statsmodels.stats.weightstats.ttost_paired(x1, x2, low, upp, transform=None, weights=None)[source]

test of (non-)equivalence for two dependent, paired sample

TOST: two one-sided t tests

null hypothesis: md < low or md > upp alternative hypothesis: low < md < upp

where md is the mean, expected value of the difference x1 - x2

If the pvalue is smaller than a threshold,say 0.05, then we reject the hypothesis that the difference between the two samples is larger than the the thresholds given by low and upp.


x1, x2 : array_like

two dependent samples

low, upp : float

equivalence interval low < mean of difference < upp

weights : None or ndarray

case weights for the two samples. For details on weights see DescrStatsW

transform : None or function

If None (default), then the data is not transformed. Given a function sample data and thresholds are transformed. If transform is log the the equivalence interval is in ratio: low < x1 / x2 < upp


pvalue : float

pvalue of the non-equivalence test

t1, pv1, df1 : tuple

test statistic, pvalue and degrees of freedom for lower threshold test

t2, pv2, df2 : tuple

test statistic, pvalue and degrees of freedom for upper threshold test