ttest_ind(a, b, axis=0, equal_var=True, alternative='two-sided')

Notes

Parameters

a, b : array_like

The arrays must have the same shape, except in the dimension corresponding to axis (the first, by default).

axis : int or None, optional

Axis along which to compute test. If None, compute over the whole arrays, a, and b.

equal_var : bool, 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.

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.

Returns

statistic : float or array

The calculated t-statistic.

pvalue : float or array

The p-value.

Examples

See :

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GitHub : /scipy/stats/_mstats_basic.py#1379
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