# nonparametric statistics

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## nonparametric statistics

[¦nän ¦par·ə′me·trik stə′tis·tiks]
(statistics)
A class of statistical methods applicable to a large set of probability distributions used to test for correlation, location, independence, and so on.

## nonparametric statistics

statistical methods used for the analysis of ordinal and categorical level sample data which do not require assumptions about the shape of the population distribution from which the samples have been drawn. Such statistics are often referred to as ‘distribution-free statistics’. In contrast to PARAMETRIC STATISTICS, assumptions underlying the use of the methods are lenient and the formulae involved are simple and easy to use. Examples are the runs tests, the signs test and Cramer's V. Although such measures are popular in sociology, they have the disadvantages that they waste information if interval data is ‘degraded’ into categorical data, and that the tests are not as powerful as parametric tests (see also SIGNIFICANCE TEST). Against this, they are often more robust, i.e. they give the same results in spite of assumptions being violated. Hence, if the assumptions of a parametric test are not met, the use of an equivalent nonparametric test will still be valid.
References in periodicals archive ?
An appendix provides distribution tables for commonly used nonparametric statistics.
While no quantitative model will probably ever perfectly meet such criteria, one existing class of nonparametric statistics, rank correlation statistics, comes close.
Most textbooks cover either nonparametric statistics or categorical data analysis, he says, but with this textbook his is able to deal with both during a one-semester course that also sets out a framework for choosing the best statistical technique.
On the other hand, G24, G1, G3, G2, G18 and G10 had the highest S i (1) and S i (2) values; therefore, they were determined to be unstable Two other nonparametric statistics (S i (3) and S i (6)) combine yield and stability based on yield ranks of genotypes in each environment (Nassar and Huehn, 1987).
With examples, illustrations and accessible text Stapleton describes discrete probability models, special discrete distributions, continuous random variables, special continuous and conditional distributions, moment generating functions and limit theory, estimation, testing of hypotheses, the multivariate normal (as well as chi-square, t and F distributions) nonparametric statistics, linear statistical models, and frequency data.

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