# Point hypothesis and equivalence hypothesis at once

Sometimes researchers (especially in collaborations) have two opposite but sound theories: There is a difference between two groups or there is only negligible difference. Now they ask their statistician. How should he approach the situation?

If he does a point hypothesis test, he favours the difference party since only their theory can be confirmed in case of rejected point hypothesis whereas not rejecting the point hypothesis teaches nothing.

If he does only equivalence testing, he favours the negligible difference party for the analogous reason.

Should he do both, of course with multiplicity correction? So a TOST for equivalence and a point hypothesis test? Or a TOST and a respective relevance test?

This procedure would have three outcomes:

1. Equivalence of both parameters up to negligible differences.
2. Large enough differences.
3. Nothing to learn since both hypotheses have not been rejected.

Is such an "et-et"-approach reasonable? Why do we hardly see such "et-et"-analyses in publications? This is a general question for reasoning. So multiple answers are encouraged and I do not restrict this question to particular models.

• It is not quite clear what you are asking. Are you asking about the importance (or not) of multiple corrections in a relevance testing situation (i.e. given that, when using TOST for equivalence, relevance testing entails three tests... albeit the TOSTs are non-overlapping in the nulls). Also, for the sake of clarity to one's audience I would encourage notationally differentiating the null hypotheses for a test for difference (H$^{+}_{0}$) from a test for equivalence (H$^{-}_{0}$) as you have seen me use in other questions and answers. Aug 19, 2014 at 14:29
• I hope I made it clearer. In fact, hypothesis testing is often used with rather explorative than confirmative intent. So both, equivalence and difference, are of equal interest. However, only one of them is usually used. Why? Aug 20, 2014 at 13:35
• Not sure. I suspect because of historical contingencies, possibly having to do with equivalence testing coming largely out of pharmaceutical research and clinical trials. I know that the (frequentist) curriculum I teach now emphasizes that one simply ought conduct both. Aug 20, 2014 at 15:46
• Are you restricted to frequentist tests, or open to Bayesian methods? Aug 21, 2014 at 17:28
• Also Bayesian approaches to testing can help. What I'm not interested in is a dichotomous decision "either difference or equivalence". Statistics should address situations too bad for reliable inference (e.g. too small sample sizes) as outcome (3) does. Aug 22, 2014 at 9:04