skills.rest
tomlupo
tomlupoCOMMUNITY
Explore all skills by this author →
𝕏@tomlupo7GitHub

statistical-analysis

Automate statistical test selection and APA-style reporting from CSV datasets using Python libraries.

Updated Jan 22, 2026
Search Tags:#statistics#regression#hypothesis-testing#power-analysis#Bayesian#APA-reporting
One-click install
npx skills add https://github.com/tomlupo/ai-playground --skill statistical-analysis-tomlupo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/tomlupo/ai-playground/tree/main/.claude/skills/statistical-analysis
Command: npx skills add https://github.com/tomlupo/ai-playground --skill statistical-analysis-tomlupo

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 research often requires selecting the correct statistical tests, validating assumptions, and producing APA-formatted reports. This skill guides users through test selection, diagnostics, and reporting, reducing guesswork and increasing reproducibility.

Core Features & Use Cases

  • Automated test selection based on data type, design, and assumptions (t-tests, ANOVA, regression, correlations, non-parametric alternatives, Bayesian options)
  • Automated assumption checks and diagnostics (normality, homogeneity of variance, linearity) with actionable recommendations
  • Calculation and interpretation of effect sizes, confidence intervals, and applied reporting in APA style
  • Integrated power analysis and sensitivity analysis to support study planning and evaluation
  • Reproducible templates and workflows for typical research analyses

Quick Start

Load a dataset (CSV) with variables of interest, specify your research question, and request an analysis plan to obtain test recommendations, diagnostics, and APA-style results.

Frequently Asked Questions about statistical-analysis

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

FAQPage Schema
How do I automate hypothesis testing and generate APA-style reports from my research data?▼

Automate hypothesis testing and APA-style reports by loading a CSV dataset, specifying your research question, and requesting an analysis plan to obtain automated test recommendations, assumption diagnostics, and formatted results.

How do I select the right statistical test for my dataset and research design?▼

Select the right statistical test through automated matching based on data type, experimental design, and assumption checks, recommending t-tests, ANOVA, regression, correlations, non-parametric alternatives, or Bayesian options.

Can I run power analysis and sensitivity analysis for study planning using Python?▼

Run power analysis and sensitivity analysis for study planning using integrated Python workflows that evaluate required sample sizes and statistical power to support robust research design evaluation.

How do I check statistical assumptions like normality and homogeneity of variance before running tests?▼

Check statistical assumptions like normality, homogeneity of variance, and linearity through automated diagnostics that provide actionable recommendations before proceeding with hypothesis testing or regression.

Does this statistical analysis workflow support Bayesian inference alongside traditional frequentist methods?▼

Bayesian inference is supported alongside traditional frequentist methods, offering Bayesian analysis options and automated reporting using Python libraries like arviz for comprehensive reproducible research outputs.

What's the best way to calculate and interpret effect sizes with confidence intervals for research reporting?▼

Calculate and interpret effect sizes with confidence intervals using automated workflows that apply selected statistical tests, compute effect size metrics, and format the applied reporting in APA style.

Related Skills

Explore More Data & Analytics Skills→
thechandanbhagatthechandanbhagat
8

csv

Parse and transform CSV data with Python and pandas.

Community
Intermediate
Lin-HiLin-Hi
1

seaborn

Create publication-quality statistical visualizations from pandas DataFrames.

Community
Intermediate
jkitchinjkitchin
34

python-plotting

Select and apply Matplotlib, Seaborn, or Plotly for Python visualizations.

Community
Intermediate
diegoscarabellidiegoscarabelli
9

statistical-analysis

Analyze quantitative data with hypothesis testing and Bayesian modeling.

Community
Advanced