Abstract: Chapter 24 Nonparametric Analysis of Variance Richard A. Armstrong, Richard A. ArmstrongSearch for more papers by this authorAnthony C. Hilton, Anthony C. HiltonSearch for more papers by this author Richard A. Armstrong, Richard A. ArmstrongSearch for more papers by this authorAnthony C. Hilton, Anthony C. HiltonSearch for more papers by this author Richard A. Armstrong, Richard A. ArmstrongSearch for more papers by this authorAnthony C. Hilton, Anthony C. HiltonSearch for more papers by this author Book Author(s):Richard A. Armstrong, Richard A. ArmstrongSearch for more papers by this authorAnthony C. Hilton, Anthony C. HiltonSearch for more papers by this author First published: 12 November 2010 https://doi.org/10.1002/9780470905173.ch24 AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onFacebookTwitterLinked InRedditWechat Summary To carry out an ANOVA, one important assumption is that the measured quantity must be a parametric variable, that is, a member of a normally distributed population. There are a limited number of nonparametric tests available for comparing three or more different groups. This chapter discussed two useful nonparametric tests are the Kruskal – Wallis and Friedmann's tests. The Kruskal – Wallis test is the nonparametric equivalent of the one - way ANOVA and essentially tests whether the medians of three or more independent groups are significantly different. Friedmann's test compares the medians of three or more dependent groups and in the nonparametric equivalent of the two - way ANOVA. Statistical Analysis in Microbiology: Statnotes RelatedInformation
Publication Year: 2010
Publication Date: 2010-11-12
Language: en
Type: other
Indexed In: ['crossref']
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