statistical-analysis

Design and execute statistical analyses with assumption checks and APA-style reporting.

52|6|Updated Nov 24, 2025

Search Tags:#regression #t-test #power analysis #statistical-analysis #anova

Ready to run

npx skills add https://github.com/ovachiever/droid-tings --skill statistical-analysis

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/ovachiever/droid-tings/tree/main/skills/statistical-analysis
Command: npx skills add https://github.com/ovachiever/droid-tings --skill statistical-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Provides a framework for planning, executing, and reporting statistical analyses with emphasis on assumption checks, effect sizes, and APA-style reporting.

Core Features & Use Cases

Hypothesis testing (t-tests, ANOVA, chi-square) and regression
Assumption checks (normality, homoscedasticity, independence)
Effect sizes and power analyses
APA-compliant reporting and visualization support

Quick Start

Start with common tests (t-test or ANOVA) and generate APA-style results including effect sizes.

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 t-tests and ANOVA on research data?▼

Hypothesis testing with t-tests and ANOVA compares means across groups to determine statistical significance. This Skill automates test selection, execution, assumption verification, and generates effect sizes with APA-style reporting using scipy, statsmodels, and pingouin libraries.

What assumption checks do I need before running statistical tests?▼

Assumption checks validate normality, homoscedasticity, and independence before hypothesis testing. This Skill verifies these prerequisites automatically and flags violations, ensuring test validity and guiding you toward appropriate alternatives when assumptions fail.

How do I calculate effect sizes and perform power analysis for my study?▼

Effect sizes and power analysis quantify practical significance and required sample sizes. This Skill computes effect sizes for all test types and runs power analyses to plan study design, supporting publication requirements and research planning.

Can I generate APA-compliant statistical reports automatically?▼

APA-style reporting formats test results, confidence intervals, and effect sizes to publication standards. This Skill produces ready-to-use reports with visualizations via matplotlib and seaborn, eliminating manual formatting.

Does this work with regression analysis and Bayesian statistics?▼

Regression analysis and Bayesian statistics extend hypothesis testing to continuous predictors and probabilistic inference. This Skill covers both approaches using statsmodels for regression and arviz for Bayesian diagnostics and visualization.