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statistical-analysis

Guide statistical test selection, assumption checking, and APA-formatted reporting.

1|Updated Jan 14, 2026
Search Tags:#research#reporting#data analysis#hypothesis testing#statistics#bayesian
One-click install
npx skills add https://github.com/Sologa/codex-pipeline --skill statistical-analysis-sologa
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/Sologa/codex-pipeline/tree/main/.codex/skills/statistical-analysis
Command: npx skills add https://github.com/Sologa/codex-pipeline --skill statistical-analysis-sologa

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill simplifies complex statistical analysis, guiding users through test selection, assumption checking, and APA-formatted reporting, making advanced statistical methods accessible for research.

Core Features & Use Cases

  • Test Selection: Recommends appropriate statistical tests based on data and research questions.
  • Assumption Checking: Automates verification of normality, homogeneity of variance, and other assumptions.
  • APA Reporting: Generates publication-ready statistical reports in APA format.
  • Use Case: A researcher needs to compare three experimental groups on a continuous outcome. This Skill helps select ANOVA, checks its assumptions, runs the analysis, and provides an APA-formatted report.

Quick Start

Use the statistical-analysis skill to perform an independent samples t-test on the provided 'group_a.csv' and 'group_b.csv' files.

Frequently Asked Questions about statistical-analysis

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

FAQPage Schema
How do I select the right statistical test for my research data?▼

Assumption checking verifies that your data meets normality and homogeneity of variance requirements before running tests. This Skill automates the validation of statistical assumptions using scipy and pingouin to prevent invalid conclusions from violated test prerequisites.

Can I generate APA-formatted reports for hypothesis testing automatically?▼

APA-formatted reporting structures your statistical results including test statistics, p-values, and effect sizes into publication-ready text. This Skill generates APA-formatted reports directly from your hypothesis testing, correlation, and regression outputs for immediate research publication use.

How do I perform Bayesian analysis using Python for my research?▼

Bayesian analysis in Python uses pymc and arviz to estimate posterior distributions and model uncertainty. This Skill supports Bayesian methods alongside traditional hypothesis testing, allowing you to run probabilistic models and visualize diagnostics for comprehensive research reporting.

Do I need to install specific Python libraries to run power analysis and hypothesis testing?▼

Running power analysis and hypothesis testing requires Python libraries including scipy, statsmodels, pingouin, pymc, and arviz. This Skill depends on these specific packages to execute statistical tests, check assumptions, and generate visualizations for your research data.

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