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| Non-parametric statistics is included in JEL classification codes: JEL C11 |
Non-parametric statistics is a branch of statistics concerned with non-parametric statistical models and non-parametric statistical tests. Non-parametric statistics are statistics that do not estimate population parameters. In contrast, see parametric statistics.
Non-parametric models differ from parametric models in that the model structure is not specified a priori but is instead determined from data. The term nonparametric is not meant to imply that such models completely lack parameters but that the number and nature of the parameters are flexible and not fixed in advance. Nonparametric models are therefore also called distribution free.
Non-parametric (or distribution-free) inferential statistical methods are mathematical procedures for statistical hypothesis testing which, unlike parametric statistics, make no assumptions about the frequency distributions of the variables being assessed.
Nonparametric tests have less power than the appropriate parametric tests, but are more robust when the assumptions underlying the parametric test are not satisfied.
See also
Pages in category "Non-parametric statistics"
The following 42 pages are in this category, out of 42 total. This list may sometimes be slightly out of date (learn more)
