  As these remarks make clear, one can interpret O(H) either as a measure of net evidence or as a measure of total evidence. To see the difference, imagine that 750 red balls and 250 black balls have been drawn at random and with replacement from an urn known to contain 10,000 red or black balls.  Assuming that this is our only evidence about the urn's contents, it is reasonable to set P(Red) = 0.75 and P(~Red) = 0.25.  On a probability-as-total-evidence reading, these assignments reflect both the fact that we have a great deal of evidence in favor of Red (namely, that 750 of 1,000 draws were red) and the fact that we have also have some evidence against it (namely, that 250 of the draws were black).  The net evidence for Red is then the disparity between our total evidence for Red and our total evidence against Red.  This can be expressed multiplicatively by saying that we have seen three times as many red draws as black draws, which is just to say that O(Red) = 3.  Alternatively, we can use O(Red) as a measure of the total evidence by taking our evidence for Red to be the ratio of red to black draws, rather than the total number of red draws, and our evidence for ~Red to be the ratio of black balls to red balls, rather than the total number of black draws. While the decision whether to use O as a measure total or net evidence makes little difference to questions about the absolute amount of total evidence for a hypothesis (since O(H) is an increasing function of P(H)), it can make a major difference when one is considering the incremental changes in total evidence brought about by conditioning on new information.