Skip to main content
MedCalc
Mail a PDF copy of this page to:
(Your email address will not be added to a mailing list)
working
Show menu

Mann-Whitney test (independent samples)

Command:Statistics
Next selectRank sum tests
Next selectMann-Whitney test (independent samples)

Description

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: the two filters must define distinct groups so that the same case is not included in the two samples.

Results

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.

When either or both sample sizes are large (>20) then MedCalc uses the Normal approximation (Lentner, 1982) to calculate the P-value. For small sample sizes, in the absence of ties, MedCalc calculates the exact probability (Conover, 1999).

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.

Literature

See also

External links