In this book, Peter Achinstein proposes and defends several objective concepts of evidence. He then explores the question of whether a scientific method, such as that represented in the four "Rules for the Study of Natural Philosophy" that Isaac Newton invoked in proving his law of gravity, can be employed in demonstrating how the proposed definitions of evidence are to be applied to real scientific cases.

This paper studies a distinction that was proposed in previous works between total and partial adjectives. In pairs of adjectives such as safe–dangerous, clean–dirty and healthy–sick, the first (“total”) adjective describes lack of danger, dirt, malady, etc., while the second (“partial”) adjective describes the existence of such properties. It is shown that the semantics of adjective phrases with modifiers such as almost, slightly, and completely is sensitive to whether the adjective is total or partial. The interpretation of such modified constructions (...) is accounted for using a novel scale structure for total and partial adjectives. It is proposed that the standard value of a total adjective is always fixed as the lower bound of the corresponding partial adjective. By contrast, the standard value of partial adjectives can take any point on the partial scale. The effects of this theoretical distinction on the behavior of modified constructions are studied in detail, and their ramifications for the semantic theory of adjectives are discussed. Some other phenomena are surveyed that show evidence for total and partial adjectival constructions with various comparatives and exceptive phrases. (shrink)

APA PsycNET abstract: This is the first volume of a two-volume work on Probability and Induction. Because the writer holds that probability logic is identical with inductive logic, this work is devoted to philosophical problems concerning the nature of probability and inductive reasoning. The author rejects a statistical frequency basis for probability in favor of a logical relation between two statements or propositions. Probability "is the degree of confirmation of a hypothesis (or conclusion) on the basis of some given evidence (...) (or premises)." Furthermore, all principles and theorems of inductive logic are analytic, and the entire system is to be constructed by means of symbolic logic and semantic methods. This means that the author confines himself to the formalistic procedures of word and symbol systems. The resulting sentence or language structures are presumed to separate off logic from all subjectivist or psychological elements. Despite the abstractionism, the claim is made that if an inductive probability system of logic can be constructed it will have its practical application in mathematical statistics, and in various sciences. 16-page bibliography. (PsycINFO Database Record (c) 2016 APA, all rights reserved). (shrink)

In these challenging pages, Unger argues for the extreme skeptical view that, not only can nothing ever be known, but no one can ever have any reason at all for anything. A consequence of this is that we cannot ever have any emotions about anything: no one can ever be happy or sad about anything. Finally, in this reduction to absurdity of virtually all our supposed thought, he argues that no one can ever believe, or even say, that anything is (...) the case. (shrink)

What is required for something to be evidence for a hypothesis? In this fascinating, elegantly written work, distinguished philosopher of science Peter Achinstein explores this question, rejecting typical philosophical and statistical theories of evidence. He claims these theories are much too weak to give scientists what they want--a good reason to believe--and, in some cases, they furnish concepts that mistakenly make all evidential claims a priori. Achinstein introduces four concepts of evidence, defines three of them by reference to "potential" evidence, (...) and characterizes the latter using a novel epistemic interpretation of probability. The resulting theory is then applied to philosophical and historical issues. Solutions are provided to the "grue," "ravens," "lottery," and "old-evidence" paradoxes, and to a series of questions. These include whether explanations or predictions furnish more evidential weight, whether individual hypotheses or entire theoretical systems can receive evidential support, what counts as a scientific discovery, and what sort of evidence is required for it. The historical questions include whether Jean Perrin had non-circular evidence for the existence of molecules, what type of evidence J. J. Thomson offered for the existence of the electron, and whether, as is usually supposed, he really discovered the electron. Achinstein proposes answers in terms of the concepts of evidence introduced. As the premier book in the fabulous new series Oxford Studies in Philosophy of Science, this volume is essential for philosophers of science and historians of science, as well as for statisticians, scientists with philosophical interests, and anyone curious about scientific reasoning. (shrink)

Scientific reasoning is—and ought to be—conducted in accordance with the axioms of probability. This Bayesian view—so called because of the central role it accords to a theorem first proved by Thomas Bayes in the late eighteenth ...

This anthology presents work on major topics surrounding the concept of evidence as employed in the empirical sciences. Focusing on the "classificatory" concept of evidence rather than the quantitative "degree of confirmation," the selections include Carl G. Hempel's satisfaction definition, R.B. Braithwaite's hypothetic-deductive view, N.R. Hanson's account of retroduction, Nelson Goodman's entrenchment theory, probability definitions discussed by Rudolf Carnap and Wesley Salmon, Clark Glymour's bootstrap theory, and a view of Achinstein's that combines probability and explanation.

The concept of evidence is central to both epistemology and the philosophy of science. Of course, ‘evidence’ is hardly a philosopher's term of art: it is not only, or even primarily, philosophers who routinely speak of evidence, but also lawyers and judges, historians and scientists, investigative journalists and reporters, as well as the members of numerous other professions and ordinary folk in the course of everyday life. The concept of evidence would thus seem to be on firmer pre-theoretical ground than (...) various other concepts which enjoy similarly central standing within philosophy. (Contrast, for example, the epistemologist's quasi-technical term ‘epistemic justification’.). (shrink)

Two notions of evidence are focused on in this essay, Carnap's positive-relevance notion of evidence (1962, pp. 462 ff.), and Achinstein's notion of potential evidence (1978; and 1983, pp. 322–350). Achinstein creates several interesting examples in his attempt to find faults in Carnap's notion of evidence; his motive, ultimately, is to impel us towards potential evidence. The purpose of this essay is to show that positive relevance is significantly more promising than potential evidence with respect to capturing the scientific sense (...) of the term evidence. This is accomplished by finding faults in the notion of potential evidence, and by defending positive relevance against Achinstein's examples. (shrink)

This paper addresses two examples due to Peter Achinstein purporting to show that the positive relevance view of evidence is too strong, that is, that evidence need not raise the probability of what it is evidence for. The first example can work only if it makes a false assumption. The second example fails because what Achinstein claims is evidence is redundant with information we already have. Without these examples Achinstein is left without motivation for his account of evidence, which uses (...) the concept of explanation in addition to that of probability. (shrink)

Confirmation is commonly identified with positive relevance, E being said to confirm H if and only if E increases the probability of H. Today, analyses of this general kind are usually Bayesian ones that take the relevant probabilities to be subjective. I argue that these subjective Bayesian analyses are irremediably flawed. In their place I propose a relevance analysis that makes confirmation objective and which, I show, avoids the flaws of the subjective analyses. What I am proposing is in some (...) ways a return to Carnap's conception of confirmation, though there are also important differences between my analysis and his. My analysis includes new accounts of what evidence is and of the indexicality of confirmation claims. Finally, I defend my analysis against Achinstein's criticisms of the relevance concept of confirmation. (shrink)

This paper investigates the way that linguistic expressions influence vagueness, focusing on the interpretation of the positive (unmarked) form of gradable adjectives. I begin by developing a semantic analysis of the positive form of ‘relative’ gradable adjectives, expanding on previous proposals by further motivating a semantic basis for vagueness and by precisely identifying and characterizing the division of labor between the compositional and contextual aspects of its interpretation. I then introduce a challenge to the analysis from the class of ‘absolute’ (...) gradable adjectives: adjectives that are demonstrably gradable, but which have positive forms that relate objects to maximal or minimal degrees, and do not give rise to vagueness. I argue that the truth conditional difference between relative and absolute adjectives in the positive form stems from the interaction of lexical semantic properties of gradable adjectives—the structure of the scales they use—and a general constraint on interpretive economy that requires truth conditions to be computed on the basis of conventional meaning to the extent possible, allowing for context dependent truth conditions only as a last resort. (shrink)

There are two reasons, I claim, scientists do and should ignore standard philosophical theories of objective evidence: (1) Such theories propose concepts that are far too weak to give scientists what they want from evidence, viz., a good reason to believe a hypothesis; and (2) They provide concepts that make the evidential relationship a priori, whereas typically establishing an evidential claim requires empirical investigation.

Recently in this journal Sherrilyn Roush (2004) defends positive relevance as a necessary (albeit not a sufficient) condition for evidence by rejecting two of the counterexamples from my earlier (2001) work. In this reply I argue that Roush's critique is not successful.

Frederick Kronz constructs interesting examples in an attempt to show deficiencies in my concept of evidence and the advantages in Carnap's positive relevance idea. His discussion raises general questions of importance in developing an adequate account of scientific evidence questions about the relationship between evidence and belief and the role of emphasis in determining evidence. His examples are challenging, but do they work?

Confirmation theory is intended to codify the evidential bearing of observations on hypotheses, characterizing relations of inductive “support” and “counter­support” in full generality. The central task is to understand what it means to say that datum E confirms or supports a hypothesis H when E does not logically entail H.

According to a standard account of evidence, one piece of information is stronger evidence for an hypothesis than is another iff the probability of the hypothesis on the one is greater than it is on the other. This condition, I argue, is neither necessary nor sufficient because various factors can strengthen the evidence for an hypothesis without increasing (and even decreasing) its probability. Contrary to what probabilists claim, I show that this obtains even if a probability function can take these (...) evidential factors into account in ways they suggest and yield a unique probability value. Nor will the problem be solved by appealing to second-order probabilities. (shrink)