scipy.stats.mannwhitneyu¶

scipy.stats.mannwhitneyu(x, y, use_continuity=True, alternative='two-sided')[source]¶

Computes the Mann-Whitney rank test on samples x and y.

Parameters:

Parameters:	x, y : array_like Array of samples, should be one-dimensional. use_continuity : bool, optional Whether a continuity correction (1/2.) should be taken into account. Default is True.
Returns:	statistic : float The Mann-Whitney statistics. pvalue : float One-sided p-value assuming a asymptotic normal distribution.

x, y : array_like

Array of samples, should be one-dimensional.

use_continuity : bool, optional

Whether a continuity correction (1/2.) should be taken into account. Default is True.

Returns:

statistic : float

The Mann-Whitney statistics.

pvalue : float

One-sided p-value assuming a asymptotic normal distribution.

Notes

Use only when the number of observation in each sample is > 20 and you have 2 independent samples of ranks. Mann-Whitney U is significant if the u-obtained is LESS THAN or equal to the critical value of U.

This test corrects for ties and by default uses a continuity correction. The reported p-value is for a one-sided hypothesis, to get the two-sided p-value multiply the returned p-value by 2.