Another epistemic approach to classical statistics is presented by Mayo (1996) and Mayo and Spanos (2011). Over the past decade or so, they have done much to push the agenda of classical statistics in the philosophy of science, which had become dominated by Bayesian statistics. Countering the original behaviourist tendencies of Neyman, the error statistical approach advances an epistemic reading of classical test and estimation procedures. Mayo and Spanos argue that classical procedures are best understood as inferential: they license inductive inferences. But they readily admit that the inferences are defeasible, i.e., they could lead us astray. Classical procedures are always associated with particular error probabilities, e.g., the probability of a false rejection or acceptance, or the probability of an estimator falling within a certain range. In the theory of Mayo and Spanos, these error probabilities obtain an epistemic role, because they are taken to indicate the reliability of the inferences licensed by the procedures.