 |
 |
Dear Diana, obviously there's no way to reconcile not doing hypothesis tests ever with recommendations how to do them. In fact, the original 2016 ASA statement does *not* say you shouldn't ever do it, but Wasserstein and some others seem to be keen on getting that message across. In a fairly recent ASA President's statement, Karen Kafadar distances herself from it: https://errorstatistics.files.wordpress.com/2019/11/kafadar-2019-1.pdf See also Deborah Mayo's extended discussion of a 2019 paper by Wasserstein et al.'s "ASA II statement" where they go further in this direction: https://errorstatistics.com/2019/11/04/on-some-self-defeating-aspects-of-the-asas-2019-recommendations-on-statistical-significance-tests/ Personally I'm with those who think misuse of p-values and tests is not the tests' and p-values' fault in the first place but of those who misuse them. There are admittedly many, many instances and possibilities to misuse them, however in my view this has more to do with the fact that statistics in very difficult indeed, with the current reward system, and that people seem to love simple black-and-white messages where more balanced discussions would be more appropriate. If Bayesian stats were as popular as p-values, we'd see it misused to more or less the same amount (don't get me started on the difficulty and pitfalls of designing a convincing prior and how often in the Bayesian literature this is not done). Best wishes, Christian On 27/04/2020 16:41, Kornbrot, Diana wrote: > Am writing experimentalist's guide on Open Access for Mss. and data > But found conflicting advice > 1. Null-hypothesis testing should not be conducted - ever. > Wasserstein, R. L., & Lazar, N. A. (2016). The ASA Statement on p-Values: Context, Process, and Purpose. The American Statistician, 70(2), 129-133. doi:10.1080/00031305.2016.1154108 > > 2. The rationale for the sample size should be given (e.g. an a priori power analysis) > Aczel, B., Szaszi, B., Sarafoglou, A., Kekecs, Z., Kucharský, Š., Benjamin, D., . . . Wagenmakers, E.-J. (2020). A consensus-based transparency checklist. Nature Human Behaviour, 4(1), 4-6. doi:10.1038/s41562-019-0772-6 > > A priori power analysis calculates N for a null hypothesis test with specified sensitivity and specificity > > How can these recommendations be reconciled? > What is the best way of choosing a sample size without any reliance on null-hypothesis tests? > Particularly if investigator does not have access to Bayes Software, or is a frequentist at heart > > Many thanks for any help > best > Diana > > ____________ > University of Hertfordshire > College Lane, Hatfield, Hertfordshire AL10 9AB, UK > +44 (0) 208 444 2081 > +44 (0) 7403 18 16 12 > [log in to unmask]<mailto:[log in to unmask]> > http://dianakornbrot.wordpress.com/ > http://go.herts.ac.uk/Diana_Kornbrot/ > skype: kornbrotme > Save our in-boxes! http://emailcharter.org<http://emailcharter.org/> > __________________ > > > > > > > > You may leave the list at any time by sending the command > > SIGNOFF allstat > > to [log in to unmask], leaving the subject line blank. > -- Christian Hennig Universita di Bologna, Dipartimento di Scienze Statistiche "Paolo Fortunati" [log in to unmask] +39 051 2098163 You may leave the list at any time by sending the command SIGNOFF allstat to [log in to unmask], leaving the subject line blank.