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Ritabrata-Chakraborty
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statistical-analysis

Automate statistical test selection and execution for hypothesis testing.

Updated Jun 6, 2026
Search Tags:#data visualization#hypothesis testing#statistical analysis#apa reporting#effect sizes
One-click install
npx skills add https://github.com/Ritabrata-Chakraborty/Claude-Setup --skill statistical-analysis-ritabrata-chakraborty
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/Ritabrata-Chakraborty/Claude-Setup/tree/main/skills/statistical-analysis
Command: npx skills add https://github.com/Ritabrata-Chakraborty/Claude-Setup --skill statistical-analysis-ritabrata-chakraborty

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill streamlines the process of statistical analysis, including hypothesis testing, data visualization, and APA-style reporting.

Core Features & Use Cases

  • Test Selection and Planning: Automatically select appropriate statistical tests based on research questions and data characteristics.
  • Assumption Checking: Conduct comprehensive checks on assumptions like normality and homogeneity of variance before running tests.
  • Statistical Testing: Perform a wide range of hypothesis tests, including t-tests, ANOVA, chi-square, non-parametric alternatives, regression, and Bayesian analyses.
  • Effect Sizes and Interpretation: Calculate and interpret effect sizes, confidence intervals, and statistical significance.
  • Professional Reporting: Generate APA-style statistical reports with tables and figures for publication-ready results.
  • Use Case: Imagine you are conducting a research study with experimental and observational data. Use this Skill to test hypotheses, analyze relationships, calculate effect sizes, and generate comprehensive APA-style reports for your findings.

Quick Start

Run the statistical-analysis skill with the 'test_selection' command to automatically select the appropriate statistical test for your data.

Frequently Asked Questions about statistical-analysis

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

FAQPage Schema
How do I automate statistical test selection for hypothesis testing in Python?▼

Automate statistical test selection by running the test_selection command to evaluate research questions and data characteristics. This automatically identifies and executes the appropriate hypothesis tests for your dataset.

Can I generate APA-style reports with effect sizes and confidence intervals automatically?▼

Generate APA-style reports complete with calculated effect sizes, confidence intervals, and statistical significance. The skill produces publication-ready tables and figures directly from your hypothesis testing results.

Does this statistical analysis approach check data assumptions like normality before running tests?▼

This statistical analysis approach conducts comprehensive assumption checking for normality and homogeneity of variance before executing tests. These checks ensure the selected hypothesis tests are valid for your data.

What Python libraries are required for comprehensive statistical analysis and Bayesian modeling?▼

Comprehensive statistical analysis and Bayesian modeling require pingouin, scipy, statsmodels, pandas, matplotlib, seaborn, pymc, and arviz. These dependencies support a wide range of hypothesis tests and data visualization.

How do I perform non-parametric alternatives and regression analysis for academic research?▼

Perform non-parametric alternatives and regression analysis by using the skill's automated statistical testing capabilities. It handles a wide range of tests suitable for academic research in psychology, economics, and other fields.

What is the best way to conduct statistical analysis and data visualization for experimental data?▼

The best way to conduct statistical analysis on experimental data is using a skill that automates test selection, assumption checking, and visualization. It applies pandas, seaborn, and matplotlib to generate comprehensive reports.

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