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statistical-analysis

Automate statistical hypothesis testing, regression, and Bayesian inference with Python libraries.

Updated May 17, 2026
Search Tags:#hypothesis testing#regression#statistical analysis#correlation#Bayesian analysis#APA reporting
One-click install
npx skills add https://github.com/galeep/plugin-place --skill statistical-analysis-galeep
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/galeep/plugin-place/tree/main/plugins/sci-data-analysis-viz/skills/statistical-analysis
Command: npx skills add https://github.com/galeep/plugin-place --skill statistical-analysis-galeep

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill simplifies the process of conducting statistical analysis, helping users choose appropriate tests, check assumptions, and generate APA-formatted reports.

Core Features & Use Cases

  • Test Selection: Guided selection of statistical tests based on data characteristics and research questions.
  • Assumption Checking: Automated verification of assumptions before running tests, with visual diagnostics.
  • Statistical Testing: Perform hypothesis tests, regression, correlation, and Bayesian analyses.
  • Reporting: Generate APA-style statistical reports with visualizations and interpretations.
  • Use Case: A researcher wants to analyze the effect of a new drug on patient recovery times. This Skill guides the user through selecting an appropriate test, checking assumptions, performing the analysis, and generating a professional report.

Quick Start

Run the statistical-analysis skill to conduct a t-test comparing the mean recovery times of patients treated with the new drug versus the control group.

Frequently Asked Questions about statistical-analysis

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

FAQPage Schema
How do I run hypothesis testing and regression analysis in Python with APA reporting?▼

Hypothesis testing and regression analysis can be automated using Python libraries like scipy and statsmodels, which handle data processing and generate APA-formatted statistical reports with visual diagnostics and interpretations.

What's the best way to check statistical assumptions before running a hypothesis test?▼

Automated assumption checking verifies data conditions before running statistical tests, providing visual diagnostics to ensure your dataset meets the requirements for accurate hypothesis testing and Bayesian inference.

Can I perform Bayesian analysis using pymc and arviz for academic research?▼

Bayesian analysis for academic research and experimental design is supported through pymc and arviz, allowing you to conduct comprehensive inference and generate professional diagnostic reports across various data structures.

Does this statistical analysis tool support guided test selection based on data characteristics?▼

Guided statistical test selection evaluates your data characteristics and research questions to recommend appropriate hypothesis tests, ensuring you apply the correct analytical method before processing begins.

How do I generate APA-style statistical reports from correlation and Bayesian inference results?▼

APA-style statistical reports are generated automatically after performing correlation, regression, or Bayesian inference, combining visualizations and result interpretations into a professional document for academic research.

What libraries do I need for statistical analysis including pingouin and statsmodels?▼

Statistical analysis requires Python libraries including pingouin, scipy, statsmodels, pymc, and arviz to execute hypothesis testing, regression analysis, and Bayesian inference with comprehensive diagnostics.

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