It is a matter of debate whether any of this can be blamed on classical statistics.  Initially, Neyman was emphatic that their procedures could not be taken as inferences, or as in some other way pertaining to the epistemic status of the hypotheses. Their own statistical philosophy was strictly behaviorist (cf. Neyman 1957), and it may be argued that the problems disappear if only scientists abandon their faulty epistemic use of classical statistics. As explained in the foregoing, we can uncontroversially associate error rates with classical procedures, and so with the decisions that flow from these procedures. Hence, a behavioural and error-based understanding of classical statistics seems just fine. However, both statisticians and philosophers have argued that an epistemic reading of classical statistics is possible, and in fact preferable (e.g., Fisher 1955, Royall 1997). Accordingly, many have attempted to reinterpret or develop the theory, in order to align it with the epistemically oriented statistical practice of scientists (see Mayo 1996, Mayo and Spanos 2011, Spanos 2013b).