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statistical-analysis

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

1|Updated Jan 26, 2026
Search Tags:#reporting#data analysis#hypothesis testing#statistics#power analysis#assumption checking
One-click install
npx skills add https://github.com/olavocarvalho/data-agents --skill statistical-analysis-olavocarvalho
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/olavocarvalho/data-agents/tree/main/skills/statistical-analysis
Command: npx skills add https://github.com/olavocarvalho/data-agents --skill statistical-analysis-olavocarvalho

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires scipy, statsmodels, pingouin, pymc, arviz, matplotlib, seaborn, pandas, 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, making rigorous statistical analysis accessible.

Core Features & Use Cases

  • Test Selection: Helps choose the right statistical test based on data and research questions.
  • Assumption Checking: Automates verification of statistical assumptions with visual diagnostics.
  • APA Reporting: Generates publication-ready statistical reports in APA format.
  • Use Case: A researcher needs to compare three experimental groups on a continuous outcome. This Skill guides them to select ANOVA, check assumptions, run the analysis, and generate an APA-style report with effect sizes.

Quick Start

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

Frequently Asked Questions about statistical-analysis

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

FAQPage Schema
How do I generate APA-formatted statistical reports from experimental data?▼

To generate APA-formatted statistical reports, you input experimental data and the Skill guides you through test selection, assumption checking, hypothesis testing, and effect size calculation. It outputs publication-ready text aligned with APA standards.

What is the best way to choose the right statistical test for my research data?▼

Choosing the right statistical test is handled by the Skill's guided test selection feature. It evaluates your data characteristics and research questions to recommend appropriate inferential statistics for valid experimental analysis.

How do I check statistical assumptions and visualize diagnostics in Python?▼

Checking statistical assumptions and visualizing diagnostics is automated by this Skill using Python libraries like scipy and statsmodels. It verifies test prerequisites and generates visual diagnostic plots to confirm data suitability before inferential analysis.

Can I perform Bayesian statistical analysis using pymc and arviz?▼

Yes, you can perform Bayesian statistical analysis using pymc and arviz. These dependencies enable comprehensive inferential modeling, allowing you to run complex hypothesis testing and evaluate posterior distributions within the guided workflow.

Does this statistical analysis tool support ANOVA and effect size calculation?▼

Yes, this statistical analysis tool supports ANOVA and effect size calculation. It leverages the pingouin library to compare multiple experimental groups on continuous outcomes, automatically calculating and reporting effect sizes for the APA report.

Do I need pandas and seaborn installed to run guided hypothesis testing?▼

Yes, you need pandas and seaborn installed to run guided hypothesis testing. Pandas manages data structures for experimental datasets, while seaborn provides the visual diagnostics required for automating assumption checks during the analysis workflow.

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