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Switch to: References
Citations of:
Did Pearson reject the Neyman-Pearson philosophy of statistics?
Synthese 90 (2):233 - 262 (1992)
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I present a new proof of the likelihood principle that avoids two responses to a well-known proof due to Birnbaum ([1962]). I also respond to arguments that Birnbaum’s proof is fallacious, which if correct could be adapted to this new proof. On the other hand, I urge caution in interpreting proofs of the likelihood principle as arguments against the use of frequentist statistical methods. 1 Introduction2 The New Proof3 How the New Proof Addresses Proposals to Restrict Birnbaum’s Premises4 A Response (...) |
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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 , (...) |
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In guarding against inferential mistakes, experimental comparative cognition errs on the side of underattributing sophisticated cognition to animals, or what I refer to as the underattribution bias. I propose eliminating this bias by altering the method of choosing the default, or null, hypothesis. Rather than choosing the most parsimonious null hypothesis, as is current practice, I argue for choosing the best-evidenced hypothesis. Doing so at once preserves the risk-controlling structure of the current statistical paradigm and introduces a sensitivity to probability-conferring (...) |
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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 (...) |
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Recent breakthroughs in the history and sociology of science have begun to help us to appreciate the vast complexity and intricate character of empirical endeavours in the sciences. The days when philosophers could blandly gesture towards “observation statements” or “falsification,” as if they were some readily understood phenomena or set of procedures, are gone, happily. This does not mean we can merely use the Duhem-Quine thesis as a shibboleth, however: we are now much more sensitive to immense difficulties in establishing (...) |
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