Hi! I am new in Bayesian Statistics and I’m trying to perform some analysis using brms. However, I’m facing some difficulties. I have two really basic questions and I would be grateful if you could give me your opinion. They can be simplified as follows:

Consider the following model:
m < brm(y ~ Group + (1Subj), data=dat)
where y is count data and Group is a categorical variable with two levels, A and B. Since y has underdispersion and currently brms has no specific family for underdispersion, may I trust the results from this model when convergence is achieved? 
Suppose I can trust these results. I want to assess the differences between the two groups. I thought about assessing the difference by analyzing the (Bayesian) 95%CI and the Evidence Ratio (ER).
95%CI: The output of model m gives me an estimate, x=.61, and a 95% Bayesian CI = [0.04, 1.29].
ER: I specified the model
m_prime < brm(y ~ 0+Group + (1Subj), data=dat, save_all_pars = TRUE, sample_prior=TRUE);
and used the ‘hypothesis’ function [that is, hypothesis(m_prime, “GroupB  GroupA > 0”)], and obtained the same estimate (x) and ER = 26.97.
So, it seems to me that the concepts of ‘credibility’ and ‘evidence’ are very different. Can you please tell me an opinion about how to report this betweengroups difference? Is there a better way to assess this difference using Bayesian tools?