Does Bayesian statistics have to meet normality assumptions?
I am fairly new to JASP and Bayesian statistics, and this question has been bugging me for some time, so I hope someone is able to give me some advice.
I read from Kruschke's (2010) paper that Bayesian ANOVA does not assume approximations to normality or homogeneity of variance, but I have also come across discussions of using nonparametric Bayesian tests on this forum. This seems rather confusing to me because if those assumptions need not be met, why is it necessary to have some nonparametric versions of the Bayesian tests? Does it apply only to Bayesian ANOVA but not Bayesian t-test or other tests? Or have i misunderstood?
I have also read from his DBDA book that changing the likelihood function to a t distribution (instead of using a normal distribution) will be more robust against outliers because of its long tails. This seems very useful and I wonder if there is any way we can change the likelihood function of Bayesian tests on JASP?