In seeking general accounts of evidence, confirmation, or inference, philosophers have looked to logical relationships between evidence and hypotheses. Such logics of evidential relationship, whether hypothetico-deductive, Bayesian, or instantiationist fail to capture or be relevant to scientific practice. They require information that scientists do not generally have (e.g., an exhaustive set of hypotheses), while lacking slots within which to include considerations to which scientists regularly appeal (e.g., error probabilities). Building on my co-symposiasts contributions, I suggest some directions in which a (...) new and more adequate philosophy of evidence can move. (shrink)

1. Introduction. The papers by Hellman and Mayo offer up a rich menu of problems and proposed solutions, so there is much here for a friendly critic to fasten on. In order to bring a modicum of focus to my commentary, I shall limit my remarks to the Duhem problem and its radiations in epistemology and methodology. Both Mayo and Hellman claim to have solutions to that hoary old problem and they tout these solutions as key indicators of the explanatory (...) power of their respective technical epistemologies, whether Bayesian or Neyman/Pearsonian. Like Mayo, I shall be arguing that the Bayesian treatment of Duhem's problem is no solution at all; that, indeed, it fails to grapple with the core challenges posed by the purported ambiguities of falsification. My response to Mayo's more detailed, and I think more right-headed, treatment of the Duhem problem will be more complex. While I believe that she is moving in the right direction in many respects, I think that she fails to see one key dimension of the Duhemian conundrum. Indeed, she risks solving not Duhem's problem but quite a different one. I shall gently try to encourage her to steer her way back towards the central task. (shrink)

This text provides a critique of the subjective Bayesian view of statistical inference, and proposes the author's own error-statistical approach as an alternative framework for the epistemology of experiment. It seeks to address the needs of researchers who work with statistical analysis.

I consider the error-statistical account as both a theory of evidence and as a theory of inference. I seek to show how inferences regarding the truth of hypotheses can be upheld by avoiding a certain kind of alternative hypothesis problem. In addition to the testing of assumptions behind the experimental model, I discuss the role of judgments of implausibility. A benefit of my analysis is that it reveals a continuity in the application of error-statistical assessment to low-level empirical hypotheses and (...) highly general theoretical principles. This last point is illustrated with a brief sketch of the issues involved in the parametric framework analysis of tests of physical theories such as General Relativity and of fundamental physical principles such as the Einstein Equivalence Principle. (shrink)