In the foregoing we have seen how classical and Bayesian statistics differ. But the two major approaches to statistics also have a lot in common. Most importantly, all statistical procedures rely on the assumption of a statistical model, here referring to any restricted set of statistical hypotheses.  Moreover, they are both aimed at delivering a verdict over these hypotheses.  For example, a classical likelihood ratio test considers two hypotheses, \(h\) and \(h'\), and then offers a verdict of rejection and acceptance, while a Bayesian comparison delivers a posterior probability over these two hypotheses. Whereas in Bayesian statistics the model presents a very strong assumption, classical statistics does not endow the model with a special epistemic status: they are simply the hypotheses currently entertained by the scientist. But across the board, the adoption of a model is absolutely central to any statistical procedure.