Probability theory as logic agrees with Fisher in spirit; that is, it gives us automatically both point and interval estimates from a single calculation. The distinction commonly made between hypothesis testing and parameter estimation is considerably greater than that which concerned Fisher; yet it too is, from our point of view, not a real difference. When we have only a small number of discrete hypotheses {H1, …, Hn} to consider, we usually want to pick out a specific one of them as the most likely in that set, in the light of the prior information and data. The cases n = 2 and n = 3 were examined in some detail in Chapter 4, and larger n is in principle a straightforward and rather obvious generalization.

When the hypotheses become very numerous, however, a different approach seems called for. A set of discrete hypotheses can always be classified by assigning one or more numerical indices which identify them, as in Ht (1 ≤ t ≤ n), and if the hypotheses are very numerous one can hardly avoid doing this.