There are multiple proposed interpretations of probability theory: one such interpretation is true-false logic under uncertainty. Cox's Theorem is a representation theorem that states, under a certain set of axioms describing the meaning of uncertainty, that every true-false logic under uncertainty is isomorphic to conditional probability theory. This result was used by Jaynes to develop a philosophical framework in which statistical inference under uncertainty should be conducted through the use of probability, via Bayes' Rule. Unfortunately, most existing correct proofs of Cox's Theorem require restrictive assumptions: for instance, many do not apply even to the simple example of rolling a pair of fair dice. We offer a new axiomatization by replacing various technical conditions with an axiom stating that our theory must be consistent with respect to repeated events. We discuss the implications of our results, both for the philosophy of probability and for the philosophy of statistics.