<P> Parametric statistics is a branch of statistics which assumes that sample data comes from a population that follows a probability distribution based on a fixed set of parameters . Most well - known elementary statistical methods are parametric . Conversely a non-parametric model differs precisely in that the parameter set (or feature set in machine learning) is not fixed and can increase, or even decrease if new relevant information is collected . </P> <P> Since a parametric model relies on a fixed parameter set, it assumes more about a given population than non-parametric methods do . When the assumptions are correct, parametric methods will produce more accurate and precise estimates than non-parametric methods, i.e. have more statistical power . However, as more is assumed by parametric methods, when the assumptions are not correct they have a greater chance of failing, and for this reason are not robust statistical methods . On the other hand, parametric formulae are often simpler to write down and faster to compute . For this reason their simplicity can make up for their lack of robustness, especially if care is taken to examine diagnostic statistics . </P>

The collection of statistical methods that require assumptions about the population is known as