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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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41Some 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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40Learning 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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39Error 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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39Some surprising facts about surprising factsStudies in History and Philosophy of Science Part A 45 79-86. 2014.A common intuition about evidence is that if data x have been used to construct a hypothesis H, then x should not be used again in support of H. It is no surprise that x fits H, if H was deliberately constructed to accord with x. The question of when and why we should avoid such “double-counting” continues to be debated in philosophy and statistics. It arises as a prohibition against data mining, hunting for significance, tuning on the signal, and ad hoc hypotheses, and as a preference for prede…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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36Frequentist 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. 2006.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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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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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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31Significance Tests: Vitiated or Vindicated by the Replication Crisis in Psychology?Review of Philosophy and Psychology 12 (1): 101-120. 2020.The crisis of replication has led many to blame statistical significance tests for making it too easy to find impressive looking effects that do not replicate. However, the very fact it becomes difficult to replicate effects when features of the tests are tied down actually serves to vindicate statistical significance tests. While statistical significance tests, used correctly, serve to bound the probabilities of erroneous interpretations of data, this error control is nullified by data-dredging…Read more
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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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31Cartwright, 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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29Introduction to recent issues in philosophy of statistics: evidence, testing, and applicationsSynthese 201 (4): 1-5. 2023.
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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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25Toward 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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25Statistical significance and its critics: practicing damaging science, or damaging scientific practice?Synthese 200 (3): 1-33. 2022.While the common procedure of statistical significance testing and its accompanying concept of p-values have long been surrounded by controversy, renewed concern has been triggered by the replication crisis in science. Many blame statistical significance tests themselves, and some regard them as sufficiently damaging to scientific practice as to warrant being abandoned. We take a contrary position, arguing that the central criticisms arise from misunderstanding and misusing the statistical tools…Read more
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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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25Causal Modeling, Explanation and Severe TestingIn 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. 331-375. 2010.
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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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16Scientific Reasoning: The Bayesian Approach. Colin Howson, Peter UrbachIsis 82 (4): 788-789. 1991.
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15Error and the Growth of Experimental KnowledgeUniversity of Chicago. 1996.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.
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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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13How 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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12Sociological versus metascientific views of technological Risk assessmentIn Kristin Shrader-Frechette & Laura Westra (eds.), Technology and Values, Rowman & Littlefield. pp. 217. 1997.
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11Acceptable Evidence: Science and Values in Risk Management (edited book)Oxford University Press USA. 1991.Discussions of science and values in risk management have largely focused on how values enter into arguments about risks, that is, issues of acceptable risk. Instead this volume concentrates on how values enter into collecting, interpreting, communicating, and evaluating the evidence of risks, that is, issues of the acceptability of evidence of risk. By focusing on acceptable evidence, this volume avoids two barriers to progress. One barrier assumes that evidence of risk is largely a matter of o…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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