New PDF release: Evidence, Explanation, and Realism: Essays in Philosophy of

By Peter Achinstein

ISBN-10: 0199735255

ISBN-13: 9780199735259

ISBN-10: 0199755736

ISBN-13: 9780199755738

The essays during this quantity tackle 3 primary questions within the philosophy of technological know-how: what's required for a few truth to be proof for a systematic speculation? What does it suggest to claim scientist or a thought explains a phenomenon? should still medical theories that postulate "unobservable" entities akin to electrons be construed realistically as aiming to properly describe an international underlying what's at once observable, or may still such theories be understood as aiming to properly describe in basic terms the observable global? exclusive thinker of technological know-how Peter Achinstein presents solutions to every of those questions in essays written over a interval of greater than forty years. the current quantity brings jointly his very important formerly released essays, permitting the reader to confront one of the most easy and hard matters within the philosophy of technology, and to contemplate Achinstein's many influential contributions to the answer of those concerns. He offers a conception of facts that relates this idea to chance and clarification; a idea of clarification that relates this idea to an explaining act in addition to to the several ways that motives are to be evaluated; and an empirical safeguard of medical realism that invokes either the idea that of facts and that of rationalization.

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But they provide little if any clarification of the concept of explanation they invoke. Their view does seem to require a concept of explanation that is objective, noncontextual, and not to be understood in terms of a nonexplanatory concept of evidence. In other writings I have proposed an account of explanation that can be used for present purposes. 21 I will then show that it generates a concept of explanation of a type needed for evidence, and I 19. ) 20. Hempel’s alternative model for nondeductive cases, the inductive statistical (I-S) model, will not suit our purposes, since it does not provide a notion of correct explanation.

Not necessarily. It may be evidence for neither. In our coin-tossing example, if we consider the two experiments to have equal probative value and we combine their outcomes in the simplest manner, the result would be 200 tosses with this coin, yielding a total of 100 heads and 100 tails. This combined information by itself would not be evidence that the coin is heads-biased and evidence that it is tails-biased. The combined results in this case do not provide a good reason for believing either or both of the bias theories.

The “high probability” definition, although it satisfies the desire for probability, generates the Wheaties case in which, as we have just seen, the “evidence” provides no reason for believing the hypothesis. 17 17. My thanks are due to Stephen Barker, Robert Cummins, and Dale Gottlieb for valuable comments on material in sections 1–7. F. 24 Evidence and Induction 8. CORRECT EXPLANATION Since the original paper in Mind was published in 1978, my thoughts on evidence (and its relationship to explanation) have expanded, particularly in the Nature of Explanation and in The Book of Evidence.

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Evidence, Explanation, and Realism: Essays in Philosophy of Science by Peter Achinstein

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