In what follows, I propose to evaluate Giere's analysis by applying it to a causal process considered in evolutionary theory, namely, natural selection. To say that there is selection for a given trait is to say that possessing that trait causes differential reproductive success. If there is selection for a trait and if no other evolutionary forces impinge and there is no "sampling error" due to random drift, individuals with the trait will on average have more offspring than individuals without (...) it. But sampling error can't be discounted for finite populations; so the combined effect of selection and drift will be a probability distribution of numbers of offspring. In this event, selection for a trait will imply that the mean value of the distribution associated with the individuals possessing the trait exceeds the mean value for individuals without the trait. Evolutionary theory assigns a probability distribution of possible reproductive outputs to each organism and, thereby, views organisms as stochastic systems. This implies no commitment to indeterminacy at the microlevel, of course. (shrink)

It is possible for a causal factor to raise the probability of a second factor in some situations while lowering the probability of the second factor in other situations. Must a genuine cause always raise the probability of a genuine effect of it? When it does not always do so, an "interaction" with some third factor may be the reason. I discuss causal interaction from the perspectives of Giere's counterfactual characterization of probabilistic causal connection (1979, 1980) and the "contextual unanimity" (...) model developed by, among others, Cartwright (1979) and Skyrms (1980). I argue that the contextual unanimity theory must exercise care, in a new way that seems to have gone unnoticed, in order to adequately accommodate the phenomenon, and that the counterfactual theory must be substantially revised; although it will still, pending clarification of a second kind of revision, be unable to accommodate a kind of interaction exemplified in cases like those described by Sober (1982). (shrink)

UNDERSTANDING SCIENTIFIC REASONING develops critical reasoning skills and guides students in the improvement of their scientific and technological literacy. The authors teach students how to understand and critically evaluate the scientific information they encounter in both textbooks and the popular media. With its focus on scientific pedagogy, UNDERSTANDING SCIENTIFIC REASONING helps students learn how to examine scientific reports with a reasonable degree of sophistication. The book also explains how to reason through case studies using the same informal logic skills employed (...) by scientists and to analyze a complex series of propositions and hypotheses using sound scientific reasoning. (shrink)

Existing definitions of relevance relations are essentially ambiguous outside the binary case. Hence definitions of probabilistic causality based on relevance relations, as well as probability values based on maximal specificity conditions and homogeneous reference classes are also not uniquely specified. A 'neutral state' account of explanations is provided to avoid the problem, based on an earlier account of aleatory explanations by the author. Further reasons in support of this model are given, focusing on the dynamics of explanation. It is shown (...) that truth in explanation need not entail maximal specificity and that probabilistic explanations should not contain a specification of probability values. (shrink)