statistical-analysis

Perform statistical tests and generate APA-style reports with Python.

3|Updated Apr 17, 2026

Search Tags:#data science #hypothesis testing #statistical analysis #power analysis #apa reporting

Ready to run

npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill statistical-analysis-ramanebrahimi

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/RamanEbrahimi/raman-marketplace/tree/main/plugins/agentic-research/skills/scientific-skills/statistical-analysis
Command: npx skills add https://github.com/RamanEbrahimi/raman-marketplace --skill statistical-analysis-ramanebrahimi

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill simplifies complex statistical analyses, hypothesis testing, and APA-formatted reporting, providing the necessary tools for data-driven decision-making in academic research.

Core Features & Use Cases

Test Selection and Planning: Offers guidance on choosing appropriate statistical tests and conducting power analyses.
Assumption Checking: Automates checks for assumptions like normality and homogeneity of variance with visual diagnostics.
Statistical Testing: Perform a wide range of tests including t-tests, ANOVA, chi-square, regression, correlation, and Bayesian analyses.
Effect Sizes and Interpretation: Calculates effect sizes and provides confidence intervals for interpreting results.
Professional Reporting: Generates APA-style reports with publication-ready figures and tables.
Use Case: Conduct an independent samples t-test on experimental data to compare the means of two groups and interpret the results for academic publication.

Quick Start

Run the statistical-analysis skill to perform a t-test on your data, comparing the means of Group A and Group B.

Frequently Asked Questions about statistical-analysis

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

FAQPage Schema

How do I perform hypothesis testing and generate APA reports from experimental data?▼

You can perform statistical testing like t-tests and ANOVA, then automatically generate APA-style reports with publication-ready figures and tables. The workflow checks assumptions, calculates effect sizes with confidence intervals, and formats results for academic publication.

Can I run Bayesian analyses using PyMC and ArviZ for statistical inference?▼

Yes, the skill supports Bayesian analyses using PyMC and ArviZ for statistical inference. You can fit Bayesian models, visualize posterior distributions, and run traditional frequentist tests like t-tests and ANOVA within the same workflow.

What's the best way to check normality and homogeneity of variance assumptions before an ANOVA?▼

The skill automates assumption checking for normality and homogeneity of variance with visual diagnostics. It validates these statistical assumptions before running tests like ANOVA or t-tests, ensuring your hypothesis testing results are reliable.

Does this statistical analysis tool support power analysis for test selection and planning?▼

Yes, the skill offers guidance on choosing appropriate statistical tests and conducting power analyses for planning. It helps determine required sample sizes and statistical power before data collection, ensuring robust hypothesis testing and experimental design.

Do I need pandas and scipy installed to calculate effect sizes and confidence intervals?▼

Yes, pandas and scipy are required dependencies along with statsmodels, pingouin, numpy, pymc, arivz, matplotlib, and seaborn. These Python libraries provide the foundational functions for hypothesis testing, effect size calculations, and data visualization.