 Neyman-Pearson statistical hypothesis testing is a very common methodology taught in statistics courses and used by practicing biologists. However, some philosophers and statisticians reject it for an alternative, Bayesianism (Howson &amp; Urbach 2006). According to Bayesianism, we neither accept nor reject hypotheses (though one can work out a notion of acceptance in the framework (see Levi 1974; Maher 1993)). Rather, we compare the probability of hypothesis with regard to some evidence and determine how it affects its probability (see entry on  philosophy of statistics).  Whether the problem of inductive risk disappears in a Bayesian inferential frameworks is an interesting question. Insofar as credences (i.e., subjective degrees of belief) are affected by attitudes towards risk, then arguably the problem arises in a different guise.