Mann–Whitney U Test

Thomas W. MacFarland and Jan M. Yates
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Thomas W. MacFarland: Nova Southeastern University, Office of Institutional Effectiveness
Jan M. Yates: Nova Southeastern University, Abraham S. Fischler College of Education

Chapter Chapter 4 in Introduction to Nonparametric Statistics for the Biological Sciences Using R, 2016, pp 103-132 from Springer

Abstract: Abstract The Mann–Whitney U test is often viewed as the nonparametric equivalent of Student’s t-Test for Independent Samples, but this comparison may be somewhat too convenient. The two tests (the nonparametric Mann–Whitney U-Test and the parametric Student’s t-Test for Independent Samples) may have similar purposes in that they are both used to determine if there are statistically significant differences between two groups. However, the Mann–Whitney U-Test is used with nonparametric data (typically, ordinal data) whereas the Student’s t-Test for Independent Samples is used with data that meet the assumptions associated with parametric distributions (typically interval data that approximate an acceptable level of normal distribution). Even so, the Mann–Whitney U-Test has many appropriate uses and it should be considered when using ranked data, data that deviate from acceptable distribution patterns, or for when there are noticeable differences in the number of subjects in the two comparative groups.

Keywords: Anderson-Darling Test; Bar plot (stacked; side-by-side); Box plot; Code Book; Comma-separated values (.csv); Continuous scale; Density plot; Descriptive statistics; Distribution-free; Frequency distribution; Histogram; Interval; Mann–Whitney U Test; Mean; Median; Mode; Nominal; Nonparametric; Normal distribution; Null hypothesis; Ordinal; Parametric; Probability (p-value); Quantile-Quantile (QQ; Q-Q); Ranking; Stacked data; Statistical significance; Student’s t-Test for Independent Samples; Unstacked data (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/978-3-319-30634-6_4

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