Scientific reasoning is—and ought to be—conducted in accordance with the axioms of probability. This Bayesian view—so called because of the central role it accords to a theorem first proved by Thomas Bayes in the late eighteenth ...

In this paper I offer an account of the meaning of must and can within the framework of possible worlds semantics. The paper consists of two parts: the first argues for a relative concept of modality underlying modal words like must and can in natural language. I give preliminary definitions of the meaning of these words which are formulated in terms of logical consequence and compatibility, respectively. The second part discusses one kind of insufficiency in the meaning definitions given in (...) the first part, which arise from the ex falso quodlibet paradox of logical consequence. In stepwise fashion, I make an attempt to avoid most of the consequences of this paradox for the meaning definitions of must and can. (shrink)

A natural way to think about epistemic possibility is as follows. When it is epistemically possible (for a subject) that p, there is an epistemically possible scenario (for that subject) in which p. The epistemic scenarios together constitute epistemic space. It is surprisingly difficult to make the intuitive picture precise. What sort of possibilities are we dealing with here? In particular, what is a scenario? And what is the relationship between scenarios and items of knowledge and belief? This chapter tries (...) to make sense of epistemic space. It explores different ways of making sense of scenarios and of their relationship to thought and language. It discusses some issues that arise and outlines some applications to the analysis of the content of thought and the meaning of language. (shrink)

By “epistemic modals,” I mean epistemic uses of modal words: adverbs like “necessarily,” “possibly,” and “probably,” adjectives like “necessary,” “possible,” and “probable,” and auxiliaries like “might,” “may,” “must,” and “could.” It is hard to say exactly what makes a word modal, or what makes a use of a modal epistemic, without begging the questions that will be our concern below, but some examples should get the idea across. If I say “Goldbach’s conjecture might be true, and it might be false,” (...) I am not endorsing the Cartesian view that God could have made the truths of arithmetic come out differently. I make the claim not because I believe in the metaphysical contingency of mathematics, but because I know that Goldbach’s conjecture has not yet been proved or refuted. Similarly, if I say “Joe can’t be running,” I am not saying that Joe’s constitution prohibits him from running, or that Joe is essentially a non-runner, or that Joe isn’t allowed to run. My basis for making the claim may be nothing more than that I see Joe’s running shoes hanging on a hook. (shrink)

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. (shrink)

Despite the widespread use of key concepts of the Neyman–Pearson (N–P) statistical paradigm—type I and II errors, significance levels, power, confidence levels—they have been the subject of philosophical controversy and debate for over 60 years. Both current and long-standing problems of N–P tests stem from unclarity and confusion, even among N–P adherents, as to how a test's (pre-data) error probabilities are to be used for (post-data) inductive inference as opposed to inductive behavior. We argue that the relevance of error probabilities (...) is to ensure that only statistical hypotheses that have passed severe or probative tests are inferred from the data. The severity criterion supplies a meta-statistical principle for evaluating proposed statistical inferences, avoiding classic fallacies from tests that are overly sensitive, as well as those not sensitive enough to particular errors and discrepancies. Introduction and overview 1.1 Behavioristic and inferential rationales for Neyman–Pearson (N–P) tests 1.2 Severity rationale: induction as severe testing 1.3 Severity as a meta-statistical concept: three required restrictions on the N–P paradigm Error statistical tests from the severity perspective 2.1 N–P test T(): type I, II error probabilities and power 2.2 Specifying test T() using p-values Neyman's post-data use of power 3.1 Neyman: does failure to reject H warrant confirming H? Severe testing as a basic concept for an adequate post-data inference 4.1 The severity interpretation of acceptance (SIA) for test T() 4.2 The fallacy of acceptance (i.e., an insignificant difference): Ms Rosy 4.3 Severity and power Fallacy of rejection: statistical vs. substantive significance 5.1 Taking a rejection of H0 as evidence for a substantive claim or theory 5.2 A statistically significant difference from H0 may fail to indicate a substantively important magnitude 5.3 Principle for the severity interpretation of a rejection (SIR) 5.4 Comparing significant results with different sample sizes in T(): large n problem 5.5 General testing rules for T(), using the severe testing concept The severe testing concept and confidence intervals 6.1 Dualities between one and two-sided intervals and tests 6.2 Avoiding shortcomings of confidence intervals Beyond the N–P paradigm: pure significance, and misspecification tests Concluding comments: have we shown severity to be a basic concept in a N–P philosophy of induction? (shrink)

I examine whether or not it is apt to attribute knowledge to groups of scientists. I argue that though research teams can be aptly described as having knowledge, communities of scientists identified with research fields, and the scientific community as a whole are not capable of knowing. Scientists involved in research teams are dependent on each other, and are organized in a manner to advance a goal. Such teams also adopt views that may not be identical to the views of (...) the individual members of the group. (shrink)

The growing availability of computer power and statistical software has greatly increased the ease with which practitioners apply statistical methods, but this has not been accompanied by attention to checking the assumptions on which these methods are based. At the same time, disagreements about inferences based on statistical research frequently revolve around whether the assumptions are actually met in the studies available, e.g., in psychology, ecology, biology, risk assessment. Philosophical scrutiny can help disentangle 'practical' problems of model validation, and conversely, (...) a methodology of statistical model validation can shed light on a number of issues of interest to philosophers of science. (shrink)

The Evidence for the Top Quark offers both a historical and philosophical perspective on an important recent discovery in particle physics: evidence for the elementary particle known as the top quark. Drawing on published reports, oral histories, and internal documents from the large collaboration that performed the experiment, Kent Staley explores in detail the controversies and politics that surrounded this major scientific result. At the same time the book seeks to defend an objective theory of scientific evidence based on error (...) probabilities. Such a theory provides an illuminating explication of the points of contention in the debate over the evidence for the top quark. Philosophers wishing to defend the objectivity of the results of scientific research must face unflinchingly the realities of scientific practice, and this book attempts to do precisely that. (shrink)

I argue that the Bayesian Way of reconstructing Duhem's problem fails to advance a solution to the problem of which of a group of hypotheses ought to be rejected or "blamed" when experiment disagrees with prediction. But scientists do regularly tackle and often enough solve Duhemian problems. When they do, they employ a logic and methodology which may be called error statistics. I discuss the key properties of this approach which enable it to split off the task of testing auxiliary (...) hypotheses from that of appraising a primary hypothesis. By discriminating patterns of error, this approach can at least block, if not also severely test, attempted explanations of an anomaly. I illustrate how this approach directs progress with Duhemian problems and explains how scientists actually grapple with them. (shrink)

This paper examines the role of evidential considerations in relation to pragmatic concerns in statements of group belief, focusing on scientific collaborations that are constituted in part by the aim of evaluating the evidence for scientific claims (evidential collaborations). Drawing upon a case study in high energy particle physics, I seek to show how pragmatic factors that enter into the decision to issue a group statement contribute positively to the epistemic functioning of such groups, contrary to the implications of much (...) of the existing discussion of group belief. I conclude by suggesting that applying social epistemological considerations to scientific collaborations could be practically beneficial, but only if an appropriately broad range of epistemic values is considered. (shrink)

: For philosophers of science interested in elucidating the social character of science, an important question concerns the manner in which and degree to which the objectivity of scientific knowledge is socially constituted. We address this broad question by focusing specifically on philosophical theories of evidence. To get at the social character of evidence, we take an interdisciplinary approach informed by categories from argumentation studies. We then test these categories by exploring their applicability to a case study from high-energy physics. (...) Our central claim is that normative philosophy of science must move beyond abstract theories of justification, confirmation, or evidence conceived impersonally and incorporate a theoretical perspective that includes dialogical elements, either as adjuncts to impersonal theories of evidence or as intrinsic to the cogency of scientific argumentation. (shrink)