selecting-statistical-test

Select the appropriate statistical test from study design, measurement scale, sample size, and assumption checks.

2|Updated May 23, 2026

Search Tags:#hypothesis testing #anova #wilcoxon #statistical test #welch t-test #fisher exact

Ready to run

npx skills add https://github.com/rocklambros/rcs --skill selecting-statistical-test

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: selecting-statistical-test
Source: https://github.com/rocklambros/rcs/tree/main/skills/ml-datasci/selecting-statistical-test
Command: npx skills add https://github.com/rocklambros/rcs --skill selecting-statistical-test

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps users choose the correct statistical test before analysis, preventing invalid conclusions from mismatched designs, scales, or assumption checks.

Core Features & Use Cases

Guides the decision tree from study design, measurement scale, sample size, and assumption checks to the correct parametric or nonparametric test.
Handles common scenarios such as paired before-after data, independent group comparisons, sparse 2x2 contingency tables, one-sample tests, and repeated-measures designs.
Explains why a test is recommended by naming the gating check that determined the choice, making the result defensible and easy to audit.
Useful for data scientists, statistics students, instructors, and ML engineers who need to pick a valid test quickly and correctly.

Quick Start

Ask which statistical test fits your design, scale, sample size, and assumption-check results, and it will return the recommended test with the reason it was chosen.

Frequently Asked Questions about selecting-statistical-test

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

FAQPage Schema

How do I choose the right statistical test for my study design and data?▼

To choose the right statistical test, you evaluate your study design, measurement scale, sample size, and assumption-check results like normality and equal variance to select a valid parametric or nonparametric test.

When should I use a nonparametric test instead of a parametric test?▼

Use a nonparametric test when your data fails assumption checks for normality or equal variance. The selection process applies explicit gating checks to justify switching from parametric to nonparametric methods.

What statistical test should I use for paired before-after data?▼

For paired before-after data, the recommended statistical test depends on the measurement scale and normality checks. The decision tree evaluates these explicit gating conditions to recommend the correct paired test.

How do I select a test for sparse 2x2 contingency tables with categorical data?▼

For sparse 2x2 contingency tables with categorical data, the test selection uses expected cell counts as a gating check to recommend the appropriate exact test, ensuring valid conclusions for binary data.

Can this test selection method handle one-sample and repeated-measures scenarios?▼

Yes, this test selection method handles one-sample and repeated-measures scenarios by mapping the study design and measurement scale to appropriate statistical tests, including continuous, ordinal, and binary data.

Why does the recommended statistical test include an explanation of the choice?▼

The recommended statistical test includes an explanation by naming the specific gating check, such as normality or expected cell counts, that determined the choice. This makes the result defensible and easy to audit.