# Statistical Analysis

Guided statistical analysis with test selection and reporting.

- Repository: https://github.com/zLanqing/codex-claude-academic-skills (5550 stars)
- Category: Testing
- License: MIT license
- Last updated: 2026-05-14
- Runs scripts: yes, review before use

## Install

Claude Code: `npx skills add zLanqing/codex-claude-academic-skills --skill statistical-analysis -g -a claude-code -y`
Codex: `npx skills add zLanqing/codex-claude-academic-skills --skill statistical-analysis -g -a codex -y`
Cursor: `npx skills add zLanqing/codex-claude-academic-skills --skill statistical-analysis -g -a cursor -y`
Gemini CLI: `npx skills add zLanqing/codex-claude-academic-skills --skill statistical-analysis -g -a gemini-cli -y`

## Description

Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# Statistical Analysis

## Overview

Statistical analysis is a systematic process for testing hypotheses and quantifying relationships. Conduct hypothesis tests (t-test, ANOVA, chi-square), regression, correlation, and Bayesian analyses with assumption checks and APA reporting. Apply this skill for academic research.

## When to Use This Skill

This skill should be used when:
- Conducting statistical hypothesis tests (t-tests, ANOVA, chi-square)
- Performing regression or correlation analyses
- Running Bayesian statistical analyses
- Checking statistical assumptions and diagnostics
- Calculating effect sizes and conducting power analyses
- Reporting statistical results in APA format
- Analyzing experimental or observational data for research

---

## Core Capabilities

### 1. Test Selection and Planning
- Choose appropriate statistical tests based on research questions and data characteristics
- Conduct a priori power analyses to determine required sample sizes
- Plan analysis strategies including multiple comparison corrections

### 2. Assumption Checking
- Automatically verify all relevant assumptions before running tests
- Provide diagnostic visualizations (Q-Q plots, residual plots, box plots)
- Recommend remedial actions when assumptions are violated

### 3. Statistical Testing
- Hypothesis testing: t-tests, ANOVA, chi-square, non-parametric alternatives
- Regression: linear, multiple, logistic, with diagnostics
- Correlations: Pearson, Spearman, with confidence intervals
- Bayesian alternatives: Bayesian t-tests, ANOVA, regression with Bayes Factors

### 4. Effect Sizes and Interpretation
- Calculate and interpret appropriate effect sizes for all analyses
- Provide confidence intervals for effect estimates
- Distinguish statistical from practical significance

### 5. Professional Reporting
- Generate APA-style statistical reports
- Create publication-ready figures and tables
- Provide complete interpretation with all required statistics

---

The rest: https://github.com/zLanqing/codex-claude-academic-skills/blob/HEAD/scientific-toolkit-skill/references/scientific-skills/statistical-analysis/SKILL.md