How to set the prior for correlation matrix with very large data sets
This is maybe more of a theoretical question.
A colleague has a data set from a very large questionnaire survey with over 18,000 participants.
If I understand correctly, the default priors in JASP assume effect sizes typically found in typical psychology experiments with N < 100?
She has created a correlation with a few variables, and of course the BFs are all extremely large, even for very weak correlations. For example, using a stretched beta prior width = 1:
r = 0.095, BF10 = 4.961e +33
r = 0.43, BF10 = infinity.
The data completely overrides the prior.
Are there any methods for defining a prior for such large data sets, which would give the null a chance, and allow hypothesis testing?