scipy.stats.mannwhitneyu¶

scipy.stats.
mannwhitneyu
(x, y, use_continuity=True, alternative=None)[source]¶ Compute the MannWhitney rank test on samples x and y.
 Parameters
 x, yarray_like
Array of samples, should be onedimensional.
 use_continuitybool, optional
Whether a continuity correction (1/2.) should be taken into account. Default is True.
 alternativeNone (deprecated), ‘less’, ‘twosided’, or ‘greater’
Whether to get the pvalue for the onesided hypothesis (‘less’ or ‘greater’) or for the twosided hypothesis (‘twosided’). Defaults to None, which results in a pvalue half the size of the ‘twosided’ pvalue and a different U statistic. The default behavior is not the same as using ‘less’ or ‘greater’: it only exists for backward compatibility and is deprecated.
 Returns
 statisticfloat
The MannWhitney U statistic, equal to min(U for x, U for y) if alternative is equal to None (deprecated; exists for backward compatibility), and U for y otherwise.
 pvaluefloat
pvalue assuming an asymptotic normal distribution. Onesided or twosided, depending on the choice of alternative.
Notes
Use only when the number of observation in each sample is > 20 and you have 2 independent samples of ranks. MannWhitney U is significant if the uobtained is LESS THAN or equal to the critical value of U.
This test corrects for ties and by default uses a continuity correction.
References