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statistical-analysis

Guide statistical analysis with test selection, assumption checks, and APA reporting.

Updated Mar 10, 2026
Search Tags:#reporting#data analysis#hypothesis testing#statistics#regression#bayesian
One-click install
npx skills add https://github.com/felixboehm/biochem-allergy --skill statistical-analysis-felixboehm
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/felixboehm/biochem-allergy/tree/main/.claude/skills/statistical-analysis
Command: npx skills add https://github.com/felixboehm/biochem-allergy --skill statistical-analysis-felixboehm

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill simplifies complex statistical analysis by guiding users through test selection, assumption checking, execution, and APA-formatted reporting.

Core Features & Use Cases

  • Test Selection: Recommends appropriate statistical tests based on data and research questions.
  • Assumption Checking: Automates verification of normality, homogeneity of variance, etc., with visualizations and recommendations.
  • Analysis Execution: Performs t-tests, ANOVA, regression, correlation, and Bayesian analyses.
  • Reporting: Generates publication-ready APA-style results, including effect sizes and confidence intervals.
  • Use Case: A researcher needs to compare three experimental groups on a continuous outcome. This Skill helps choose ANOVA, checks assumptions, runs the analysis, and provides an APA-formatted report with post-hoc tests and effect sizes.

Quick Start

Use the statistical-analysis skill to perform an independent samples t-test on the 'scores.csv' dataset, comparing 'group_a' and 'group_b' columns.

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 and get APA-formatted reporting for my research data?▼

Hypothesis testing with APA-formatted reporting is executed by guiding you through test selection, assumption checking, and analysis execution to generate publication-ready results with effect sizes and confidence intervals.

What is the best way to check statistical assumptions like normality and homogeneity of variance before running an ANOVA?▼

Checking statistical assumptions like normality and homogeneity of variance is automated with visualizations and diagnostic recommendations, ensuring your data meets parametric test requirements before running an ANOVA.

Can I perform Bayesian analysis and regression using pandas and statsmodels?▼

Bayesian analysis and regression can be performed using pandas, statsmodels, and pymc, supporting both parametric and Bayesian frameworks to execute tests and calculate effect sizes for data-driven decision-making.

How do I select the right statistical test for comparing multiple experimental groups on a continuous outcome?▼

Selecting the right statistical test for comparing multiple experimental groups is handled by recommending appropriate methods like ANOVA based on your data structure and research questions, followed by post-hoc tests and effect size calculation.

Does this statistical analysis approach support power analysis and effect size calculation for academic research?▼

Power analysis and effect size calculation are fully supported for academic research, ensuring methodological rigor and valid inference alongside execution of parametric and Bayesian tests with clear interpretation of results.

When should I not use parametric tests for my data analysis, and what are the limitations?▼

Parametric tests should not be used when assumption checking fails to verify normality or homogeneity of variance, requiring adherence to diagnostic procedures and potentially switching to Bayesian analysis or non-parametric alternatives for valid inference.

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