  Bayes' Theorem can also help us understand the difference between rows.  The measures within a given row agree about the role of predictability in incremental confirmation.  In the top row the incremental evidence that E provides for H increases linearly with PH(E)/P(E), whereas in the bottom row it increases linearly with PH(E)/P~H(E). Thus, when probabilities measure total evidence what matters is the degree to which H exceeds T as a predictor of E, but when odds measure total evidence it is the degree to which H exceeds ~H as a predictor of E that matters.