CompareMeans.ttest_ind()

statsmodels.stats.weightstats.CompareMeans.ttest_ind

CompareMeans.ttest_ind(alternative='two-sided', usevar='pooled', value=0) [source]

ttest for the null hypothesis of identical means

this should also be the same as onewaygls, except for ddof differences

Parameters:

x1, x2 : array_like, 1-D or 2-D two independent samples, see notes for 2-D case alternative : string The alternative hypothesis, H1, has to be one of the following ?two-sided?: H1: difference in means not equal to value (default) ?larger? : H1: difference in means larger than value ?smaller? : H1: difference in means smaller than value usevar : string, ?pooled? or ?unequal? If pooled, then the standard deviation of the samples is assumed to be the same. If unequal, then Welsh ttest with Satterthwait degrees of freedom is used value : float difference between the means under the Null hypothesis.

Returns:

tstat : float test statisic pvalue : float pvalue of the t-test df : int or float degrees of freedom used in the t-test

Notes

The result is independent of the user specified ddof.