Among the various meanings in which the word ‘probability’ is used in everyday language, in the discussion of scientists, and in the theories of probability, there are especially two which must be clearly distinguished. We shall use for them the terms ‘probability1’ and ‘probability2'. Probability1 is a logical concept, a certain logical relation between two sentences ; it is the same as the concept of degree of confirmation. I shall write briefly “c” for “degree of confirmation,” and “c” for “the (...) degree of confirmation of the hypothesis h on the evidence e“; the evidence is usually a report on the results of our observations. On the other hand, probability2 is an empirical concept; it is the relative frequency in the long run of one property with respect to another. The controversy between the so-called logical conception of probability, as represented e.g. by Keynes, and Jeffreys, and others, and the frequency conception, maintained e.g. by v. Mises and Reichenbach, seems to me futile. These two theories deal with two different probability concepts which are both of great importance for science. Therefore, the theories are not incompatible, but rather supplement each other. (shrink)

Writing on the justification of certain inductive inferences, the author proposes that sometimes induction is justified and that arguments to prove otherwise are not cogent. In the first part he defends the argument of D.C. Williams' The Ground of Induction that induction is justified as a matter of logic by the proportional syllogism: "The vast majority of large samples match the population, therefore (probably) this sample matches the population"). In the second part he deals with such topics as deductive logic (...) (arguing that deductive logic is not formal), the theory of logical probability, and probability and truth. (shrink)

At least one of these conceptions of probability underlies any theory of statistical inference (or, to use Neyman's phrase, 'inductive behavior'). ...

The logical interpretation of probability, or "objective Bayesianism'' – the theory that (some) probabilities are strictly logical degrees of partial implication – is defended. The main argument against it is that it requires the assignment of prior probabilities, and that any attempt to determine them by symmetry via a "principle of insufficient reason" inevitably leads to paradox. Three replies are advanced: that priors are imprecise or of little weight, so that disagreement about them does not matter, within limits; that it (...) is possible to distinguish reasonable from unreasonable priors on logical grounds; and that in real cases disagreement about priors can usually be explained by differences in the background information. It is argued also that proponents of alternative conceptions of probability, such as frequentists, Bayesians and Popperians, are unable to avoid committing themselves to the basic principles of logical probability. (shrink)

In clinical and agricultural trials, there is the danger that an experimental outcome appears to arise from the causal process or treatment one is interested in when, in reality, it was produced by some extraneous variation in the experimental conditions. The remedy prescribed by classical statisticians involves the procedure of randomization, whose effectiveness and appropriateness is criticized. An alternative, Bayesian analysis of experimental design, is shown, on the other hand, to provide a coherent and intuitively satisfactory solution to the problem.

This book is a balanced and up-to-date introduction to the philosophy of science. It covers all the main topics in the area, as well as introducing the student to the moral and social reality of science. The author's style is free from jargon, and although he makes use of scientific examples, these should be intelligible to those without much scientific background. At the same time the questions he raises are not merely abstract, so the book will be of interest and (...) concern to scientists as well as philosophers.The author discusses the growth of knowledge of science, the status of scientific theories and their relationship to observational data, the extent to which scientific theories rest on unprovable paradigms, and the nature of scientific explanations. In later chapters he considers probability, scientific reductionism, the relationship between science and technology, and the relationship between scientific and other values. (shrink)

It would be difficult to overestimate the influence Hume’s problem of induction exercises on contemporary epistemology. At the same time, the problem of induction has not perceptibly slowed the progress of mathematics and science. This ironic state of affairs, immortalized by C. D. Broad’s description of induction as “the glory of science” and “the scandal of philosophy,” ought in all fairness to give both sides some pause. And on occasion, it does: the mathematicians stop to concede that Hume has not (...) yet been answered, the scientists worry about randomization of experiments, and inductive skeptics stir uneasily in their chairs at the mention of certain mathematical theorems that seem palpably to have bearing on the problem. (shrink)

This paper begins with a rigorous critique of David Stove''s recent bookThe Rationality of Induction. In it, Stove produced four different proofs to refute Hume''s sceptical thesis about induction. I show that Stove''s attempts to vindicate induction are unsuccessful. Three of his proofs refute theses that are not the sceptical thesis about induction at all. Stove''s fourth proof, which uses the sampling principle to justify one particular inductive inference, makes crucial use of an unstated assumption regarding randomness. Once this assumption (...) is made explicit, Hume''s thesis once more survives.The refutation of Stove''s fourth proof leads to some observations which relate Goodman''s grue paradox with randomness of a sample. I formulate a generalized version of Goodman''s grue paradox, and argue that whenever a sample, no matter how large, is drawn from a predetermined smaller interval of a population that is distributed over a larger interval, any conclusion drawn about the characteristics of the population based on the observed characteristics of the sample is fatally vulnerable to the generalized grue paradox. (shrink)

This balanced and up-to-date introduction to the philosophy of science covers all the main topics in the area, and initiates the student into the moral and social reality of science. O'Hear discusses the growth of knowledge of science, the status of scientific theories and their relationship to observational data, the extent to which scientific theories rest on unprovable paradigms, and the nature of scientific explanations. In later chapters he considers probability, scientific reductionism, the relationship between science and technology, and the (...) relationship between scientific and other values. (shrink)