Kent Staley

This book guides readers by gradual steps through the central concepts and debates in the philosophy of science. Using concrete examples from the history of science, Kent W. Staley shows how seemingly abstract philosophical issues are relevant to important aspects of scientific practice. Structured in two parts, the book first tackles the central concepts of the philosophy of science, such as the problem of induction, falsificationism, and underdetermination, and important figures and movements, such as the logical empiricists, Thomas Kuhn, and Paul Feyerabend. The second part turns to contemporary debates in the philosophy of science, such as scientific realism, explanation, the role of values in science, the different views of scientific inference, and probability. This broad yet detailed overview will give readers a strong grounding whilst also providing opportunities for further exploration. It will be of particular interest to students of philosophy, the philosophy of science, and science.

Experimenters sometimes insist that it is unwise to examine data before determining how to analyze them, as it creates the potential for biased results. I explore the rationale behind this methodological guideline from the standpoint of an error statistical theory of evidence, and I discuss a method of evaluating evidence in some contexts when this predesignation rule has been violated. I illustrate the problem of potential bias, and the method by which it may be addressed, with an example from the search for the top quark. A point in favor of the error statistical theory is its ability, demonstrated here, to explicate such methodological problems and suggest solutions, within the framework of an objective theory of evidence.

Some accounts of evidence regard it as an objective relationship holding between data and hypotheses, perhaps mediated by a testing procedure. Mayo’s error-statistical theory of evidence is an example of such an approach. Such a view leaves open the question of when an epistemic agent is justified in drawing an inference from such data to a hypothesis. Using Mayo’s account as an illustration, I propose a framework for addressing the justification question via a relativized notion, which I designate security , meant to conceptualize practices aimed at the justification of inferences from evidence. I then show how the notion of security can be put to use by showing how two quite different theoretical approaches to model criticism in statistics can both be viewed as strategies for securing claims about statistical evidence

It is sometimes held that facts confirm a hypothesis only if they were not used in the construction of that hypothesis. This requirement of "use novelty" introduces a historical aspect into the assessment of evidence claims. I examine a methodological principle invoked by physicists in the experimental search for the top quark that bears a striking resemblance to this view. However, this principle is better understood, both historically and philosophically, in terms of the need to conduct a severe test than in terms of use novelty. Nevertheless, a historical factor remains in the assessment of some evidence claims

Some prominent accounts of scientific evidence treat evidence as an unrelativized concept. But whether belief in a hypothesis is justified seems relative to the epistemic situation of the believer. The issue becomes yet more complicated in the context of group epistemic agents, for then one confronts the problem of relativizing to an epistemic situation that may include conflicting beliefs. As a step toward resolution of these difficulties, an ideal of justification is here proposed that incorporates both an unrelativized evidence requirement and the requirement of the security of the evidence on which a conclusion from data is based. The latter requirement incorporates the consideration of epistemic modal statements.

The recent discovery of a Higgs boson prompted increased attention of statisticians and philosophers of science to the statistical methodology of High Energy Physics. Amidst long-standing debates within the field, HEP has adopted a mixed statistical methodology drawing upon both frequentist and Bayesian methods, but with standard frequentist techniques such as significance testing and confidence interval estimation playing a primary role. Physicists within HEP typically deny that their methodological decisions are guided by philosophical convictions, but are instead based on “pragmatic” considerations, thus distancing themselves from what they perhaps perceive as an ongoing pitched ideological battle between frequentists and Bayesians. Here I argue that there is a philosophical orientation to HEP that is neither exclusively frequentist nor Bayesian, but that lies squarely in the tradition of philosophical pragmatism. I further argue that understanding the statistical methodology of HEP through the perspective of pragmatism clarifies the role of and rationale for significance testing in the search for new phenomena such as the Higgs boson.

Many philosophers have claimed that evidence for a theory is better when multiple independent tests yield the same result, i.e., when experimental results are robust. Little has been said about the grounds on which such a claim rests, however. The present essay presents an analysis of the evidential value of robustness that rests on the fallibility of assumptions about the reliability of testing procedures and a distinction between the strength of evidence and the security of an evidence claim. Robustness can enhance the security of an evidence claim either by providing what I call second-order evidence, or by providing back-up evidence for a hypothesis.

: Peter Galison has recently claimed that twentieth-century microphysics has been pursued by two distinct experimental traditions--the image tradition and the logic tradition--that have only recently merged into a hybrid tradition. According to Galison, the two traditions employ fundamentally different forms of experimental argument, with the logic tradition using statistical arguments, while the image tradition strives for non-statistical demonstrations based on compelling ("golden") single events. I show that discoveries in both traditions have employed the same statistical form of argument, even when basing discovery claims on single, golden events. Where Galison sees an epistemic divide between two communities that can only be bridged by a creole- or pidgin-like "interlanguage," there is in fact a shared commitment to a statistical form of experimental argument

: Evidence claims depend on fallible assumptions. Three strategies for making true evidence claims in spite of this fallibility are strengthening the support for those assumptions, weakening conclusions, and using multiple independent tests to produce robust evidence. Reliability itself, understood in frequentist terms, does not explain the usefulness of all three strategies; robustness, in particular, sometimes functions in a way that is not well-characterized in terms of reliability. I argue that, in addition to reliability, the security of evidence claims is of epistemic value, where an evidence claim is secure relative to an epistemic situation if it remains true in all scenarios that are epistemically possible relative to that epistemic situation.

Among the evidence concepts that Peter Achinstein has defended is one (ES-evidence) that he claims is based on the idea of providing an epistemic justification for belief. To the contrary, This chapter argue that his account of evidence lacks a substantive account of epistemic justification. To remedy this, This chapter propose an ideal of justification that this chapter argue is compatible with his account of evidence. This chapter's proposal does not introduce any new notion of evidence, but articulates instead a relativized, non-probabilistic notion (the _securing_ of evidence) that helps make sense of certain strategies by which scientists justify their claims about what evidence they have and their inferences from that evidence. This chapter illustrate this framework by drawing upon a historical episode about which Achinstein himself has written: Heinrich Hertz's cathode ray researches.

I contrast two modes of error-elimination relevant to evaluating evidence in accounts that emphasize frequentist reliability. The contrast corresponds to that between the use of of a reliable inference procedure and the critical scrutiny of a procedure with regard to its reliability, in light of what is and is not known about the setting in which the procedure is used. I propose a notion of security as a category of evidential assessment for the latter. In statistical settings, robustness theory and misspecification testing exemplify two distinct strategies for securing statistical inferences.