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statistical-analysis

Guide statistical analysis workflows with test selection and APA-style reporting.

Updated Apr 2, 2026
Search Tags:#diagnostics#statistics#hypothesis-testing#bayesian#power-analysis#effect-sizes
One-click install
npx skills add https://github.com/viniruggeri/applied-dynamical-systems --skill statistical-analysis-viniruggeri
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/viniruggeri/applied-dynamical-systems/tree/main/.agents/skills/statistical-analysis
Command: npx skills add https://github.com/viniruggeri/applied-dynamical-systems --skill statistical-analysis-viniruggeri

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Guided statistical analysis with test selection, diagnostics, and APA-formatted reporting to simplify rigorous research workflows.

Core Features & Use Cases

  • Automated test selection for t-tests, ANOVA, regression, correlation, and Bayesian analyses
  • Comprehensive assumption checks and visual diagnostics
  • Power analyses, sample-size planning, and publication-ready reporting templates

Quick Start

Ask me to generate a complete analysis plan for a dataset, including test choice, diagnostics, and reporting templates.

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 with assumption checks and power analysis in Python?▼

Hypothesis testing workflows with assumption checks and power analysis are executed by integrating scipy, statsmodels, and pingouin, providing automated test selection and visual diagnostics for academic research.

What is the best way to generate APA-style reports for t-tests, ANOVA, and regression?▼

APA-style reporting templates for t-tests, ANOVA, and regression are generated through guided statistical analysis workflows, producing publication-ready outputs alongside effect sizes and comprehensive diagnostics.

Can I use scipy and pandas to perform Bayesian analysis and calculate effect sizes?▼

Yes, Bayesian analyses and effect size calculations are supported using pandas for data handling and scipy for statistical operations, integrated into guided workflows that cover comprehensive test selection and diagnostics.

How do I plan sample size and conduct power analysis for academic research?▼

Sample-size planning and power analysis for academic research are conducted through guided statistical workflows, utilizing Python dependencies like numpy and scipy to compute required parameters before data collection.

Does this statistical analysis workflow include visual diagnostics for checking data assumptions?▼

Yes, visual diagnostics for checking data assumptions are included using matplotlib and seaborn, providing comprehensive visual checks alongside automated test selection and statistical reporting.

When should I use Bayesian methods instead of traditional hypothesis testing in my analysis?▼

Bayesian methods provide an alternative to traditional hypothesis testing when prior distributions need to be incorporated, with guided workflows supporting both approaches alongside effect sizes and power analysis for robust research.

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