Description: Non-parametric methods are statistical techniques that do not assume a specific distribution for the data, making them particularly useful in situations where normality assumptions are not met. Unlike parametric methods, which require data to follow a known distribution (such as normal), non-parametric methods are more flexible and can be applied to a variety of data types. These techniques are especially valuable in data analysis where small samples are available or where data contain outliers or skewed distributions. Non-parametric methods include tests such as the Wilcoxon test, Kruskal-Wallis test, and Friedman test, among others. Additionally, they are widely used in machine learning and data science, where they are applied in classification and regression algorithms that do not rely on assumptions about the shape of the data distribution. Non-parametric methods can enhance model robustness, allowing for better generalization and performance on complex datasets.
