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alterlab-statistical-analysis

Guide researchers through selecting statistical tests and interpreting APA-formatted results.

58|9|Updated Mar 16, 2026
Search Tags:#statistics#data-analysis#statistical-analysis#research-design#power-analysis#apa-formatting
One-click install
npx skills add https://github.com/AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-statistical-analysis
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: alterlab-statistical-analysis
Source: https://github.com/AlterLab-IEU/AlterLab-Academic-Skills/tree/main/skills/data-science/alterlab-statistical-analysis
Command: npx skills add https://github.com/AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-statistical-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?

Researchers in academia often struggle to choose the correct statistical tests, verify assumptions, plan power analyses, and produce APA-formatted reports. This Skill provides a structured, guidance-rich workflow to navigate complex data analyses in academic settings.

Core Features & Use Cases

  • Test selection guidance for t-tests, ANOVA, regression, and correlations.
  • Comprehensive assumption checking with diagnostic guidance and recommended remedies.
  • Power analysis and sample size planning for experimental designs.
  • APA-ready reporting templates and example outputs to streamline manuscript preparation.

Quick Start

Provide a complete statistical analysis plan for a dataset, including test choices, diagnostics, and report-ready results.

Frequently Asked Questions about alterlab-statistical-analysis

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

FAQPage Schema
How do I choose the right statistical test for my academic research data?▼

To choose the right statistical test, this Skill guides you through structured decision trees for selecting t-tests, ANOVA, regression, or correlations based on your data type and research design. It outlines step-by-step decisions to match your specific dataset.

How do I check statistical assumptions and run diagnostics before running an ANOVA or regression?▼

Checking statistical assumptions is handled through comprehensive diagnostic guidance for each recommended test. The Skill outlines necessary assumption checks, identifies potential violations, and recommended remedies to ensure robust analysis.

How do I calculate sample size and perform a power analysis for my experimental design?▼

Calculating sample size and performing a power analysis requires planning your experimental design parameters. This Skill provides dedicated power analysis and sample size planning workflows to help you determine adequate participant numbers.

Can I generate APA-formatted reports for my statistical analysis results?▼

Yes, you can generate APA-formatted reports using the provided APA-ready reporting templates and example outputs. This streamlines manuscript preparation by converting your statistical analysis results into properly formatted academic reporting text.

Do I need pandas and scipy installed to run guided statistical analyses?▼

Yes, you need pandas, scipy, numpy, matplotlib, and seaborn installed. These dependencies support the underlying data manipulation, statistical computation, and visualization required for the academic research analysis workflows.

What's the best way to structure a complete statistical analysis plan for a dataset?▼

The best way to structure a complete statistical analysis plan is to follow a guided workflow that includes test selection, assumption diagnostics, power analysis, and APA-formatted reporting. This Skill helps you generate that comprehensive plan quickly.

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