The question of the relationship of the making of value judgments in a typically ethical sense to the methods and procedures of science has been discussed in the literature at least to that point which e. e. cummings somewhere refers to as “The Mystical Moment of Dullness.” Nevertheless, albeit with some trepidation, I feel that something more may fruitfully be said on the subject.

Several authors have recently contended that modern statistical theory provides a powerful argument in favor of the view that if scientists accept or reject hypotheses at all they do so only in a behavioral sense--i.e., in a sense which reduces "accepting P" to "acting on the basis of P relative to an objective O". In this paper, the argument from statistics in favor of a behavioral view is outlined; an interpretation of two statistical procedures (Bayes method and signifigance testing) is (...) offered which does not entail a behavioral analysis of "accepting a hypothesis"; and the conclusion that non-behavioral analyses of belief are compatible with the application of current statistical theory in the sciences is tentatively advanced. (shrink)

Although epistemic values have become widely accepted as part of scientific reasoning, non-epistemic values have been largely relegated to the "external" parts of science (the selection of hypotheses, restrictions on methodologies, and the use of scientific technologies). I argue that because of inductive risk, or the risk of error, non-epistemic values are required in science wherever non-epistemic consequences of error should be considered. I use examples from dioxin studies to illustrate how non-epistemic consequences of error can and should be considered (...) in the internal stages of science: choice of methodology, characterization of data, and interpretation of results. (shrink)

Claims that science should be more democratic than it is frequently arouse opposition. In this essay, I distinguish my own views about the democratization of science from the more ambitious theses defended by Paul Feyerabend. I argue that it is unlikely that the complexity of some scientific debates will allow for resolution according to the methodological principles of any formal confirmation theory, suggesting instead that major revolutions rest on conflicts of values. Yet these conflicts should not be dismissed as irresoluble.

When interests and preferences of researchers or their sponsors cause bias in experimental design, data interpretation or dissemination of research results, we normally think of it as an epistemic shortcoming. But as a result of the debate on science and values, the idea that all extra-scientific influences on research could be singled out and separated from pure science is now widely believed to be an illusion. I argue that nonetheless, there are cases in which research is rightfully regarded as epistemologically (...) deficient due to the influence of preferences on its outcomes. I present examples from biomedical research and offer an analysis in terms of social epistemology. (shrink)

Underdetermination arguments support the conclusion that no amount of empirical data can uniquely determine theory choice. The full content of a theory outreaches those elements of it (the observational elements) that can be shown to be true (or in agreement with actual observations).2 A number of strategies have been developed to minimize the threat such arguments pose to our aspirations to scientific knowledge. I want to focus on one such strategy: the invocation of additional criteria drawn from a pool of (...) cognitive or theoretical values, such as simplicity or generality, to bolster judgements about the worth of models, theories, and hypotheses. What is the status of such criteria? Larry Laudan, in Science and Values, argued that cognitive values could not be treated as self-validating, beyond justification, but are embedded in a three-way reticulational system containing theories, methods, and aims or values, which are involved in mutually supportive relationships (Laudan, 1984). My interest in this paper is not the purportedly self-validating nature of cognitive values, but their cognitive nature. Although Laudan rejects the idea that what he calls cognitive values are exempt from rational criticism and disagreement, he does seem to think that the reticulational system he identifies is independent of non-cognitive considerations. It is this cognitive/non-cognitive distinction that I wish to query in this paper. Let me begin by summarizing those of my own views about inquiry in which this worry about the distinction arises. (shrink)

Douglas proposes a new ideal in which values serve an essential function throughout scientific inquiry, but where the role values play is constrained at key points, protecting the integrity and objectivity of science.

This is an important book precisely because there is none other quite like it.

Claims that science should be more democratic than it is frequently arouse opposition. In this essay, I distinguish my own views about the democratization of science from the more ambitious theses defended by Paul Feyerabend. I argue that it is unlikely that the complexity of some scientific debates will allow for resolution according to the methodological principles of any formal confirmation theory, suggesting instead that major revolutions rest on conflicts of values. Yet these conflicts should not be dismissed as irresoluble.

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)

The discovery of the Higgs particle required a signal of 5σ significance. The rigid application of that condition is a convention that disregards more specific aspects of the given experiment. In particular, it does not account for the characteristics of the look elsewhere effect in the individual experimental context. The paper relates this aspect of data analysis to the question as to what extent theoretical reasoning should be admitted to play a role in the assessment of the significance of empirical (...) data. (shrink)

Philosophy of Science: A Unified Approach combines a general introduction to philosophy of science with an integrated survey of all its important subfields. As the book’s subtitle suggests, this excellent overview is guided methodologically by "a unified approach" to philosophy of science: behind the diversity of scientific fields one can recognize a methodological unity of the sciences. This unity is worked out in this book, revealing all the while important differences between subject areas. Structurally, this comprehensive book offers a two-part (...) approach, which makes it an excellent introduction for students new to the field and a useful resource for more advanced students. Each chapter is divided into two sections. The first section assumes no foreknowledge of the subject introduced, and the second section builds upon the first by bringing into the conversation more advanced, complementary topics. Definitions, key propositions, examples and figures overview all of the core material. At the end of every chapter there are selected readings and exercises . The book also includes a comprehensive bibliography and an index. (shrink)

1. Deductive and Inductive Inference. Within the traditional treatments of scientific method, e.g., in and, it was customary to divide scientific inference into two parts: deductive and inductive. Deductive inference was taken to mean the activity of deducing theorems from postulates and definitions, whereas inductive inference represented the activity of constructing a general statement from a set of particular “facts.” Deductive inference was relegated to the mathematical sciences, and inductive inference to the empirical sciences. As a consequence, the whole of (...) science was split into two quite distinct parts: deductive science was conceived to start with precise axioms, postulates, and definitions, and its method consisted in drawing inferences from these “givens” in such a way that if the original assumptions are taken as true, then the theorems must be taken as true also. The obligation behind this “must” of the mathematical sciences was an obligation based on strict rules of deductive inference; in the earlier analyses, it was supposed that these rules were all aspects of the general theory of the syllogism, as it was described in an almost complete form in but in later work the rules have been formalized in more general terms. Inductive inference, on the other hand, was supposed to start with observed facts; these facts, or “data,” were presumably given by the senses, and described particular aspects of the external world. If the facts were given in a certain way, then the empirical scientist could “infer” a generalized description; this generalization had the characteristic that if it were granted as true, then all the facts could be deduced as consequences. The inductive process admittedly does not lead to unique generalizations, and the philosopher of science searched for other defining characteristics of good inference, such as “simplicity” and “convenience.” These empirical generalizations were taken to be “descriptions of the phenomenal world of the scientist”; a further inductive step might be taken by constructing a set of very general principles out of the more specialized generalizations within specific research problems. These “higher” inductive inferences were regarded as “theories,” which could be studied by themselves within the method of deductive inference. The scientist was then described as “checking” theory by determining whether or not the predicted consequences deduced from theory actually hold in the phenomenal world. (shrink)

Amidst long-running debates within the field, high energy physics has adopted a statistical methodology that primarily employs standard frequentist techniques such as significance testing and confidence interval estimation, but incorporates Bayesian methods for limited purposes. The discovery of the Higgs boson has drawn increased attention to the statistical methods employed within HEP. Here I argue that the warrant for the practice in HEP of relying primarily on frequentist methods can best be understood as pragmatic, in the sense that statistical methods (...) are chosen for their suitability for the practical demands of experimental HEP, rather than reflecting a commitment to a particular epistemic framework. In particular, I argue that understanding the statistical methodology of HEP through the perspective of pragmatism clarifies the role of and rationale for significance testing in the search for new phenomena such as the Higgs boson. (shrink)

Two views of the role of truth as an aim of inquiry are contrasted: The Peirce-Popper or messianic view of approach to the truth as an ultimate aim of inquiry and the myopic view according to which a concern to avoid error is a proximate aim common to many otherwise diverse inquiries. The messianic conception is held to be responsible for the tendency to conflate fallibilism with corrigibilism and for the consequent problems faced by Peirceans and Popperians alike in squaring (...) the alleged relevance of the fruits of scientific inquiry for practice while insisting on the corrigibility of knowledge. Myopic realism is advocated as promising escape from these difficulties. (shrink)