scipy.stats.mstats.ttest_ind#
- scipy.stats.mstats.ttest_ind(a, b, axis=0, equal_var=True, alternative='two-sided')[source]#
Calculates the T-test for the means of TWO INDEPENDENT samples of scores.
- Parameters
- a, barray_like
The arrays must have the same shape, except in the dimension corresponding to axis (the first, by default).
- axisint or None, optional
Axis along which to compute test. If None, compute over the whole arrays, a, and b.
- equal_varbool, optional
If True, perform a standard independent 2 sample test that assumes equal population variances. If False, perform Welch’s t-test, which does not assume equal population variance.
New in version 0.17.0.
- alternative{‘two-sided’, ‘less’, ‘greater’}, optional
Defines the alternative hypothesis. The following options are available (default is ‘two-sided’):
‘two-sided’: the means of the distributions underlying the samples are unequal.
‘less’: the mean of the distribution underlying the first sample is less than the mean of the distribution underlying the second sample.
‘greater’: the mean of the distribution underlying the first sample is greater than the mean of the distribution underlying the second sample.
New in version 1.7.0.
- Returns
- statisticfloat or array
The calculated t-statistic.
- pvaluefloat or array
The p-value.
Notes
For more details on
ttest_ind, see stats.ttest_ind.