data-analysis

Analyze datasets with assumption-checked hypothesis tests and effect sizes.

31|8|Updated Apr 10, 2026

Search Tags:#data visualization #pandas #hypothesis testing #statistics #reproducible research #effect size #scipy

Ready to run

npx skills add https://github.com/itallstartedwithaidea/agent-skills --skill data-analysis-itallstartedwithaidea

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-analysis
Source: https://github.com/itallstartedwithaidea/agent-skills/tree/main/skills/scientific-research/data-analysis
Command: npx skills add https://github.com/itallstartedwithaidea/agent-skills --skill data-analysis-itallstartedwithaidea

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Data-analysis turns messy datasets into defensible statistical conclusions with correctly matched hypothesis tests and figures that communicate results clearly.

Core Features & Use Cases

Assumption-checked hypothesis testing: validates normality and homoscedasticity before choosing parametric tests, and switches to non-parametric alternatives when assumptions fail.
Quantified evidence: reports effect sizes (Cohen’s d / eta-squared) alongside p-values so significance is interpreted with magnitude.
Reproducible, publication-quality visualization: generates static journal-ready plots (matplotlib/seaborn) and interactive exploratory plots (plotly) with accessible styling and export-ready formats.

Quick Start

Use the data-analysis skill to analyze your CSV file and produce assumption-checked statistical comparisons and figures suitable for a research report.

Frequently Asked Questions about data-analysis

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

FAQPage Schema

How do I run hypothesis testing in Python while automatically checking normality and variance assumptions?▼

Hypothesis testing in Python requires validating normality and homoscedasticity before selecting parametric tests. This analysis skill automatically checks distribution assumptions and switches to non-parametric alternatives like Mann-Whitney or Kruskal-Wallis when assumptions fail, ensuring defensible statistical conclusions.

How do I calculate effect sizes with confidence intervals alongside p-values for a research report?▼

Calculating effect sizes with confidence intervals requires computing Cohen's d or eta-squared alongside p-values to interpret significance with magnitude. This skill quantifies evidence by reporting effect sizes, ensuring statistical significance is evaluated with practical importance for publication-ready documentation.

What is the best way to generate publication-quality statistical plots from a CSV file using pandas and seaborn?▼

Generating publication-quality statistical plots from CSV files involves using pandas for data ingestion and seaborn/matplotlib for visualization. This skill produces journal-ready static figures with accessible styling and export-ready formats, plus optional interactive plotly plots for exploratory analysis.

Does scipy support reproducible research workflows with assumption-checked ANOVA and t-tests?▼

Scipy supports reproducible research workflows by enabling assumption-checked ANOVA and t-tests. This skill uses scipy to validate test assumptions before choosing parametric tests, automatically switching to non-parametric alternatives when needed, and outputs reproducible documentation with every analysis.

When should I use non-parametric tests like Kruskal-Wallis instead of ANOVA for my dataset?▼

Non-parametric tests like Kruskal-Wallis should be used instead of ANOVA when normality or homoscedasticity assumptions fail. This skill assesses distribution assumptions during exploratory analysis and automatically selects the appropriate non-parametric alternative to ensure valid statistical inference.

Can I perform exploratory data analysis and statistical inference on observational datasets using pandas and numpy?▼

Exploratory data analysis and statistical inference on observational datasets can be performed using pandas and numpy. This skill handles data ingestion, cleaning, distribution assessment, and hypothesis testing, applying structured statistical analysis for inference and effect quantification across grouped comparisons.