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statistical-analysis

Run statistical analyses and generate APA-style reports from pandas DataFrames.

3|Updated Dec 3, 2025
Search Tags:#statistics#regression#ANOVA#hypothesis-testing#power-analysis#APA-reporting
One-click install
npx skills add https://github.com/braselog/researchAssistant --skill statistical-analysis-braselog
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/braselog/researchAssistant/tree/main/.ra/skills/statistical-analysis
Command: npx skills add https://github.com/braselog/researchAssistant --skill statistical-analysis-braselog

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides a guided toolkit for performing rigorous statistical analyses in research projects, helping users design studies, run appropriate tests, and report results in APA style with clear effect sizes and diagnostics.

Core Features & Use Cases

  • Test selection & Assumption checks: Guidance to choose the correct statistical tests and diagnose data assumptions.
  • Common analyses: T-tests, ANOVA, regression, correlation, Bayesian methods, and power analysis with reproducible workflows.
  • Reporting: APA-formatted reporting templates, confidence intervals, and effect sizes for results.
  • Use Case: Suppose you collected a dataset with multiple groups and continuous outcomes; this Skill guides you from test choice through reporting in a coherent Methods and Results section.

Quick Start

  • Load your dataset (CSV) into a dataframe.
  • Choose a test using the workflow prompts and run the corresponding analyses.
  • Generate APA-formatted summaries and export results to a CSV or Markdown section.

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 report results in APA style using Python?▼

To perform regression analysis and check data assumptions, load your CSV data into a pandas DataFrame. This skill uses statsmodels and pingouin to run regression models, diagnose assumptions, and generate reproducible workflows with clear diagnostics.

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

Choosing the correct statistical test is handled through guided workflow prompts that evaluate your data structure. This skill provides test selection guidance and assumption checks to ensure you apply the appropriate statistical method for your specific research design.

Can I perform power analysis and Bayesian statistics with pandas DataFrames?▼

Yes, you can perform power analysis and Bayesian statistics with pandas DataFrames. This skill integrates the pingouin library to conduct power analysis and Bayesian methods, ensuring your study planning and data analysis remain fully reproducible.

Does this statistical analysis workflow support exporting results to Markdown?▼

Yes, the statistical analysis workflow supports exporting results to Markdown. After running your analyses and generating APA-formatted reporting templates, you can export the final results to a CSV file or a Markdown section for your research paper.

How do I calculate effect sizes and confidence intervals for an ANOVA?▼

To calculate effect sizes and confidence intervals for an ANOVA, use the integrated analysis functions powered by statsmodels and pingouin. This skill computes the necessary metrics and embeds them directly into your APA-formatted reporting summaries.

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