scipy.stats.ttest_ind_from_stats¶

scipy.stats.ttest_ind_from_stats(mean1, std1, nobs1, mean2, std2, nobs2, equal_var=True)[source]¶

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

This is a two-sided test for the null hypothesis that 2 independent samples have identical average (expected) values.

Parameters:

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 [R645]. If False, perform Welch’s t-test, which does not assume equal population variance [R646].
Returns:	statistic : float or array The calculated t-statistics pvalue : float or array The two-tailed p-value.

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 [R645]. If False, perform Welch’s t-test, which does not assume equal population variance [R646].

Returns:

statistic : float or array

The calculated t-statistics

pvalue : float or array

The two-tailed p-value.