Definition
Non-parametric tests are statistical procedures that do not assume normal distribution of data, relying instead on rank or order of observations.

Introduction
Real-world data often disobey textbook rules—skewed distributions, small samples, or ordinal scales defy parametric assumptions. Non-parametric methods rescue analysis in such cases, maintaining rigor when mathematical perfection is absent.

Explanation
The Mann-Whitney U Test compares two independent groups on ordinal or non-normally distributed data, while the Kruskal-Wallis Test extends comparison to more than two groups. Instead of means, they analyze ranks.

These methods are robust, simple, and applicable where data violate conditions of equal variance or interval scaling. They sacrifice some efficiency but gain resilience, ensuring meaningful interpretation of messy realities.

Key Takeaways
When normality fails, non-parametric tests uphold fairness, preserving validity through flexibility.

Real-World Case
In medical research comparing patient pain levels across treatments measured on ordinal scales, non-parametric tests like Kruskal-Wallis provide reliable results without assuming normal pain distributions.
Reference: https://www.bmj.com