statsmodels.stats.rates.nonequivalence_poisson_2indep(count1, exposure1, count2, exposure2, low, upp, method=
Test for non-equivalence, minimum effect for poisson.
This reverses null and alternative hypothesis compared to equivalence testing. The null hypothesis is that the effect, ratio (or diff), is in an interval that specifies a range of irrelevant or unimportant differences between the two samples.
The Null and alternative hypothesis comparing the ratio of rates are
for compare = ‘ratio’:
H0: low < rate1 / rate2 < upp
H1: rate1 / rate2 <= low or upp <= rate1 / rate2
for compare = ‘diff’:
H0: rate1 - rate2 <= low or upp <= rate1 - rate2
H1: low < rate - rate < upp
This is implemented as two one-sided tests at the minimum effect boundaries (low, upp) with (nominal) size alpha / 2 each. The size of the test is the sum of the two one-tailed tests, which corresponds to an equal-tailed two-sided test. If low and upp are equal, then the result is the same as the standard two-sided test.
The p-value is computed as 2 * min(pvalue_low, pvalue_upp) in analogy to two-sided equal-tail tests.
In large samples the nominal size of the test will be below alpha.
Hodges, J. L., Jr., and E. L. Lehmann. 1954. Testing the Approximate Validity of Statistical Hypotheses. Journal of the Royal Statistical Society, Series B (Methodological) 16: 261–68.
Kim, Jae H., and Andrew P. Robinson. 2019. “Interval-Based Hypothesis Testing and Its Applications to Economics and Finance.” Econometrics 7 (2): 21. https://doi.org/10.3390/econometrics7020021.