class eelbrain.test.MannWhitneyU(y: Union[eelbrain._data_obj.Var, str], x: Union[eelbrain._data_obj.Factor, eelbrain._data_obj.Interaction, eelbrain._data_obj.NestedEffect, str, eelbrain._data_obj.Var], c1: Union[str, Tuple[str, ...]] = None, c0: Union[str, Tuple[str, ...]] = None, match: Union[eelbrain._data_obj.Factor, eelbrain._data_obj.Interaction, eelbrain._data_obj.NestedEffect, str] = None, sub: Union[eelbrain._data_obj.Var, numpy.ndarray, str] = None, ds: eelbrain._data_obj.Dataset = None, tail: int = 0, continuity: bool = 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 : None | index-array

Perform the test with a subset of the data.

ds : None | 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).

See also

parametric alternative


Based on scipy.stats.mannwhitneyu().

u : float

Mann-Whitney U statistic.

p : float


tail : 0 | 1 | -1

Tailedness of the p value.