# Do we need to report Frequentist analyses when using Bayesian inference?

Aram
Posts:

**7**On the manual of BayesFactors, the only times you use frequentist inference is as a means of facilitating comparison. In The New Statistics: Why and How, Geoff Cumming wrote:

- Whenever possible, avoid using statistical significance or p values; simply omit any mention of nullhypothesis

significance testing (NHST).

But I have found many papers saying that they obtained certain BF supporting the null or alternative hypothesis. Is it possible to obtain all the information needed for publishing a paper, by using **only ** the BayesFactors package? I haven't tried JASP so I don't know how much they differ from each other in terms of the information one can get using each one.

## Comments

408Hi Aram,

1. As far as Bayesian analyses are concerned, we are trying to keep BayesFactors and JASP in sync.

2. There is a confusion in the literature about classical NHST and BFs. My perspective is that p-values are not to be recommended (but you might mention them in a footnote to pacify reviewers); however, hypothesis testing per se is often exactly what researchers want, and imo there is nothing wrong with that. You just have to test hypothesis the right way, and basically replace p-values with BFs. Zoltan Dienes has a more inclusive perspective: "a B for every p".

3. After a few drinks, when I've mellowed a bit, I can see the value in reporting both p, BF, and Bayesian estimation. Sometimes the stats are really important for supporting a key claim, and it can't hurt for the readers to have all the information at their disposal.

4. As far as the limitations of a "estimation only" approach, see part I here: https://osf.io/m6bi8/

E.J.

2,846Just to avoid confusion: The term "null-hypothesis significance testing" (NHST) is generally used as a synonym for reporting

pvalues. So if you test a null hypothesis with a Bayes Factor, most people would not call that "null-hypothesis significance testing", even though in a sense it is—a semantically confusing legacy from the times when NHST was (perceived to be) the only way to test a null hypothesis.There's much bigger issues in the world, I know. But I first have to take care of the world I know.

cogsci.nl/smathot