statsmodels.stats.weightstats.ztost#

statsmodels.stats.weightstats.ztost(x1, low, upp, x2=None, usevar='pooled', ddof=1.0)[source]#

Equivalence test based on normal distribution

Parameters:

x1array_like: one sample or first sample for 2 independent samples
low, uppfloat: equivalence interval low < m1 - m2 < upp
x2array_like or None, optional: second sample for 2 independent samples test. If None, then a one-sample test is performed.
usevar{“pooled”, “unequal”}, optional: If pooled, then the standard deviation of the samples is assumed to be the same. If unequal, then the standard deviation of the sample is assumed to be different.
ddofint or float, optional: Degrees of freedom used in the calculation of the variance of the mean estimate. In the case of comparing means this is one, however it can be adjusted for testing other statistics (proportion, correlation).

Returns:

pvaluefloat: pvalue of the non-equivalence test
t1, pv1tuple of floats: test statistic and pvalue for lower threshold test
t2, pv2tuple of floats: test statistic and pvalue for upper threshold test

Notes

checked only for 1 sample case