In this article I issue a challenge to what I call the Independence Thesis of Theory Assessment (ITTA). According to ITTA, the evidence for (or against) a theory must be assessed independently from the theory explaining the evidence. I argue that ITTA is undermined by cases of evidential uncertainty, in which scientists have been guided by the explanatory power of their theories in the assessment of the evidence. Instead, I argue, these cases speak in favor of a model of theory assessment in which explanatory power may indeed contribute to the stabilization of the evidential basis.

This article distinguishes causal modeling, metaphysical, epistemic, and modeling reasons for variable/parameter choice. I argue in favor of justifying variable/parameter choices by appealing to modeling reasons concerning the limitations of the available measurements, experimental data, modeling techniques, and modeling frameworks. I use this “tyranny of availability” to identify normative criteria for variable/parameter choice that apply across most scientific modeling contexts and investigate their metaphysical and epistemological implications.

The Black–Scholes (B-S) model is considered by the academic environment one of the greatest achievements of financial economics. Yet it brings with it some conundrums. The model is often used in a manner that contradicts one of its assumptions, and its predictions are not supported by market reality. Here we address, from the perspective of philosophy of science, an additional issue related to this model: the distinction between its explanatory and predictive capabilities.

This paper evaluates the back-and-forth between Mayo, Howson, and Achinstein over whether classical statistics commits the base-rate fallacy. I show that Mayo is correct to claim that Howson’s arguments rely on a misunderstanding of classical theory. I then argue that Achinstein’s refined version of the argument turns on largely undefended epistemic assumptions about “what we care about” when evaluating hypotheses. I end by suggesting that Mayo’s positive arguments are no more decisive than her opponents’: even if correct, they are unlikely to compel anyone not already sympathetic to the classical picture.

The article reevaluates the distinction between experiment and observation. It is first argued that to get clear on what role observation plays in the generation of scientific knowledge, we need to distinguish “experiential observation” as a concept closely connected to experience from “observation” in a technical sense and from “field observation” as a concept that reasonably contrasts with “experiment.” It is then argued that observation construed as field observation can enjoy systematic epistemic advantages over experiment, contrary to appearances.

Stegenga (forthcoming) formulates and defends a novel account of scientific progress, according to which science makes progress just in case there is a change in scientific justification. Here, we present several problems for Stegenga’s account, concerning, respectively, (i) obtaining misleading evidence, (ii) losses or destruction of evidence, (iii) oscillations in scientific justification, and (iv) the possibility of scientific regress. We conclude by sketching a substantially different justification-based account of scientific progress that avoids these problems.