This eye-opener illuminates controversies surrounding widely used statistical methods across the physical, social, and biological sciences. New solutions to philosophical problems of induction, falsification, science vs. pseudoscience are put to work to let statisticians and reproducibility researchers get beyond hardened conceptual disagreements.
This eye-opener illuminates controversies surrounding widely used statistical methods across the physical, social, and biological sciences. New solutions to philosophical problems of induction, falsification, science vs. pseudoscience are put to work to let statisticians and reproducibility researchers get beyond hardened conceptual disagreements.
Deborah G. Mayo is Professor Emerita in the Department of Philosophy at Virginia Tech. Author of Error and the Growth of Experimental Knowledge (1996), she won the 1998 Lakatos Prize for an outstanding contribution to philosophy of science. She directed the NEH Summer Seminar (1999) on Philosophy of Experimental Inference. She co-founded, with G. W. Chatfield, the Fund for Experimental Reasoning, Reliability and Objectivity and Rationality (E.R.R.O.R) in 2006 which has co-sponsored 10 conferences, workshops and distinguished lecture series. She's a visiting professor at the London School of Economics and Political Science, Centre for the Philosophy of Natural and Social Science (CPNSS) (2007-present).
Inhaltsangabe
Preface Excursion 1. How to Tell What's True about Statistical Inference: Tour I. Beyond probabilism and performance Tour II. Error probing tools vs. logics of evidence Excursion 2. Taboos of Induction and Falsification: Tour I. Induction and confirmation Tour II. Falsification, pseudoscience, induction Excursion 3. Statistical Tests and Scientific Inference: Tour I. Ingenious and severe tests Tour II. It's the methods, stupid Tour III. Capability and severity: deeper concepts Excursion 4. Objectivity and Auditing: Tour I. The myth of 'the myth of objectivity' Tour II. Rejection fallacies: whose exaggerating what? Tour III. Auditing: biasing selection effects and randomization Tour IV. More auditing: objectivity and model checking Excursion 5. Power and Severity: Tour I. Power: pre-data and post-data Tour II. How not to corrupt power Tour III. Deconstructing the N-P vs. Fisher debates Excursion 6. (Probabilist) Foundations Lost, (Probative) Foundations Found: Tour I. What ever happened to Bayesian foundations? Tour II. Pragmatic and error statistical Bayesians Souvenir (Z) farewell References Index.
Preface Excursion 1. How to Tell What's True about Statistical Inference: Tour I. Beyond probabilism and performance Tour II. Error probing tools vs. logics of evidence Excursion 2. Taboos of Induction and Falsification: Tour I. Induction and confirmation Tour II. Falsification, pseudoscience, induction Excursion 3. Statistical Tests and Scientific Inference: Tour I. Ingenious and severe tests Tour II. It's the methods, stupid Tour III. Capability and severity: deeper concepts Excursion 4. Objectivity and Auditing: Tour I. The myth of 'the myth of objectivity' Tour II. Rejection fallacies: whose exaggerating what? Tour III. Auditing: biasing selection effects and randomization Tour IV. More auditing: objectivity and model checking Excursion 5. Power and Severity: Tour I. Power: pre-data and post-data Tour II. How not to corrupt power Tour III. Deconstructing the N-P vs. Fisher debates Excursion 6. (Probabilist) Foundations Lost, (Probative) Foundations Found: Tour I. What ever happened to Bayesian foundations? Tour II. Pragmatic and error statistical Bayesians Souvenir (Z) farewell References Index.
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