 Here, then, suppose we are interested in the probability that the original result reported by Researcher A is true given Researcher B’s first replication failure. Earp and Trafimow represent this probability with the notation \(p(T\mid F)\) where p is a probability function, T represents the proposition that the original result is true and F represents Researcher B’s replication failure. According to Bayes’s theorem below, this probability is calculable from the aficionado’s degree of confidence that the original result is true prior to learning of the replication failure \(p(T)\), their degree of expectation of the replication failure on the condition that the original result is true \(p(T\mid F)\), and the degree to which they would unconditionally expect a replication failure prior to learning of the replication failure \(p(F)\):