Statistical analysis is often used to evaluate the strength of evidence for or against scientific hypotheses. Here we consider evidence measurement from the point of view of representational measurement theory, focusing in particular on the 0-points of measurement scales. We argue that a properly calibrated evidence measure will need to count up from absolute 0, in a sense to be defined, and that this 0-point is likely to be something other than what one might have expected. This suggests the need for a new theory of statistical evidence in the context of which calibrated evidence measurement becomes tractable.

Absolute and relative outcome measures measure a treatment’s effect size, purporting to inform treatment choices. I argue that absolute measures are at least as good as, if not better than, relative ones for informing rational decisions across choice scenarios. Specifically, this dominance of absolute measures holds for choices between a treatment and a control group treatment from a trial and for ones between treatments tested in different trials. This distinction has hitherto been neglected, just like the role of absolute and baseline risks in rational decision making that my analysis reveals. Recognizing both aspects advances the discussion on reporting outcome measures.

This article draws on Kukla’s “Institutional Definition of Health” to provide a definition of “psychiatric condition” that delineates the proper bounds of psychiatry. I argue that this definition must include requirements that psychiatrization of a condition benefit the well-being of (1) the society as a collective and (2) the individual whose condition is in question. I then suggest that psychiatry understand individual well-being in terms of the subjective values of individuals. Finally, I propose that psychiatry’s understanding of collective well-being should be the result of a “socially objective” process and give certain desiderata for this understanding.

Inferences from clinical research results to estimates of therapeutic effectiveness suffer due to various biases. I argue that predictions of medical effectiveness are prone to failure because current medical research overlooks the impacts of a particularly detrimental set of biases: meta-biases. Meta-biases are linked to higher-level characteristics of medical research and their effects are only observed when comparing sets of studies that share certain meta-level properties. I offer a model for correcting research results based on meta-research evidence, the bias dynamics model, which employs regularly updated empirical bias coefficients to attenuate estimates of therapeutic effectiveness.

Cancer biologists ascribe normal functions to parts of cancer. Normal functions are activities that parts of systems are in some minimal sense supposed to perform. Cancer biologists’ finding normality within the abnormality of cancer poses difficulties for two main approaches to normal function. One approach claims that normal functions are activities for which parts are selected. However, some parts of cancers that have normal functions aren’t selected to perform them. The other approach claims that normal functions are part-activities that (typically) contribute to the survival or reproduction of the relevant system. However, cancers are too heterogeneous to establish what (typically) contributes to their progression across a type.

The concept of scaffold is widespread in science and increasingly common in evolutionary biology. While this concept figures in causal explanations, it is not clear what scaffolds are and what role they play in those explanations. Here we present evolutionary scaffolding explanation as a distinct type of explanatory strategy, distinguishing it from other types of evolutionary explanation. By doing so, we clarify the meaning of “scaffold” as a causal concept and its potential contribution to accounts of evolutionary novelty and major transitions.

Adaptationism is often taken to be the thesis that most traits are adaptations. To assess this thesis, it seems we must be able to establish either an exhaustive set of all traits or a representative sample of this set. Either task requires a more systematic and principled way of individuating traits than is currently available. Moreover, different trait individuation criteria can make adaptationism turn out true or false. For instance, individuation based on natural selection may render adaptationism true, but may do so by presupposing adaptationism. In this article, we show how adaptationism depends on trait individuation and that the latter is an open and unsolved problem.

Research in evolutionary ecology on random foraging seems to ignore the possibility that some random foraging is an adaptation not to environmental randomness, but to what Wimsatt called “perceived randomness.” This occurs when environmental features are unpredictable, whether physically random or not. Mere perceived randomness may occur, for example, due to effects of climate change or certain kinds of static landscape variation. I argue that an important mathematical model concerning random foraging doesn’t depend on environmental randomness, despite contrary remarks by researchers. I use computer simulations to illustrate the idea that random foraging is an adaptation to mere perceived randomness.

Measurement of subjective animal welfare creates a special problem in validating the measurement indicators used. Validation is required to ensure indicators are measuring the intended target state, and not some other object. While indicators can usually be validated by looking for correlation between target and indicator under controlled manipulations, this is not possible when the target state is not directly accessible. In this article, I outline a four-step approach using the concept of robustness that can help with validating indicators of subjective animal welfare.

Immunology is a notoriously complex field with distinct concepts and terminology. Yet immunologists regularly and effectively collaborate with other researchers, notably clinicians and experts in population health. How does such “collaboration without convergence” work? This paper offers an answer. Immunology exhibits three features that support collaboration in the absence of major consensus on theories, methods, or concepts. These are a multifaceted target of inquiry, therapeutic aspirations, and a clear interdisciplinary pathway. Building on these features, I sketch a general account of “low-effort interdisciplinarity” and connect this result to recent work on population health.

We examine arguments regarding the use of mechanistic evidence in assessing treatment efficacy and find that advocates and critics of EBM+ have largely been talking past each other due to a difference in focus. We explore aducanumab for the treatment of Alzheimer’s disease as a case that may speak to the role of EBM+ in pharmaceutical regulation. The case suggests the debate may be more fruitful if philosophers confine debates to particular domains of medicine and weigh in prospectively instead of relying on historical cases in which outcomes are known and that are susceptible to hindsight bias.

This article shows how institutional values influence the design and evaluation of arguments in funding proposals for research. We characterize a general argument made within proposals and several kinds of subarguments that contribute to it. We indicate that funders’ values inform the kinds of proposal documents funders require and their relative weighting of them. We illustrate these points by showing how a program office in the US National Oceanic and Atmospheric Administration (NOAA) uses its public service mission to require and heavily weigh arguments to transition new knowledge to NOAA service providers, and we suggest conceptual questions raised by the use of transition knowledge.