Posterior distributions. From them you can derive confidence intervals (or credible intervals as called within Bayesian stats) for your estimates and also set up tests. I'm not that familiar with testing within the Bayesian regime though so if someone else could add on this I would also be interested.
Kruschke goes over decision rules for dealing with the null hypothesis within the context of a 95% highest probability density interval, among other things.
well, there are other problems with the Bayesian approach. Look into uninformative priors for example. Bayesian stats are really powerful for some tasks and frequentist in others. I think it is always good to know a bit of both and when to use what.
