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. - Added 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. 
 - Added 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- scipy.stats.ttest_ind.