 Any inductive logic that treats such arguments should address two challenges. (1) It should tell us which enumerative inductive arguments should count as good inductive arguments. In particular, it should tell us how to determine the appropriate degree p to which such premises inductively support the conclusion, for a given margin of error q. (2) It should demonstrably satisfy the  CoA.  That is, it should be provable (as a metatheorem) that if a conclusion expressing the approximate proportion for an attribute in a population is true, then it is very likely that sufficiently numerous random samples of the population will provide true premises for good inductive arguments that confer degrees of support p approaching 1 for that true conclusion—where, on pain of triviality, these sufficiently numerous samples are only a tiny fraction of a large population. The supplement on  Enumerative Inductions: Bayesian Estimation and Convergence,  shows precisely how a a Bayesian account of enumerative induction may meet these two challenges.