Nonparametric Statistics
Nonparametric statistics is a branch of statistics that does not rely on assumptions about the underlying distribution of data, such as normality or specific parameter values. It uses methods based on ranks, signs, or other distribution-free techniques to analyze data, making it robust for skewed, ordinal, or small sample datasets. Common applications include hypothesis testing, correlation analysis, and comparing groups when parametric assumptions are violated.
Developers should learn nonparametric statistics when working with data that does not meet the assumptions of parametric tests, such as in machine learning for handling outliers, in data science for exploratory analysis with unknown distributions, or in research with non-normal or categorical data. It is essential for robust statistical inference in fields like bioinformatics, social sciences, and quality control, where data may be messy or assumptions are uncertain.