statistical-analysis

Name: statistical-analysis
Availability: InStock
Author: jasrajtulsi

Select appropriate statistical tests and check assumptions for research data.

Updated Jul 1, 2026

Search Tags:#hypothesis testing #statistical analysis #power analysis #bayesian statistics #apa reporting #assumption checking #effect sizes

Ready to run

npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill statistical-analysis-jasrajtulsi

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: statistical-analysis
Source: https://github.com/jasrajtulsi/GRAD-SCOPE/tree/main/.claude/skills/statistical-analysis
Command: npx skills add https://github.com/jasrajtulsi/GRAD-SCOPE --skill statistical-analysis-jasrajtulsi

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill helps you choose, validate, run, and report statistical analyses without guessing which test fits your data. It reduces mistakes from assumption violations, weak effect-size reporting, and incomplete results writeups.

Core Features & Use Cases

Guides test selection for t-tests, ANOVA, correlation, regression, chi-square, and Bayesian alternatives.
Checks assumptions such as normality, homogeneity of variance, linearity, and outliers before interpretation.
Calculates effect sizes, power, and APA-style reporting details for research papers and lab analyses.
Example: use it when you have grouped experimental data and need to decide between a standard t-test, Welch correction, or a nonparametric alternative, then write the results correctly.

Quick Start

Ask the Skill to review your dataset, choose the appropriate statistical test, check assumptions, and draft an APA-style results summary.

Frequently Asked Questions about statistical-analysis

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

FAQPage Schema

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

Choosing the right statistical test requires checking assumptions like normality and variance. This Skill evaluates your dataset to select appropriate tests like t-tests, ANOVA, or Bayesian alternatives, then validates assumptions before interpretation.

How do I report statistical results in APA format?▼

To report statistical results in APA format, you need effect sizes, test statistics, and p-values. This Skill processes your experimental data and generates APA-style reporting details, ensuring your academic writeups meet publication standards.

What statistical assumptions should I check before running hypothesis testing?▼

Statistical assumption checking involves verifying normality, homogeneity of variance, linearity, and outliers before hypothesis testing. This Skill runs these diagnostics automatically using scipy and statsmodels to prevent interpretation errors.

Can I run Bayesian statistics as an alternative to standard hypothesis tests?▼

Yes, Bayesian statistics can serve as an alternative to standard hypothesis tests. This Skill uses pymc and arviz to run Bayesian alternatives when traditional tests are invalid or when you need robust probability estimates.

How do I calculate effect sizes and perform power analysis?▼

Calculating effect sizes and performing power analysis requires test statistics and variance estimates. This Skill computes these metrics using pandas and scipy, providing quantitative measures of magnitude alongside your hypothesis test results.

Does statistical analysis work with grouped experimental data in pandas?▼

Yes, statistical analysis works seamlessly with grouped experimental data in pandas. This Skill evaluates grouped datasets to decide between standard tests, Welch corrections, or nonparametric alternatives using numpy and scipy.