  Though a mathematical triviality, Bayes' Theorem is of great value in calculating conditional probabilities because inverse probabilities are typically both easier to ascertain and less subjective than direct probabilities. People with different views about the unconditional probabilities of E and H often disagree about E's value as an indicator of H.  Even so, they can agree about the degree to which the hypothesis predicts the data if they know any of the following intersubjectively available facts: (a) E's objective probability given H, (b) the frequency with which events like E will occur if H is true, or (c) the fact that H logically entails E. Scientists often design experiments so that likelihoods can be known in one of these "objective" ways.  Bayes' Theorem then ensures that any dispute about the significance of the experimental results can be traced to "subjective" disagreements about the unconditional probabilities of H and E.