The effects of misconceptions on the properties of friedman's test

Roy St Laurent, Philip Turk

Research output: Contribution to journalArticle

4 Scopus citations

Abstract

Friedman's test is a widely used rank-based alternative to the analysis of variance (ANOVA) F-test for identifying treatment differences in a randomized complete block design. Many texts provide incomplete or misleading information about when Friedman's test may be appropriately applied. We discuss the assumptions needed for the test and common misconceptions. We show via simulation that when the variance or skew of the treatment distributions differ, application of Friedman's test to detect differences in treatment location can result in Type I error probabilities larger than the nominal α, and even when α is unaffected, the power of the test can be less than expected.

Original languageEnglish (US)
Pages (from-to)1596-1615
Number of pages20
JournalCommunications in Statistics: Simulation and Computation
Volume42
Issue number7
DOIs
StatePublished - Aug 1 2013

Keywords

  • Analysis of variance
  • Heteroskedasticity
  • Nonparametric test
  • Power
  • Size of test
  • Skew

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation

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