 In other words, we only suppose that for each of m observations, \(c_k, h_i\) says observation \(c_k\) has at least a small likelihood \(\delta\) of producing one of the outcomes \(o_{ku}\) that \(h_j\) says is impossible. If the number m of such experiments or observations is large enough (or if the lower bound \(\delta\) on the likelihoods of getting such outcomes is large enough), and if \(h_i\) (together with \(b\cdot c^n)\) is true, then it is highly likely that one of the outcomes held to be impossible by \(h_j\) will actually occur. If one of these outcomes does occur, then the likelihood ratio for \(h_j\) as compared to over \(h_i\) will become 0. According to Bayes’ Theorem, when this happen, \(h_j\) is absolutely refuted by the evidence—its posterior probability becomes 0.