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statistical-analysis

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

Updated Mar 10, 2026
Search Tags:#reporting#data analysis#hypothesis testing#statistics#power analysis#bayesian statistics
One-click install
npx skills add https://github.com/Yezez9/Research-Agent --skill statistical-analysis-yezez9
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/Yezez9/Research-Agent/tree/main/scientific-skills/statistical-analysis
Command: npx skills add https://github.com/Yezez9/Research-Agent --skill statistical-analysis-yezez9

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill simplifies complex statistical analysis by guiding users through test selection, assumption checking, execution, and APA-formatted reporting.

Core Features & Use Cases

  • Test Selection: Recommends appropriate statistical tests based on research questions and data.
  • Assumption Checking: Automates verification of normality, homogeneity of variance, and other assumptions.
  • Analysis Execution: Performs t-tests, ANOVA, regression, and Bayesian analyses using robust libraries.
  • Reporting: Generates publication-ready APA-style reports with effect sizes and confidence intervals.
  • Use Case: A researcher has collected data for a three-group experiment and needs to choose the correct ANOVA, check assumptions, run the analysis, and report the results in APA format.

Quick Start

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

Frequently Asked Questions about statistical-analysis

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

FAQPage Schema
How do I generate APA-formatted reports for hypothesis testing in Python?▼

To generate APA-formatted reports for hypothesis testing, this Skill executes your statistical tests and outputs publication-ready text with effect sizes and confidence intervals. It guides you through test selection, assumption checking, and analysis execution.

What is the best way to choose the correct statistical test for my research data?▼

The best way to choose the correct statistical test is to let the Skill recommend appropriate methods based on your research questions and data. It evaluates your inputs to suggest the right t-test, ANOVA, regression, or Bayesian analysis.

How do I check normality and homogeneity of variance assumptions before running an ANOVA?▼

Checking normality and homogeneity of variance assumptions before running an ANOVA is automated by this Skill. It verifies necessary statistical assumptions directly using libraries like scipy and pingouin before executing the core analysis.

Can I perform Bayesian statistics and power analysis using scipy and statsmodels?▼

You can perform Bayesian statistics and power analysis using pymc and arviz alongside scipy and statsmodels. This Skill integrates these dependencies to provide comprehensive statistical operations, diagnostics, and power calculations for your research workflows.

Do I need to install pingouin and pymc to run statistical analysis and reporting?▼

Yes, you need to install pingouin and pymc, along with scipy, statsmodels, and arviz, to run statistical analysis and reporting. These dependencies are required for the Skill to execute its comprehensive statistical operations and diagnostics.

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