Non-Parametric Statistics
Non-parametric statistics are statistical methods that do not assume a specific probability distribution for the data, such as the normal distribution. They are used when data does not meet the assumptions of parametric tests, often with ordinal data, small sample sizes, or non-normal distributions. These methods rely on ranks, signs, or other distribution-free techniques to make inferences.
Developers should learn non-parametric statistics when working with data that violates assumptions of parametric methods, such as in exploratory data analysis, A/B testing with skewed data, or machine learning with non-normal features. It is essential for robust statistical analysis in fields like bioinformatics, social sciences, or any domain with messy, real-world data where distributional assumptions are uncertain.