statsmodels.stats.weightstats.ttest_ind

statsmodels.stats.weightstats.ttest_ind(x1, x2, alternative='two-sided', usevar='pooled', weights=(None, None), value=0)[source]

ttest independent sample

Convenience function that uses the classes and throws away the intermediate results, compared to scipy stats: drops axis option, adds alternative, usevar, and weights option.

Parameters:
x1 : array_like, 1-D or 2-D

first of the two independent samples, see notes for 2-D case

x2 : array_like, 1-D or 2-D

second of the two independent samples, see notes for 2-D case

alternative : str

The alternative hypothesis, H1, has to be one of the following

  • ’two-sided’ (default): H1: difference in means not equal to value

  • ’larger’ : H1: difference in means larger than value

  • ’smaller’ : H1: difference in means smaller than value

usevar : str, 'pooled' or 'unequal'

If pooled, then the standard deviation of the samples is assumed to be the same. If unequal, then Welch ttest with Satterthwait degrees of freedom is used

weights : tuple of None or ndarrays

Case weights for the two samples. For details on weights see DescrStatsW

value : float

difference between the means under the Null hypothesis.

Returns:

  • tstat (float) – test statistic

  • pvalue (float) – pvalue of the t-test

  • df (int or float) – degrees of freedom used in the t-test