Sewall Wright's is a mathematical test used to characterize differences between biological populations and identify causes of those differences. I discuss Cockerham and Weir's popular, empirically successful approach to statistical estimation of , arguing that their assumption that actual populations are sampled from an infinite set of counterfactual populations (with a common ancestral population) supports a view of natural selection and genetic drift as distinct causes. I also argue that the way in which F-statistics and other statistical tests are applied to computer simulations in empirical research shows that selection and drift correspond to differences in objective, causal probabilities.

The relation among method, concept, and theory in science is complicated. I seek to shed light on that relation by considering an instance of it in systematics: the additional challenges phylogeneticists face when reconstructing phylogeny not at a single level but simultaneously at multiple levels of the hierarchy. How does this complicate the task of phylogenetic inference, and how might it inform and shape the conceptual foundations of phylogenetics? This offers a lens through which the interplay of method, theory, and concepts may be understood in systematics, which, in turn, provides data for a more general account.

Phylogenetic trees are meant to represent the genealogical history of life and apparently derive their justification from the existence of the tree of life and the fact that evolutionary processes are treelike. However, there are a number of problems for these assumptions. Here it is argued that once we understand the important role that phylogenetic trees play as models that contain idealizations, we can accept these criticisms and deny the reality of the tree while justifying the continued use of trees in phylogenetic theory and preserving nearly all of what defenders of trees have called the “importance of tree thinking.”

Characterizing the genetic structure of populations is of importance to evolutionary biology, to human disease gene mapping, and to forensic science. Sewall Wright introduced a set of F-statistics to describe population structure in 1951, and he emphasized that these quantities were ratios of variances. Responding to uncertainty over the best way to estimate F-statistics, Weir and Cockerham published a method-of-moments set of estimators in 1984. This paper continues to be widely cited, with over 7,000 citations to date. Some background to the publishing history of the Weir and Cockerham paper is given here, along with subsequent developments and a discussion of current uses of Wright's F-statistics.

This article unpacks a particular use of ‘cases’ within developmental biology, namely as a means of describing the typical or canonical patterns of phenomena. The article explores how certain cases have come to be established within the field and argues that although they were initially selected for reasons of convenience or ease of experimental manipulation, these cases come to serve as key reference points within the field because of the epistemological structures imposed on them by the scientists using them and, hence, become usable in a wider variety of circumstances including future theory development.

Political science research, particularly in international relations and comparative politics, has increasingly become dominated by statistical and formal approaches. The promise of these approaches shifted the methodological emphasis away from case study research. In response, supporters of case study research argue that case studies provide evidence for causal claims that is not available through statistical and formal research methods, and many have advocated multimethod research. I propose a way of understanding the integration of multiple methodologies in which the causes sought in case studies are treated as singular causation and contingent on a theoretical framework.

Critiques of case studies as an epistemic genre usually focus on the domain of justification and hinge on comparisons with statistics and laboratory experiments. In this domain, case studies can be defended by the notion of “infirming”: they use many different bits of evidence, each of which may independently “infirm” the account. Yet their efficacy may be more powerful in the domain of discovery, in which these same different bits of evidence must be fully integrated to create an explanatory account with internal validity.

The burgeoning science of well-being makes no secret of being value laden: improvement of well-being is its explicit goal. But in order to achieve this goal its concepts and claims need to be value adequate; that is, they need, among other things, to adequately capture well-being. In this article I consider two ways of securing this adequacy—first, by relying on philosophical theory of prudential value and, second, by the psychometric approach. I argue that neither is fully adequate and explore an alternative. This alternative requires thorough changes in the way philosophers theorize about well-being.

Acidity provides an interesting example of an everyday concept that developed fully into a scientific one; it is one of the oldest concepts in chemistry and remains an important one. However, up to now there has been no unity to it. Currently two standard theoretical definitions coexist (Br⊘nsted and Lowry's and Lewis's); the standard laboratory measure of acidity, namely the pH, only corresponds directly to the Br⊘nsted-Lowry concept. The lasting identity of the acidity concept in modern chemistry is based on the persistence of the quotidian concept. This is suggestive for considerations of other scientific concepts.

This essay unpacks a seeming paradox: a concept used to formulate, promote, and legitimate oppressive ideologies—a concept used to formulate mistaken, because they were typological, biological theories about human diversity—is, it seems, the same concept that now promises to deliver wonderful, socially sensitized, innovative results in social and genetic epidemiology. But how could that be? How could scientists expect a concept as problematic as ordinary race to deliver useful scientific results? I propose that there is a process for retranslating Ballungen race concepts in appropriate ways to make them fit and work within social scientific and bioscientific contexts.

In 1894, William Bateson objected to the terms “heredity” and “inheritance” in biology, on grounds of contamination with misleading notions from the everyday world. Yet after the rediscovery of Mendel's work in the spring of 1900, Bateson promoted that work as disclosing the “principles of heredity.” For historians of science, Bateson's change of mind provides a new angle on these terms at a crucial moment in their history. For philosophers of science, the case can serve as a reminder of the potential of Putnam's hypothesis of a division of linguistic labor for analyzing the semantic lives of scientific kind terms.

There is a growing recognition that fictions have a number of legitimate functions in science, even when it comes to scientific explanation. However, the question then arises, what distinguishes an explanatory fiction from a nonexplanatory one? Here I examine two cases—one in which there is a consensus in the scientific community that the fiction is explanatory and another in which the fiction is not explanatory. I shall show how my account of “model explanations” is able to explain this asymmetry, and argue that realism—of a more subtle form—does have a role in distinguishing explanatory from nonexplanatory fictions.

Some philosophers of science—myself included—appeal to fiction as an interpretation of the practice of modeling. This raises the specter of an incompatibility with realism, since fiction making is essentially nontruth regulated. I argue that the prima facie conflict can be resolved in two ways, each involving a distinct notion of fiction and a corresponding formulation of realism. The main goal of the essay is to describe these two packages. I comment also on how to choose between them.

The relation between models and the world is mediated by experimental procedures generating data that are used as evidence to evaluate the model. Data can serve as empirical evidence, for or against, only if they result from reliable experimental procedures. The aim of this article is to discuss the role of relevance judgments in the evaluation of reliability and to clarify the conditions under which reliability can be a strictly empirical matter. It is argued that reliability is a strictly empirical issue only in the restricted case in which the claim under test/investigation is about a data-generating procedure.

Inquiries into the nature of scientific modeling have tended to focus their attention on mathematical models and, relatedly, to think of nonconcrete models as mathematical structures. The arguments of this article are arguments for rethinking both tendencies. Nonmathematical models play an important role in the sciences, and our account of scientific modeling must accommodate that fact. One key to making such accommodations, moreover, is to recognize that one kind of thing we use the term ‘model’ to refer to is a collection of propositions.

A scientific theory offers models for the phenomena in its domain; these models involve theoretical quantities, and a model's structure is the set of relations it imposes on these quantities. A fundamental demand in scientific practice is for those quantities to be clearly and feasibly related to measurement. This demand for empirical grounding can be articulated by displaying the theory-dependent criteria for a procedure to count as a measurement and for identifying the quantity it measures.

Although most philosophical accounts about model/world relations focus on structural mappings such as isomorphism, similarity has long been discussed as an alternative account. Despite its attractions, proponents of the similarity view have not provided detailed accounts of what it means that a model is similar to a real-world target system. This article gives the outlines of such an account, drawing on the work of Amos Tversky.

Robustness concepts are often invoked to manage two obstacles confronting models of ecological systems: complexity and uncertainty. The intuitive idea is that any result derived from many idealized but credible models is thereby made more reliable or is better confirmed. An appropriate basis for this inference has proven elusive. Here, several representations of robustness analysis are vetted, paying particular attention to complex models of ecosystems and the global climate. The claim that robustness is itself confirmatory because robustness analysis employs a Bayesian variety-of-evidence argument is criticized, but recent overwhelming pessimism about robustness may have a silver lining.

The use of power to infer null hypotheses from negative results has recently come under severe attack. In this article, I show that the power of a test can justify accepting the null hypothesis. This argument also gives us a new powerful reason for not treating p-values and power as measures of the strength of evidence.