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statistical-analysis

Analyze quantitative data with hypothesis testing and Bayesian modeling.

9|Updated Feb 26, 2026
Search Tags:#data analysis#hypothesis testing#statistics#regression#bayesian#frequentist
One-click install
npx skills add https://github.com/diegoscarabelli/system2 --skill statistical-analysis-diegoscarabelli
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/diegoscarabelli/system2/tree/main/src/server/agents/skills/statistical-analysis
Command: npx skills add https://github.com/diegoscarabelli/system2 --skill statistical-analysis-diegoscarabelli

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill addresses the challenge of performing methodologically sound quantitative analysis, helping users avoid common statistical fallacies and ensuring rigorous interpretation of data.

Core Features & Use Cases

  • Method Selection: Provides expert guidance on choosing between frequentist and Bayesian approaches based on data structure and research goals.
  • Assumption Validation: Automates the checking of statistical assumptions like normality, homoscedasticity, and independence to ensure result validity.
  • Reporting Standards: Ensures all outputs adhere to professional standards like APA 7th Edition, including effect sizes, confidence intervals, and proper p-value reporting.

Quick Start

Use the statistical-analysis skill to review the assumptions and select the appropriate test for comparing the means of three independent groups in my dataset.

Frequently Asked Questions about statistical-analysis

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

FAQPage Schema
How do I choose between Bayesian and frequentist frameworks for statistical hypothesis testing?▼

Statistical hypothesis testing framework selection depends on your data structure and research goals. Method selection guidance evaluates your quantitative research parameters to recommend either Bayesian modeling or frequentist approaches for valid inferences.

What is the best way to check statistical assumptions like normality and homoscedasticity before regression analysis?▼

Checking statistical assumptions like normality and homoscedasticity is automated through systematic diagnostic checking. Assumption validation ensures your regression analysis and quantitative modeling meet rigorous reporting standards before generating results.

How do I report effect sizes and confidence intervals according to APA 7th Edition standards?▼

Reporting effect sizes and confidence intervals according to APA 7th Edition standards requires adherence to professional formatting rules. Statistical analysis outputs enforce these reporting standards automatically for all quantitative inferences.

Can I use this statistical analysis approach for data engineering projects instead of just academic research?▼

Statistical analysis for data engineering projects is fully supported alongside quantitative research. Comprehensive methodological review and assumption validation apply to both contexts, ensuring rigorous quantitative analysis across data structures.

Why does my statistical inference fail validity checks when comparing independent group means?▼

Statistical inference validity fails when assumptions like independence and homoscedasticity are unmet. Systematic diagnostic checking validates these assumptions for independent group comparisons, preventing common statistical fallacies in hypothesis testing.

When do I need Bayesian modeling instead of standard regression for quantitative analysis?▼

Bayesian modeling is needed for quantitative analysis when your data structure requires probabilistic inference rather than point estimates. Method selection guidance evaluates your research goals to determine if Bayesian approaches outperform frequentist regression.

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