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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

one sample or first sample for 2 independent samples

low, upp : float

equivalence interval low < m1 - m2 < upp

x1 : array_like or None

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

usevar : string, ‘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

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