skills.rest
ManfronEnrico
ManfronEnricoCOMMUNITY
Explore all skills by this author →
GitHub

stats-analysis

Select and apply statistical tests with APA-formatted results using Python libraries.

Updated Mar 21, 2026
Search Tags:#statistics#data-analysis#assumption-checking#hypothesis-testing#power-analysis#apa-reporting#statistical-models
One-click install
npx skills add https://github.com/ManfronEnrico/thesis-manifold --skill stats-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: stats-analysis
Source: https://github.com/ManfronEnrico/thesis-manifold/tree/main/.claude/skills/stats-analysis
Command: npx skills add https://github.com/ManfronEnrico/thesis-manifold --skill stats-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Statistical analysis planning and reporting can be error-prone and time-consuming; this skill guides researchers to select appropriate tests, perform assumption checks, and produce APA-compliant results.

Core Features & Use Cases

  • Guided test selection for t-tests, ANOVA, regression, correlation, and non-parametric alternatives.
  • Automated assumption checks (normality, homogeneity, independence) with visual diagnostics and actionable recommendations.
  • Power analysis, sample size planning, and effect-size computation with APA-style reporting templates.
  • Use Case: A researcher can plan a study, choose the right tests, and generate a complete manuscript-ready results section.

Quick Start

Provide your dataset and specify the outcome and predictor variables to receive guidance on test selection, diagnostics, and APA-style reporting.

Frequently Asked Questions about stats-analysis

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

FAQPage Schema
How do I perform statistical analysis and generate APA-formatted results in Python?▼

Statistical analysis with APA-formatted reporting is performed by providing a dataset and specifying predictor variables to receive automated test selection, assumption checks, and manuscript-ready results using scipy and statsmodels.

How do I run assumption checks for normality and homogeneity before hypothesis testing?▼

Assumption checks for normality and homogeneity are automated by applying visual diagnostics and actionable recommendations across your dataset, ensuring data meets requirements before hypothesis testing and generating APA-style reports.

Can I use pandas and scipy for power analysis and sample size planning?▼

Power analysis and sample size planning are supported using Python libraries like scipy and pingouin, allowing researchers to compute effect sizes and generate APA-style reporting templates for study designs.

What is the best way to choose between t-tests, ANOVA, and non-parametric alternatives?▼

Choosing between t-tests, ANOVA, and non-parametric alternatives is handled through guided test selection that evaluates study designs and dataset variables to apply the appropriate statistical models and diagnostics.

Does this statistical analysis approach work with regression and correlation models?▼

Statistical analysis for research reporting works with regression and correlation models by applying appropriate tests, enforcing reporting standards, and computing effect sizes using statsmodels and arviz.

Related Skills

Explore More Data & Analytics Skills→
tondevreltondevrel
19

seaborn

Create statistical graphics from Pandas DataFrames using a high-level Python interface.

Community
Intermediate
Clay-HHKClay-HHK
2

statistical-analysis

Automate statistical analysis planning and APA-style reporting for research data.

Community
Advanced
AlterLab-IEUAlterLab-IEU
58

alterlab-seaborn

Create statistical visualizations from DataFrame inputs using seaborn plot families.

Community
Advanced
hxk622hxk622
8

pymc

Build Bayesian models with PyMC for MCMC sampling and predictive checks.

Community
Advanced