How do we go about weighing evidence, testing hypotheses, and making inferences? According to this work, we infer the hypothesis that would, if correct, provide the best explanation of the available evidence. Articulating the model of inference to the best explanation requires an account of what makes one explanation better than another. The author of this treatise distinguishes between the explanation best supported by the evidence - the most likely explanation - and the explanation that would, if true, provide the most understanding. He argues that an illuminating version of inference to the best explanation must rely on the latter notion, and provides an account of what makes one explanation better than another by analyzing the structure of contrastive explanations.
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How do we go about weighing evidence, testing hypotheses, and making inferences? According to the model of Inference to the Best Explanation, we work out what to infer from the evidence by thinking about what would actually explain that evidence, and we take the ability of a hypothesis to explain the evidence as a sign that the hypothesis is correct. In Inference to the Best Explanation, Peter Lipton gives this important and influential idea the development and assessment it deserves.
The second edition has been substantially enlarged and reworked, with a new chapter on the relationship between explanation and Bayesianism, and an extension and defence of the account of contrastive explanation. It also includes an expanded defence of the claims that our inferences really are guided by diverse explanatory considerations, and that this pattern of inference can take us towards the truth. This edition of Inference to the Best Explanation has also been updated throughout and includes a new bibliography.
Peter Lipton is Hans Rausing Professor of History and Philosophy of Science at the University of Cambridge.
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