stats.weightstats.ttost_paired()

statsmodels.stats.weightstats.ttost_paired

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.

Parameters:

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

Returns:

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