  The quantity LR(H, E) = PH(E)/P~H(E) that appears in (1.5) is the likelihood ratio of H given E. In testing situations like the one described in Example 1, the likelihood ratio is the test's true positive rate divided by its false positive rate: LR = sensitivity/(1 − specificity). As with the probability ratio, we can construe the likelihood ratio as a measure of the degree to which H predicts E. Instead of comparing E's probability given H with its unconditional probability, however, we now compare it with its probability conditional on ~H.  LR(H, E) is thus the degree to which the hypothesis surpasses its negation as a predictor of the data.  Once more, Bayes' Theorem tells us how to factor conditional probabilities into unconditional probabilities and measures of predictive power.