statsmodels.stats.weightstats.DescrStatsW.ztost_mean
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DescrStatsW.ztost_mean(low, upp)
[source] -
test of (non-)equivalence of one sample, based on z-test
TOST: two one-sided z-tests
null hypothesis: m < low or m > upp alternative hypothesis: low < m < upp
where m is the expected value of the sample (mean of the population).
If the pvalue is smaller than a threshold, say 0.05, then we reject the hypothesis that the expected value of the sample (mean of the population) is outside of the interval given by thresholds low and upp.
Parameters: low, upp : float
equivalence interval low < mean < upp
Returns: pvalue : float
pvalue of the non-equivalence test
t1, pv1 : tuple
test statistic and p-value for lower threshold test
t2, pv2 : tuple
test statistic and p-value for upper threshold test
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