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Mann-Whitney test (independent samples)

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The Mann-Whitney test is the non-parametric equivalent of the independent samples t-test (it is sometimes - wrongly - called a 'non-parametric t-test').

This test should be used when the sample data are not Normally distributed, and they cannot be transformed to a Normal distribution by means of a logarithmic transformation.

Required input

Mann-Whitney test - dialog box

Select the variables for sample 1 and sample 2. You can use the Drop-down button button to select variables and filters in the variables list.

Caveat: if the two variables are the same, then the two filters must define distinct groups so that the same case is not included in the two samples.


Summary statistics

The results windows for the Mann-Whitney test (independent samples) displays summary statistics of the two samples.

The statistics include the Hodges-Lehmann median difference (the Hodges-Lehmann estimate of location shift) and its 95% confidence interval (Conover, 1999). For two independent samples with sample size m and n, the Hodges-Lehmann median difference is the median of all m × n paired differences between the observations in the two samples. Differences are calculated as sample 2 − sample 1. The confidence interval is derived according to Conover (1999, p. 281).

Note that the Hodges-Lehmann median difference is not necessarily the same as the difference between the two medians.

Mann-Whitney test - statistics

Mann-Whitney test results

The Mann-Whitney test (independent samples) combines and ranks the data from sample 1 and sample 2 and calculates a statistic on the difference between the sum of the ranks of sample 1 and sample 2.

If the resulting P-value is small (P<0.05) then a statistically significant difference between the two samples can be accepted.

Note that in MedCalc P-values are always two-sided.


See also

External links

Recommended book

Book cover

Practical Nonparametric Statistics
W. J. Conover

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This classic text and reference book is intended mainly for one-semester advanced undergraduate and undergrad/graduate introductory courses in nonparametric (or distribution free) statistics. The book will also appeal to applied research workers as a quick reference to the most useful nonparametric methods.