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 (array_like) – first of the two independent samples

  • x2 (array_like) – second of the two independent samples

  • low (float) – equivalence interval low < mean of difference < upp

  • 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