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Abstract 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. 

Abstract I extend the literature on norms of assertion to the ubiquitous use of graphs in scientific papers and presentations, which I term “graphical testimony.” On my account, the testimonial presentation of a graph involves commitment to both (a) the in‐context reliability of the graph's framing devices and (b) the perspective‐relative accuracy of the graph's content. Despite apparent disagreements between my account and traditional accounts of assertion, the two are compatible and I argue that we should expect a similar pattern of commitments in a set of cases that extends beyond the graphical one. I end by demonstrating that the account resolves apparent tensions between the demands of honesty and the common scientific practice of presenting idealized or simplified graphs: these “distortions” can be honest so long as there's the right kind of alignment between the distortion and the background beliefs and values of the audience. 

What probability distribution should we use when calculating the expected utility of different climate policies? There’s a substantial literature on this question in economics, where it is largely treated as empirical or technical—that is, the question is what distri- bution is justified by the empirical evidence and/or economic theory. The question has a largely overlooked methodological component, however—a component that concerns how (climate) economics should be carried out, rather than what the science tells us. Indeed, the major dispute in the literature is over precisely this aspect of the question: figures like Nordhaus and Weitzman disagree less about the evidence or the theory than they do about which possibilities we should consider when making political decisions—or offering economic advice—about climate change. There are two important implications. First, at least some of the economic literature misfires in attempting to treat the debate as open to empirical or technical resolution; a better path to progress on the question involves further investigating the policy recommendations that can be derived from the two po- sitions. Second, the choice of discount rate is entangled with the choice of probability distribution: as both choices are responsive to the same normative reasons, we cannot evaluate the arguments in favour of a particular discount rate without considering the implications of those same arguments for the choice of distribution. 

The stopping rule for a sequential experiment is the rule or procedure for determining when that experiment should end. Accordingly, the stopping rule principle (SRP) states that the evidential relationship between the final data from a sequential experiment and a hypothesis under consideration does not depend on the stopping rule: the same data should yield the same evidence, regardless of which stopping rule was used. I clarify and provide a novel defense of two interpretations of the main argument against the SRP, the foregone conclusion argument. According to the first, the SRP allows for highly confirmationally unreliable experiments, which concept I make precise, to confirm highly. According to the second, it entails the evidential equivalence of experiments differing significantly in their confirmational reliability. I rebut several attempts to deflate or deflect the foregone conclusion argument, drawing connections with replication in science and the likelihood principle.