class eelbrain.test.TTestInd(y: Union[eelbrain._data_obj.Var, str], x: Union[eelbrain._data_obj.Factor, eelbrain._data_obj.Interaction, eelbrain._data_obj.NestedEffect, str], 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)

Independent measures t-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.

x : categorial

Model containing the cells which should be 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 and should be averaged over (e.g. ‘subject’ in a between-group comparison).

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).

t : float


p : float


tail : 0 | 1 | -1

Tailedness of the p value.

df : int

Degrees of freedom.