statsmodels.stats.weightstats.ztost

statsmodels.stats.weightstats.ztost(x1, low, upp, x2=None, usevar='pooled', ddof=1.0)[source]

Equivalence test based on normal distribution

Parameters:
x1 : array_like or None

one sample or first sample for 2 independent samples

low : float

equivalence interval low < m1 - m2 < upp

upp : float

equivalence interval low < m1 - m2 < upp

x1

second sample for 2 independent samples test. If None, then a one-sample test is performed.

usevar : str, 'pooled'

If pooled, then the standard deviation of the samples is assumed to be the same. Only pooled is currently implemented.

Returns:

  • pvalue (float) – pvalue of the non-equivalence 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

checked only for 1 sample case