We may learn from our mistakes, but this work argues that, where experimental knowledge is concerned, we haven't begun to learn enough. It provides a critique of the subjective Bayesian view of statistical inference, and proposes the author's own error-statistical approach as a more robust framework for the epistemology of experiment. Deborah Mayo seeks to address the needs of researchers who work with statistical analysis, and simultaneously engages the basic philosophical problems of objectivity and rationality. Mayo has argued for an account of learning from error that goes beyond detecting logical inconsistencies. In this book, she presents her complete programme for how we learn about the world by being "shrewd inquisitors of error, white gloves off." Her approach should be relevant to philosophers, historians and sociologists of science, as well as researchers in the physical, biological and social sciences whose work depends upon statistical analysis.
Preface 1: Learning from Error 2: Ducks, Rabbits, and Normal Science: Recasting the Kuhn's-Eye View of Popper 3: The New Experimentalism and the Bayesian Way 4: Duhem, Kuhn, and Bayes 5: Models of Experimental Inquiry 6: Severe Tests and Methodological Underdetermination 7: The Experimental Basis from Which to Test Hypotheses: Brownian Motion 8: Severe Tests and Novel Evidence 9: Hunting and Snooping: Understanding the Neyman-Pearson Predesignationist Stance 10: Why You Cannot Be Just a Little Bit Bayesian 11: Why Pearson Rejected the Neyman-Pearson (Behavioristic) Philosophy and a Note on Objectivity in Statistics 12: Error Statistics and Peircean Error Correction 13: Toward an Error-Statistical Philosophy of Science References Index