2014Wiley StatsRef: Statistics Reference OnlineRequires access

Wilcoxon‐Mann‐Whitney Test: Overview

Venita DePuy, Vance W. Berger, YanYan Zhou

Open publisher page 11 citations

Abstract

Abstract The Wilcoxon‐Mann‐Whitney test evaluates the difference in medians between two similarly shaped populations, which have the same variance. This nonparametric test is similar to the two‐sample Student's t Test. The primary differences are that the t Test requires the assumption of normality, while the Wilcoxon‐Mann‐Whitney test can be performed where only rankings, that is, ordinal data, are recorded. While exact computations of the test statistic are possible, a large sample approximation is often used for ease of calculation. Both methods are available in various commercial statistical packages.

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What this paper is about

Abstract The Wilcoxon‐Mann‐Whitney test evaluates the difference in medians between two similarly shaped populations, which have the same variance. This nonparametric test is similar to the two‐sample Student's t Test. The primary differences are that the t Test requires the assumption of normality, while the Wilcoxon‐Mann‐Whitney test can be performed where only rankings, that is, ordinal data, are recorded. While exact computations of the test statistic are possible, a large sample approximation is often used for ease of calculation. Both methods are available in various commercial statistical packages.

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Available abstract

Abstract The Wilcoxon‐Mann‐Whitney test evaluates the difference in medians between two similarly shaped populations, which have the same variance. This nonparametric test is similar to the two‐sample Student's t Test. The primary differences are that the t Test requires the assumption of normality, while the Wilcoxon‐Mann‐Whitney test can be performed where only rankings, that is, ordinal data, are recorded. While exact computations of the test statistic are possible, a large sample approximation is often used for ease of calculation. Both methods are available in various commercial statistical packages.

Key concepts: Mann–Whitney U test, Wilcoxon signed-rank test, Statistics, Nonparametric statistics, Mathematics, Normality test, Sign test, Goldfeld–Quandt test

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