  Verbally, this says that the posterior odds on \(M\) (that is, the ratio of the posterior probability of \(M\) to the posterior probability of its negation) equal the product of the prior odds and the Bayes factor. More colloquially, \(M\) becomes more plausible when we take into account evidence \(E\) that is more to be expected if \(M\) is true than if \(M\) is false. A categorical argument of this sort would involve plugging in values (either point-valued or interval-valued) for each term in this equation and concluding that \(P(M\mid E) \gt k\), where \(k\) is some constant with a value greater than or equal to 0.5. The evaluation of such an argument is likely to turn principally on the relative magnitudes of \(P(M)\) and \(P(E\mid {\sim}M)\), since in many contexts the disputants will grant that the other two probabilities that appear on the right side of the equation—\(P({\sim}M)\) and \(P(E\mid M)\)—are very close to 1. A confirmatory probabilistic argument might proceed from the same premises but dispense with the ratio of the priors, focusing on the fact that the ratio \(P(E\mid M)/P(E\mid {\sim}M)\) is top heavy.