A number of philosophers have thought that fair lotteries over countably infinite sets of outcomes are conceptually incoherent by virtue of violating countable additivity. In this article, I show that a qualitative analogue of this argument generalizes to an argument against the conceptual coherence of a much wider class of fair infinite lotteries—including continuous uniform distributions. I argue that this result suggests that fair lotteries over countably infinite sets of outcomes are no more conceptually problematic than continuous uniform distributions. Along the way, I provide a novel argument for a weak qualitative, epistemic version of regularity.

Simple random-sampling resolutions of the raven paradox relevantly diverge from scientific practice. We develop a stratified random-sampling model, yielding a better fit and apparently rehabilitating simple random sampling as a legitimate idealization. However, neither accommodates a second concern, the objection from potential bias. We develop a third model that crucially invokes causal considerations, yielding a novel resolution that handles both concerns. This approach resembles Inference to the Best Explanation (IBE) and relates the generalization’s confirmation to confirmation of an associated law. We give it an objective Bayesian formalization and discuss the compatibility of Bayesianism and IBE.

It is a plausible speculation that conventional choices in outcome measures might influence the results of meta-analyses. We test that speculation by simulating data from trials on antidepressants. We vary real drug effectiveness while modulating conventional values for outcome measures. We had previously shown that one conventional choice used in meta-analyses of antidepressants falls in a narrow range of values that maximize estimates of effectiveness. Our present analysis investigates why this phenomenon occurs. Moreover, our results suggest the superiority of absolute outcome measures over relative measures. This research program can be extended to test numerous other aspects of clinical research.

This paper discusses Millstein’s (2020) criticism of the consensus view formed against selected-effects ecological functions. I argue that Millstein’s defense of coevolution-based selected-effects ecological functions applies to a notion of function as an activity, whereas proponents of the consensus view are concerned with a notion of ecological function as the contribution of an organism, population, species, or abiotic item to the maintenance of its community and/or the functioning of its ecosystem. Millstein’s arguments hence do not invalidate the consensus view but draw attention to a notion of function that has been neglected in philosophical discussions and that deserves more attention.

Standard proposals of scientific anti-realism assume that the methodology of a scientific research program can be endorsed without accepting its metaphysical commitments. I argue that the distinction between competence, the rules governing one’s language faculty, and performance, or linguistic behavior, precludes this. Linguistic theories aim to describe competence, not performance, and so must be able to distinguish observations reflective of the former from those reflective of the latter. This classification of data makes sense only against the background of a psychologically realistic view of linguistic theory. So the very methodology of the science commits one to its realistic interpretation.

For model-based frequentist statistics, based on a parametric statistical model ${{\cal M}_\theta }({\bf{x}})$ , the trustworthiness of the ensuing evidence depends crucially on (i) the validity of the probabilistic assumptions comprising ${{\cal M}_\theta }({\bf{x}})$ , (ii) the optimality of the inference procedures employed, and (iii) the adequateness of the sample size (n) to learn from data by securing (i)–(ii). It is argued that the criticism of the postdata severity evaluation of testing results based on a small n by Rochefort-Maranda (2020) is meritless because it conflates [a] misuses of testing with [b] genuine foundational problems. Interrogating this criticism reveals several misconceptions about trustworthy evidence and estimation-based effect sizes, which are uncritically embraced by the replication crisis literature.

Formal and social epistemologists have devoted significant attention to the question of how to aggregate the credences of a group of agents who disagree about the probabilities of events. Moss (2011) and Pettigrew (2019) argue that group credences can be a linear mean of the credences of each individual in the group. By contrast, I argue that if the epistemic value of a credence function is determined solely by its accuracy, then we should, where possible, aggregate the underlying statistical models that individuals use to generate their credence functions, using “stacking” techniques from statistics and machine learning first developed by Wolpert (1992).

The role or function of experimental and observational replication within empirical science has implications for how replication should be measured. Broadly, there seems to be consensus that replication’s central goal is to confirm or vouchsafe the reliability of scientific findings. I argue that if this consensus is correct, then most of the measures of replication used in the scientific literature are actually poor indicators of this reliability or confirmation. Only meta-analytic measures of replication align functionally with the goals of replication. I conclude by addressing some objections to meta-analysis.

I argue that Carl Hempel’s pioneering work on scientific explanation introduced an assumption that Hempel never motivated, namely, that explanation is an aim of science. Ever since, it has remained largely unquestioned in analytic philosophy of science. By expanding the historical scope of the debate on explanation to philosophers from the first half of the twentieth century, I show that the debate should include a critical reflection on Hempel’s assumption. This reflection includes two problems: how to motivate one’s position on the aims of scientific knowledge and how to decide which examples count as expressions of those aims.

Jonathan Livengood and Justin Sytsma have published a series of studies on “Actual Causation and Compositionality,” in which they investigate causal attributions of laypeople. We use one of their vignettes to follow up on their research. Our findings cast doubt on their conclusion that ordinary causal attributions tend to violate the compositionality constraint if one looks at cases in which someone is responsible for an effect by way of an intermediary that does not share in the responsibility.

Bayesians often appeal to “merging of opinions” to rebut charges of excessive subjectivity. But what happens in the short run is often of greater interest than what happens in the limit. Seidenfeld and coauthors use this observation as motivation for investigating the counterintuitive short run phenomenon of dilation, since, they allege, dilation is “the opposite” of asymptotic merging of opinions. The measure of uncertainty relevant for dilation, however, is not the one relevant for merging of opinions. We explicitly investigate the short run behavior of the metric relevant for merging, and show that dilation is independent of the opposite of merging.

Numerous philosophers of science have argued that the incentive structure of science is a major contributor to the replication crisis. In this article I review FiveThirtyEight’s election forecasting model and show that it confronts similar problems in political polling and successfully mitigates them, not by changing the incentives, but by weighting a pollster’s effect on the model by their past reliability. I argue that a similar weighting procedure holds a promising approach to addressing the replication crisis in science.

In a recent paper, Pettigrew (2022) reports a generalization of the celebrated accuracy-dominance theorem due to Predd et al. (2009), but Pettigrew’s proof is incorrect. I will explain the mistakes and provide a correct proof.

Philosophical work on meta-analysis focuses on biomedical research and revolves around the question: Is meta-analytic evidence the best kind of evidence? By focusing on conservation science rather than biomedical science, I identify further questions and puzzles for meta-analysis and show their importance for the epistemology of meta-analysis.