class eelbrain.test.MannWhitneyU(y, x, c1=None, c0=None, match=None, sub=None, data=None, tail=0, continuity=True)

Mann-Whitney U-test (non-parametric independent measures test)

The test data can be specified in two forms:

  • In long form, with y supplying the data, x specifying condition for each case.

  • With y and x supplying data for the two conditions.

  • y (Var) – Dependent variable. Alternatively, the first of two variables that are compared.

  • x (categorial) – Model containing the cells which should be compared. Alternatively, the second of two varaibles that are compared.

  • c1 (str | tuple | None) – Test condition (cell of x). c1 and c0 can be omitted if x only contains two cells, in which case cells will be used in alphabetical order.

  • c0 (str | tuple | None) – Control condition (cell of x).

  • match (categorial) – Units within which measurements are related (e.g. ‘subject’ in a within-subject comparison). If match is unspecified, it is assumed that y and x are two measurements with matched cases.

  • sub (index-array) – Perform the test with a subset of the data.

  • data (Dataset) – If a Dataset is specified, all data-objects can be specified as names of Dataset variables.

  • tail (0 | 1 | -1) – Which tail of the t-distribution to consider: 0: both (two-tailed, default); 1: upper tail (one-tailed); -1: lower tail (one-tailed).

  • continuity (bool) – Continuity correction (default True).

  • u (float) – Mann-Whitney U statistic.

  • p (float) – P-value.

  • tail (0 | 1 | -1) – Tailedness of the p value.

See also


parametric alternative


Based on scipy.stats.mannwhitneyu().