statsmodels.stats.weightstats.DescrStatsW.ttost_mean

DescrStatsW.ttost_mean(low, upp)[source]

test of (non-)equivalence of one sample

TOST: two one-sided t 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, uppfloat

equivalence interval low < mean < upp

Returns:
pvaluefloat

pvalue of the non-equivalence test

t1, pv1, df1tuple

test statistic, pvalue and degrees of freedom for lower threshold test

t2, pv2, df2tuple

test statistic, pvalue and degrees of freedom for upper threshold test


Last update: Apr 12, 2024