Module 4 · Lesson 16 of 22
Non-parametric comparison with Kruskal-Wallis
Listen to this article
Ready
100% Free
This lesson and the entire Lean Six Sigma Black Belt academy are free of charge — no fees, ever.
Learning objectives
- Recognise when Kruskal-Wallis is appropriate.
- Interpret a rank-based result.
- State its limitations.
What it tests
Kruskal-Wallis compares distributions across independent groups using ranks. Similar group shapes let it be read as a median comparison.
It does not test means and it is not assumption-free — independence and similar shapes still matter.
Garment-factory example
Compare highly skewed repair minutes across three defect categories where ANOVA residual assumptions fail.
Method
- Plot groups and confirm independence.
- Run the test in a free spreadsheet or tool.
- Follow up with adjusted-error pairwise comparisons.
- Report effect and its practical meaning.
Common mistakes
- Using it for paired data.
- Claiming it tests means.
Knowledge check
Pick one answer per question. Explanations appear after you submit.
1. Kruskal-Wallis analyses:
2. Non-parametric implies:
Author: NOSK.life · Last reviewed: 2026-07-21