I propose the “Propositional Account” of effective quantum field theories. According to the Propositional Account, each effective quantum field theory expresses propositions about various physical items: fields, interactions, and more. In addition, two effective quantum field theories are physically equivalent just in case they express the same propositions. As I explain, the Propositional Account is scientifically naturalistic, since it invokes terms and principles from the empirical science of linguistics. And the Propositional Account avoids problems faced by other accounts of the physical contents of effective theories.

Racial discrimination is a multidimensional concept. Yet, subjective measures of racial discrimination focus on particular dimensions (interpersonal over institutional, attributable over ambiguously attributable). I argue that there is path dependence in the development and validation of subjective measures, such that existing scales prevent the development of scales that are better for some purpose. Path dependence can occur when researchers: (1) adopt a coherentist view of measurement, namely, in iteratively refining constructs and measures and (2) employ current psychometric validation practices. The main takeaway is that norms are needed to evaluate the initial refinement of the construct rather than taking it for granted.

Dewar (2019) argues that removing excess structure via “sophistication” can have explanatory benefits to removing excess structure via “reduction.” In this paper, I argue that a more robust reason to prefer sophisticated theories is that they have representational benefits.

Well-known debates among statistical inferential paradigms emerge from conflicting views on the notion of probability. One dominant view understands probability as a representation of sampling variability; another prominent view understands probability as a measure of belief. The former generally describes model parameters as fixed values, in contrast to the latter. We propose that there are actually two versions of a parameter within both paradigms: a fixed unknown value that generated the data and a random version to describe the uncertainty in estimating the unknown value. An inferential approach based on CDs deciphers seemingly conflicting perspectives on parameters and probabilities.

When many statistical hypotheses are evaluated simultaneously, statisticians often recommend adjusting (or “correcting”) standard hypothesis tests. In this article, I (1) distinguish two senses of adjustment, (2) investigate the prudential and epistemic goals that adjustment might achieve, and (3) identify conditions under which a researcher should not adjust for multiplicity in the two senses I identify. I tentatively conclude that the goals of scientists and the public may be misaligned with the decision criteria used to evaluate multiple-testing regimes.