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statistical-analysis

Select statistical tests, check assumptions, and generate APA-style results.

1|Updated Apr 12, 2026
Search Tags:#python#assumption-checking#statistical-analysis#statsmodels#power-analysis#test-selection#apa-reporting
One-click install
npx skills add https://github.com/wrayre/Agentic_Coding_Workshop --skill statistical-analysis-wrayre
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/wrayre/Agentic_Coding_Workshop/tree/main/.github/skills/statistical-analysis
Command: npx skills add https://github.com/wrayre/Agentic_Coding_Workshop --skill statistical-analysis-wrayre

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Statistical-analysis guides researchers in choosing appropriate statistical tests, performing assumption checks, powering analyses, and generating APA-style results, reducing misapplication and errors.

Core Features & Use Cases

  • Test selection guidance: Recommend tests based on data type and study design.
  • Assumption checks: Automated checks with visualizations and actionable recommendations.
  • Reporting: APA-style result templates and publication-ready figures.
  • Use Case: A researcher comparing two groups uses the skill to select the correct test, verify assumptions, compute effect sizes, and produce a ready results narrative.

Quick Start

Run an end-to-end statistical analysis workflow on your dataset: select tests, run assumptions checks, and generate APA-style reporting.

Frequently Asked Questions about statistical-analysis

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

FAQPage Schema
How do I choose the right statistical test for my dataset and study design?▼

Choosing the right statistical test requires matching your data type and study design to the appropriate method. This skill recommends tests based on your specific data structure, verifying assumptions to reduce misapplication and errors.

How do I run assumption checks and calculate effect sizes in Python before reporting results?▼

Run assumption checks using Python libraries like scipy and statsmodels to validate your data before analysis. The skill automates these checks with visualizations, computes effect sizes, and ensures correct interpretation to support robust statistical analysis.

Can I generate APA-style results and publication-ready figures using pandas and scipy?▼

Yes, you can generate APA-style results and publication-ready figures using scipy, pandas, and seaborn. The skill produces APA-style result templates and reproducible reports, creating a ready results narrative for publication.

Does this statistical analysis workflow support power analysis across different study designs?▼

Yes, the statistical analysis workflow supports power analysis across different study designs and data types. It applies these analyses to support power calculations, effect size reporting, and APA-style results for robust data analysis.

What is the best way to automate statistical test selection and assumption checking for research data?▼

The best way to automate statistical test selection and assumption checking is to use a guided workflow that evaluates data types and study designs. This skill automates checks with visualizations and provides actionable recommendations to reduce errors.

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