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 (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’: 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 (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

  • 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

Notes

The result is independent of the user specified ddof.