Non-Parametric Methods
Non-parametric methods are statistical techniques that do not assume a specific distribution or functional form for the data, making them flexible for analyzing complex or unknown data structures. They rely on data-driven approaches, such as ranks or distances, to perform inference, classification, or regression without fixed parameters. These methods are particularly useful when data violates assumptions of parametric models or when the underlying distribution is not well-defined.
Developers should learn non-parametric methods when working with data that has unknown distributions, outliers, or non-linear relationships, such as in exploratory data analysis, machine learning, or robust statistical modeling. They are essential for tasks like density estimation, hypothesis testing with small samples, or handling non-normal data in fields like bioinformatics, finance, or social sciences. Using these methods helps avoid model misspecification and provides more reliable insights in real-world, messy datasets.