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141Models 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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56Error 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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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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254Ducks, 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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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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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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24On 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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94Error statistical modeling and inference: Where methodology meets ontologySynthese 192 (11): 3533-3555. 2015.In empirical modeling, an important desiderata for deeming theoretical entities and processes as real is that they can be reproducible in a statistical sense. Current day crises regarding replicability in science intertwines with the question of how statistical methods link data to statistical and substantive theories and models. Different answers to this question have important methodological consequences for inference, which are intertwined with a contrast between the ontological commitments o…Read more
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40Error and the law : exchanges with Larry LaudanIn 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. 397. 2009.
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26Toward a More Objective Understanding of the Evidence of Carcinogenic RiskPSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1988. 1988.I argue that although the judgments required to reach statistical risk assessments may reflect policy values, it does not follow that the task of evaluating whether a given risk assessment is warranted by the evidence need also be imbued with policy values. What has led many to conclude otherwise, I claim, stems from misuses of the statistical testing methods involved. I set out rules for interpreting what specific test results do and do not say about the extent of a given risk. By providing a m…Read more
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3Can scientific theories be warranted with severity? Exchanges with Alan ChalmersIn 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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The Methods of Science: No Dogs or Philosophers AllowedDVD. forthcoming.What is science, and what is it not? Is falsifiability the key to drawing this line? How and why does science work? Should we worry whether science is talking about a "real" world? And should we stop thinking there is a single thing we can call "the scientific method"? With Deborah Mayo, Robert Rynasiewicz, and Drew Arrowood
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82Philosophical Scrutiny of Evidence of Risks: From Bioethics to BioevidencePhilosophy of Science 73 (5): 803-816. 2006.We argue that a responsible analysis of today's evidence-based risk assessments and risk debates in biology demands a critical or metascientific scrutiny of the uncertainties, assumptions, and threats of error along the manifold steps in risk analysis. Without an accompanying methodological critique, neither sensitivity to social and ethical values, nor conceptual clarification alone, suffices. In this view, restricting the invitation for philosophical involvement to those wearing a "bioethicist…Read more
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122Novel evidence and severe testsPhilosophy of Science 58 (4): 523-552. 1991.While many philosophers of science have accorded special evidential significance to tests whose results are "novel facts", there continues to be disagreement over both the definition of novelty and why it should matter. The view of novelty favored by Giere, Lakatos, Worrall and many others is that of use-novelty: An accordance between evidence e and hypothesis h provides a genuine test of h only if e is not used in h's construction. I argue that what lies behind the intuition that novelty matter…Read more
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38Frequentist statistics as a theory of inductive 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. 2009.After some general remarks about the interrelation between philosophical and statistical thinking, the discussion centres largely on significance tests. These are defined as the calculation of p-values rather than as formal procedures for ‘acceptance‘ and ‘rejection‘. A number of types of null hypothesis are described and a principle for evidential interpretation set out governing the implications of p- values in the specific circumstances of each application, as contrasted with a long-run inter…Read more
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47Toward progressive critical rationalism : exchanges with Alan MusgraveIn 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. 113. 2009.
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60Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science (edited book)Cambridge University Press. 2009.Although both philosophers and scientists are interested in how to obtain reliable knowledge in the face of error, there is a gap between their perspectives that has been an obstacle to progress. By means of a series of exchanges between the editors and leaders from the philosophy of science, statistics and economics, this volume offers a cumulative introduction connecting problems of traditional philosophy of science to problems of inference in statistical and empirical modelling practice. Phil…Read more
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16Scientific Reasoning: The Bayesian Approach. Colin Howson, Peter UrbachIsis 82 (4): 788-789. 1991.
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75An objective theory of statistical testingSynthese 57 (3). 1983.Theories of statistical testing may be seen as attempts to provide systematic means for evaluating scientific conjectures on the basis of incomplete or inaccurate observational data. The Neyman-Pearson Theory of Testing (NPT) has purported to provide an objective means for testing statistical hypotheses corresponding to scientific claims. Despite their widespread use in science, methods of NPT have themselves been accused of failing to be objective; and the purported objectivity of scientific cl…Read more
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96Peircean Induction and the Error-Correcting ThesisTransactions of the Charles S. Peirce Society 41 (2). 2005.
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72In defense of the Neyman-Pearson theory of confidence intervalsPhilosophy of Science 48 (2): 269-280. 1981.In Philosophical Problems of Statistical Inference, Seidenfeld argues that the Neyman-Pearson (NP) theory of confidence intervals is inadequate for a theory of inductive inference because, for a given situation, the 'best' NP confidence interval, [CIλ], sometimes yields intervals which are trivial (i.e., tautologous). I argue that (1) Seidenfeld's criticism of trivial intervals is based upon illegitimately interpreting confidence levels as measures of final precision; (2) for the situation which…Read more
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56Error and the growth of experimental knowledgeInternational Studies in the Philosophy of Science 15 (1): 455-459. 1996.
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101The error statistical philosopher as normative naturalistSynthese 163 (3). 2008.We argue for a naturalistic account for appraising scientific methods that carries non-trivial normative force. We develop our approach by comparison with Laudan’s (American Philosophical Quarterly 24:19–31, 1987, Philosophy of Science 57:20–33, 1990) “normative naturalism” based on correlating means (various scientific methods) with ends (e.g., reliability). We argue that such a meta-methodology based on means–ends correlations is unreliable and cannot achieve its normative goals. We suggest an…Read more
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167Did Pearson reject the Neyman-Pearson philosophy of statistics?Synthese 90 (2). 1992.I document some of the main evidence showing that E. S. Pearson rejected the key features of the behavioral-decision philosophy that became associated with the Neyman-Pearson Theory of statistics (NPT). I argue that NPT principles arose not out of behavioral aims, where the concern is solely with behaving correctly sufficiently often in some long run, but out of the epistemological aim of learning about causes of experimental results (e.g., distinguishing genuine from spurious effects). The view…Read more
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54Error and the Growth of Experimental KnowledgePhilosophical Review 107 (2): 324. 1998.Once upon a time, logic was the philosopher’s tool for analyzing scientific reasoning. Nowadays, probability and statistics have largely replaced logic, and their most popular application—Bayesianism—has replaced the qualitative deductive relationship between a hypothesis h and evidence e with a quantitative measure of h’s probability in light of e.
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37Response 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. 2009.
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224How 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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