mars.tensor.stats.ttest_ind_from_stats#

mars.tensor.stats.ttest_ind_from_stats(mean1, std1, nobs1, mean2, std2, nobs2, equal_var=True, alternative='two-sided')[source]#

T-test for means of two independent samples from descriptive statistics.

This is a two-sided test for the null hypothesis that two independent samples have identical average (expected) values.

Parameters

  • mean1 (array_like) – The mean(s) of sample 1.

  • std1 (array_like) – The standard deviation(s) of sample 1.

  • nobs1 (array_like) – The number(s) of observations of sample 1.

  • mean2 (array_like) – The mean(s) of sample 2.

  • std2 (array_like) – The standard deviations(s) of sample 2.

  • nobs2 (array_like) – The number(s) of observations of sample 2.

  • equal_var (bool, optional) – If True (default), perform a standard independent 2 sample test that assumes equal population variances 1. If False, perform Welch’s t-test, which does not assume equal population variance 2.

  • alternative ({'two-sided', 'less', 'greater'}, optional) –

    Defines the alternative hypothesis. The following options are available (default is ‘two-sided’):

    • ’two-sided’

    • ’less’: one-sided

    • ’greater’: one-sided

Returns

  • statistic (float or array) – The calculated t-statistics.

  • pvalue (float or array) – The two-tailed p-value.

See also

scipy.stats.ttest_ind

Notes

References

1

https://en.wikipedia.org/wiki/T-test#Independent_two-sample_t-test

2

https://en.wikipedia.org/wiki/Welch%27s_t-test