Abstract

In this entry, two methods of statistical inference, parametric and nonparametric, are discussed. Parametric inference depends on selecting a probability model that describes the population under study based on the sample observations, whereas nonparametric inference encompasses statistical methods that do not require stringent assumptions about any particular parametric family of probability distributions. This entry compares and contrasts the two approaches and discusses the selection of an appropriate method of analysis and verification of the underlying assumptions.