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.