Includes bibliographical references (pages 303-318) and index.
CONTENTS NOTE
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Introduction -- The probability calculus -- The laws of probability -- Bayesian induction: deterministic theories -- Classical inference: significance tests and estimation -- Statistical inference in practice: Clinical trials -- Regression analysis -- Bayesian induction: statistical theories -- Finale: some general issues.
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SUMMARY OR ABSTRACT
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"Scientific Reasoning: The Bayesian Approach explains, in an accessible style, those elements of the probability calculus that are relevant to Bayesian methods, and argues that the probability calculus is best regarded as a species of logic. Howson and Urbach contrast the Bayesian with the 'classical' view that was so influential in the last century, and demonstrate that familiar classical procedures for evaluating statistical hypotheses, such as significance tests, point estimation, confidence intervals, and other techniques, provide an utterly false basis for scientific inference. They also expose the well-known non-probabilistic philosophies of Popper, Lakatos, and Kuhn as similarly unscientific. Scientific Reasoning shows how Bayesian theory, by contrast with these increasingly discredited approaches, provides a unified and highly satisfactory account of scientific method, an account which practicing scientists and all those interested in the sciences ought to master."--COVER.