Deborah Mayo

While this chapter and Achinstein agree that an account of evidence should be objective, not subjective, and empirical, not a priori, Achinstein has argued that we may reach conflicting assessments of evidence. There are cases where little has been done to rule out threats of error to H—as severity requires—that Achinstein construes as good evidence for H. Conversely, data x may fail to count as evidence for H, according to Achinstein's epistemic probabilist, even where H has passed a severe test by dint of x. We may call this the “highly probed vs. highly probable” conflict. This chapter argues, based on Achinstein's most recent installment to this debate, that the severity account is more in sync with the Achinstein's goals and the special features of his brand of Bayesianism. This chapter also considers how Achinstein's defense of Mill's account of induction gives further grounds for viewing his objective epistemologist as a severe tester.

Cartwright argues for being a realist about theoretical entities but non-realist about theoretical laws. Her reason is that while the former involves causal explanation, the latter involves theoretical explanation; and inferences to causes, unlike inferences to theories, can avoid the redundancy objection--that one cannot rule out alternatives that explain the phenomena equally well. I sketch Cartwright's argument for inferring the most probable cause, focusing on Perrin's inference to molecular collisions as the cause of Brownian motion. I argue that either the inference she describes fails to be a genuinely causal one, or else it too is open to the redundancy objection. However, I claim there is a way to sustain Cartwright's main insight: that it is possible to avoid the redundancy objection in certain cases of causal inference from experiments (e.g., Perrin). But, contrary to Cartwright, I argue that in those cases one is able to infer causes only by inferring some theoretical laws about how they produce experimental effects.