-jadzoghaib

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scipy, statsmodels, pingouin, arviz, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill helps researchers and analysts perform accurate statistical tests and generate professional reports without deep statistical expertise.

Core Features & Use Cases

  • Test Selection and Planning: Guides choosing appropriate statistical tests based on research questions and data types.
  • Assumption Checking: Automates verification of normality, homogeneity, and linearity with visualizations and formal tests.
  • Analysis Execution: Performs t-tests, ANOVA, regression, correlations, Bayesian tests, and more with effect size calculations.
  • Reporting and Visualization: Generates APA-style reports, effect sizes with CIs, and publication-ready figures.
  • Use Case: A researcher evaluating treatment effects can automatically check assumptions, run the correct tests, and produce publication-ready results with effect sizes and diagnostics.

Quick Start

Use the statistical-analysis skill to compare group means, check assumptions, and generate a detailed report.

Frequently Asked Questions about statistical-analysis

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

FAQPage Schema
How do I run hypothesis testing and check statistical assumptions automatically?▼

Hypothesis testing with automated assumption checking is achieved by using this Skill to verify normality, homogeneity, and linearity, then executing the appropriate tests and generating publication-ready reports with effect sizes.

Can I use Python to generate APA-style reports with effect sizes for ANOVA and regression?▼

Yes, you can generate APA-style reports with effect sizes and confidence intervals for ANOVA and regression. This Skill leverages statsmodels and pingouin to perform the analysis and format the reporting automatically.

What statistical libraries do I need for Bayesian tests and effect size calculations?▼

You need scipy, statsmodels, pingouin, and arviz installed to perform Bayesian tests, calculate effect sizes, and execute robust statistical workflows. These dependencies provide the necessary backend for accurate analysis.

How do I select the correct statistical test for my research data types?▼

Selecting the correct statistical test is guided by the Skill based on your research questions and data types. It helps you plan the analysis by matching your dataset characteristics to appropriate tests like t-tests, ANOVA, or Bayesian tests.

Is there a tool for biostatistics and social sciences that provides reproducible analysis workflows?▼

Yes, this Skill is designed specifically for biostatistics, social sciences, and academic research workflows. It provides transparent, accurate, and reproducible statistical analysis with guided steps from test selection to reporting.

Best way to verify data normality and homogeneity before running a statistical test?▼

The best way to verify data normality and homogeneity is through automated assumption checking. This Skill performs formal tests and generates visualizations to ensure your data meets the required assumptions before analysis execution.

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