International Encyclopedia of Statistical Science
pp 10511052
Parametric Versus Nonparametric Tests
 David J. SheskinAffiliated withWestern Connecticut State University
A common distinction made with reference to statistical tests/procedures is the classification of a procedure as parametric versus nonparametric. This distinction is generally predicated on the number and severity of assumptions regarding the population that underlies a specific test. Although some sources use the term assumption free (as well as distribution free) in reference to nonparametric tests, the latter label is misleading, in that nonparametric tests are not typically assumption free. Whereas parametric statistical tests make certain assumptions with respect to the characteristics and/or parameters of the underlying population distribution upon which a test is based, nonparametrictests make fewer or less rigorous assumptions. Thus, as Marascuilo and McSweeney (1977) suggest, nonparametric tests should be viewed as assumption freer tests. Perhaps the most common assumption associated with parametric tests that does not apply to nonparametric tests is that data are derived from ...
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 Title
 Parametric Versus Nonparametric Tests
 Reference Work Title
 International Encyclopedia of Statistical Science
 Pages
 pp 10511052
 Copyright
 2011
 DOI
 10.1007/9783642048982_440
 Print ISBN
 9783642048975
 Online ISBN
 9783642048982
 Publisher
 Springer Berlin Heidelberg
 Copyright Holder
 SpringerVerlag Berlin Heidelberg
 Additional Links
 Topics
 Industry Sectors
 Editors

 Miodrag Lovric ^{(620)}
 Editor Affiliations

 620. Department of Statistics and Informatics, Faculty of Economics, University of Kragujevac
 Authors

 David J. Sheskin ^{(163)}
 Author Affiliations

 163. Western Connecticut State University, Danbury, CT, USA
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