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36Response to Howson and LaudanPhilosophy of Science 64 (2): 323-333. 1997.A toast is due to one who slays Misguided followers of Bayes, And in their heart strikes fear and terror With probabilities of error! (E.L. Lehmann)
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25NewPerspectiveson (SomeOld) Problems of Frequentist StatisticsIn Deborah G. Mayo & Aris Spanos (eds.), Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science, Cambridge University Press. pp. 247. 2010.
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223How everyone can have a rare property: Response to Sober on frequency-dependent causationPhilosophy of Science 54 (2): 266-276. 1987.In a recent discussion note Sober (1985) elaborates on the argument given in Sober (1982) to show the inadequacy of Ronald Giere's (1979, 1980) causal model for cases of frequency-dependent causation, and denies that Giere's (1984) response avoids the problem he raises. I argue that frequency-dependent effects do not pose a problem for Giere's original causal model, and that all parties in this dispute have been guity of misinterpreting the counterfactual populations involved in applying Giere's…Read more
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59Evidence as Passing Severe Tests: Highly Probable versus Highly Probed HypothesesIn P. Achinstein (ed.), Scientific Evidence: Philosophical Theories & Applications, The Johns Hopkins University Press. pp. 95--128. 2005.
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62The Philosophical Relevance of StatisticsPSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1980. 1980.While philosophers have studied probability and induction, statistics has not received the kind of philosophical attention mathematics and physics have. Despite increasing use of statistics in science, statistical advances have been little noted in the philosophy of science literature. This paper shows the relevance of statistics to both theoretical and applied problems of philosophy. It begins by discussing the relevance of statistics to the problem of induction and then discusses the reasoning…Read more
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232Behavioristic, evidentialist, and learning models of statistical testingPhilosophy of Science 52 (4): 493-516. 1985.While orthodox (Neyman-Pearson) statistical tests enjoy widespread use in science, the philosophical controversy over their appropriateness for obtaining scientific knowledge remains unresolved. I shall suggest an explanation and a resolution of this controversy. The source of the controversy, I argue, is that orthodox tests are typically interpreted as rules for making optimal decisions as to how to behave--where optimality is measured by the frequency of errors the test would commit in a long …Read more
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380Severe testing as a basic concept in a neyman–pearson philosophy of inductionBritish Journal for the Philosophy of Science 57 (2): 323-357. 2006.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 ar…Read more
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26Principles of inference and their consequencesIn David Corfield & Jon Williamson (eds.), Foundations of Bayesianism, Kluwer Academic Publishers. pp. 381--403. 2001.
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41Learning from error, severe testing, and the growth of theoretical knowledgeIn Deborah G. Mayo & Aris Spanos (eds.), Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science, Cambridge University Press. pp. 28. 2009.
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328Experimental practice and an error statistical account of evidencePhilosophy of Science 67 (3): 207. 2000.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…Read more
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92Duhem's problem, the bayesian way, and error statistics, or "what's belief got to do with it?"Philosophy of Science 64 (2): 222-244. 1997.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 fr…Read more
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107The New Experimentalism, Topical Hypotheses, and Learning from ErrorPSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1994 270-279. 1994.An important theme to have emerged from the new experimentalist movement is that much of actual scientific practice deals not with appraising full-blown theories but with the manifold local tasks required to arrive at data, distinguish fact from artifact, and estimate backgrounds. Still, no program for working out a philosophy of experiment based on this recognition has been demarcated. I suggest why the new experimentalism has come up short, and propose a remedy appealing to the practice of sta…Read more
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37An error in the argument from conditionality and sufficiency to the likelihood principleIn Deborah G. Mayo & Aris Spanos (eds.), Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science, Cambridge University Press. pp. 305. 2009.
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31Sins of the epistemic probabilist : exchanges with Peter AchinsteinIn Deborah G. Mayo & Aris Spanos (eds.), Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science, Cambridge University Press. pp. 189. 2009.
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76Novel work on problems of novelty? Comments on HudsonStudies in History and Philosophy of Science Part B: Studies in History and Philosophy of Modern Physics 34 (1): 131-134. 2003.
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16How to Discount Double-Counting When It Counts: Some ClarificationsBritish Journal for the Philosophy of Science 59 (4): 857-879. 2008.The issues of double-counting, use-constructing, and selection effects have long been the subject of debate in the philosophical as well as statistical literature. I have argued that it is the severity, stringency, or probativeness of the test—or lack of it—that should determine if a double-use of data is admissible. Hitchcock and Sober ([2004]) question whether this ‘severity criterion' can perform its intended job. I argue that their criticisms stem from a flawed interpretation of the severity…Read more
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11Explanation and testing exchanges with Clark GlymourIn Deborah G. Mayo & Aris Spanos (eds.), Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science, Cambridge University Press. pp. 351. 2009.
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32Cartwright, Causality, and CoincidencePSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1986. 1986.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…Read more
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68Severe tests, arguing from error, and methodological underdeterminationPhilosophical Studies 86 (3): 243-266. 1997.
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49Objectivity and conditionality in frequentist inferenceIn Deborah G. Mayo & Aris Spanos (eds.), Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science, Cambridge University Press. pp. 276. 2009.
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36Philosophy of Science AssociationIn Richard Boyd, Philip Gasper & J. D. Trout (eds.), The Philosophy of Science, Mit Press. pp. 58--4. 1991.
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136Models of group selectionPhilosophy of Science 54 (4): 515-538. 1987.The key problem in the controversy over group selection is that of defining a criterion of group selection that identifies a distinct causal process that is irreducible to the causal process of individual selection. We aim to clarify this problem and to formulate an adequate model of irreducible group selection. We distinguish two types of group selection models, labeling them type I and type II models. Type I models are invoked to explain differences among groups in their respective rates of pr…Read more
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55Error statistics and learning from error: Making a virtue of necessityPhilosophy of Science 64 (4): 212. 1997.The error statistical account of testing uses statistical considerations, not to provide a measure of probability of hypotheses, but to model patterns of irregularity that are useful for controlling, distinguishing, and learning from errors. The aim of this paper is (1) to explain the main points of contrast between the error statistical and the subjective Bayesian approach and (2) to elucidate the key errors that underlie the central objection raised by Colin Howson at our PSA 96 Symposium
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251Ducks, Rabbits, and Normal Science: Recasting the Kuhn’s-Eye View of Popper’s Demarcation of ScienceBritish Journal for the Philosophy of Science 47 (2): 271-290. 1996.Kuhn maintains that what marks the transition to a science is the ability to carry out ‘normal’ science—a practice he characterizes as abandoning the kind of testing that Popper lauds as the hallmark of science. Examining Kuhn's own contrast with Popper, I propose to recast Kuhnian normal science. Thus recast, it is seen to consist of severe and reliable tests of low-level experimental hypotheses (normal tests) and is, indeed, the place to look to demarcate science. While thereby vindicating Kuh…Read more
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14The Objective Epistemic Probabilist and the Severe TesterIn Gregory J. Morgan (ed.), Philosophy of Science Matters: The Philosophy of Peter Achinstein, Oxford University Press. pp. 135. 2011.
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9An ad hoc save of a theory of adhocness? Exchanges with John WorrallIn Deborah G. Mayo & Aris Spanos (eds.), Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science, Cambridge University Press. 2009.
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42Some problems with Chow's problems with powerBehavioral and Brain Sciences 21 (2): 212-213. 1998.Chow correctly pinpoints several confusions in the criticisms of statistical hypothesis testing but his book is considerably weakened by its own confusions about concepts of testing (perhaps owing to an often very confusing literature). My focus is on his critique of power analysis (Ch. 6). Having denied that NHSTP considers alternative statistical hypotheses, and having been misled by a quotation from Cohen, Chow finds power analysis conceptually suspect.
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23On After-Trial Criticisms of Neyman-Pearson Theory of StatisticsPSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1982. 1982.Despite its widespread use in science, the Neyman-Pearson Theory of Statistics (NPT) has been rejected as inadequate by most philosophers of induction and statistics. They base their rejection largely upon what the author refers to as after-trial criticisms of NPT. Such criticisms attempt to show that NPT fails to provide an adequate analysis of specific inferences after the trial is made, and the data is known. In this paper, the key types of after-trial criticisms are considered and it is argu…Read more
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Introduction and backgroundIn Deborah G. Mayo & Aris Spanos (eds.), Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science, Cambridge University Press. 2009.
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