statsmodels.stats.weightstats.ttost_paired
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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
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