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statistical-analysis

Guide statistical test selection, assumption checks, and APA-style reporting.

4|1|Updated Jun 18, 2025
Search Tags:#reporting#statistics#assumptions#hypothesis-testing#bayesian#power-analysis#effect-sizes
One-click install
npx skills add https://github.com/HolobiomicsLab/Toolomics --skill statistical-analysis-holobiomicslab
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/HolobiomicsLab/Toolomics/tree/main/mcp_host/skills/scientific-skills/scientific-skills/statistical-analysis
Command: npx skills add https://github.com/HolobiomicsLab/Toolomics --skill statistical-analysis-holobiomicslab

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

Streamlines complex statistical workflows by guiding test selection, assumption checks, effect size calculations, and APA-style reporting, reducing manual guesswork and errors in data analysis.

Core Features & Use Cases

  • Test selection guidance: Helps choose appropriate tests based on data type and design.
  • Diagnostics and assumptions: Automates normality, homogeneity, and linearity checks with actionable recommendations.
  • Effect sizes and reporting: Computes effect sizes and produces publication-ready summaries and APA-style outputs.
  • Breadth of analyses: Supports t-tests, ANOVA, regression, Bayesian methods, and power analyses across common research contexts.
  • Use Case: A researcher planning a study uses this skill to select tests, validate assumptions, run analyses, and generate a full results section.

Quick Start

Run an initial analysis on a sample dataset, perform assumption checks, and generate an APA-style report in one workflow.

Frequently Asked Questions about statistical-analysis

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

FAQPage Schema
How do I automate assumption checks and hypothesis testing in Python?▼

Hypothesis testing requires checking normality and homogeneity assumptions to ensure valid results. This Skill automates these diagnostic checks using pandas and scipy, guiding test selection and providing actionable recommendations for your research data.

What is the best way to generate APA-style reports from statistical analysis?▼

APA-style reporting requires computing effect sizes and formatting statistical summaries for publication. This Skill automates effect size calculations and produces publication-ready APA-style outputs directly from your hypothesis testing results.

How do I select the right statistical test for my research data?▼

Statistical test selection depends on your data type and experimental design. This Skill guides you through choosing appropriate tests like t-tests, ANOVA, or regression based on your specific data structure and research context.

Can I run Bayesian methods and power analysis using scipy and statsmodels?▼

Bayesian methods and power analysis extend traditional hypothesis testing capabilities. This Skill supports Bayesian analysis and power calculations within Python workflows using libraries like pymc, scipy, and statsmodels.

Does this statistical analysis Skill support academic research workflows?▼

Academic research workflows require rigorous hypothesis testing, diagnostics, and effect-size reporting. This Skill is specifically designed for academic research scenarios, streamlining complex statistical workflows from assumption checks to final reporting.

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