statsmodels.stats.weightstats.ttost_ind¶

statsmodels.stats.weightstats.
ttost_ind
(x1, x2, low, upp, usevar='pooled', weights=(None, None), transform=None)[source]¶ test of (non)equivalence for two independent samples
TOST: two onesided t tests
null hypothesis: m1  m2 < low or m1  m2 > upp alternative hypothesis: low < m1  m2 < upp
where m1, m2 are the means, expected values of the two samples.
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, 1D or 2D
two independent samples, see notes for 2D case
low, upp : float
equivalence interval low < m1  m2 < upp
usevar : string, ‘pooled’ or ‘unequal’
If
pooled
, then the standard deviation of the samples is assumed to be the same. Ifunequal
, then Welsh ttest with Satterthwait degrees of freedom is usedweights : tuple of None or ndarrays
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, then the equivalence interval is in ratio: low < m1 / m2 < upp
Returns: pvalue : float
pvalue of the nonequivalence test
t1, pv1 : tuple of floats
test statistic and pvalue for lower threshold test
t2, pv2 : tuple of floats
test statistic and pvalue for upper threshold test
Notes
The test rejects if the 2*alpha confidence interval for the difference is contained in the
(low, upp)
interval.This test works also for multiendpoint comparisons: If d1 and d2 have the same number of columns, then each column of the data in d1 is compared with the corresponding column in d2. This is the same as comparing each of the corresponding columns separately. Currently no multicomparison correction is used. The raw pvalues reported here can be correction with the functions in
multitest
.