scipy.stats. ttest_ind (a, b, *, axis = 0, equal_var = True, nan_policy = 'propagate', permutations = None, random_state = None, alternative = 'two-sided', trim = 0, method = None, keepdims = False) [source] # Calculate the T-test for the means of two independent samples of scores.

scipy.stats. ttest_rel (a, b, axis = 0, nan_policy = 'propagate', alternative = 'two-sided', *, keepdims = False) [source] # Calculate the t-test on TWO RELATED samples of scores, a and b. This is a test for the null hypothesis that two related or repeated …

scipy.stats. ttest_1samp (a, popmean, axis = 0, nan_policy = 'propagate', alternative = 'two-sided', *, keepdims = False) [source] # Calculate the T-test for the mean of ONE group of scores. This is a test for the null hypothesis that the expected value (mean) of a sample of independent observations a is equal to the given population mean ...

scipy.stats. t = <scipy.stats._continuous_distns.t_gen object> [source] # A Student’s t continuous random variable. For the noncentral t distribution, see nct .

scipy.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.

Apr 26, 2021 · scipy.stats.ttest_ind (a, b, axis = 0, equal_var = True, nan_policy = 'propagate', alternative = 'two-sided') [source] ¶ Calculate the T-test for the means of two independent samples of scores. This is a two-sided test for the null hypothesis that 2 independent samples have identical average (expected) values.

Calculate the T-test for the means of two independent samples of scores. This is a test for the null hypothesis that 2 independent samples have identical average (expected) values. This test assumes that the populations have identical variances by default. Parameters a, b array_like

scipy.stats. ttest_1samp (a, popmean, axis = 0, nan_policy = 'propagate', alternative = 'two-sided') [source] # Calculate the T-test for the mean of ONE group of scores. This is a test for the null hypothesis that the expected value (mean) of a sample of independent observations a is equal to the given population mean, popmean .

Performs the (one-sample or two-sample) Kolmogorov-Smirnov test for goodness of fit. The one-sample test compares the underlying distribution F(x) of a sample against a given distribution G(x). The two-sample test compares the underlying distributions of two independent samples.

T-test and KS-test# We can use the t-test to test whether the mean of our sample differs in a statistically significant way from the theoretical expectation. >>> print ( 't-statistic = %6.3f pvalue = %6.4f ' % stats . ttest_1samp ( x , m )) t-statistic = 0.391 pvalue = 0.6955 # random