Philosophers such as Goodman, Scheffler and Glymour aim to answer the Paradox of the Ravens by distinguishing between confirmation simpliciter and selective confirmation. In the latter concept, the evidence both supports a hypothesis and undermines one of its "rivals". In this article, I argue that while selective confirmation does seem to be an important scientific notion, no attempt to formalise it thus far has managed to solve the Paradox of the Ravens.

Reverse inference is the most commonly used inferential strategy for bringing images of brain activation to bear on psychological hypotheses, but its inductive validity has recently been questioned. In this article, I show that, when it is analyzed in likelihoodist terms, reverse inference does not suffer from the problems highlighted in the recent literature, and I defend the appropriateness of treating reverse inference in these terms. 1 Introduction2 Reverse Inference3 Reverse Inference Defended3.1 Typical reverse inferences are fallacious3.2 No quick and (...) easy fix3.3 A likelihoodist defense of reverse inference3.4 An example4 Appropriateness of the Likelihoodist Approach4.1 Likelihoodist reverse inference is not applicable4.2 Cognitive neuroscientists are not interested in comparative conclusions4.3 Reverse inference and negative hypotheses4.4 Likelihoodist reverse inference may confuse cognitive neuroscientists4.5 Bayesian reverse inferences should be preferred to likelihoodist reverse inferences5 Conclusion. (shrink)

We investigate the epistemological consequences of a positive polymerase chain reaction SARS-CoV test for two relevant hypotheses: V is the hypothesis that an individual has been infected with SARS-CoV-2; C is the hypothesis that SARS-CoV-2 is the cause of flu-like symptoms in a given patient. We ask two fundamental epistemological questions regarding each hypothesis: First, how much confirmation does a positive test lend to each hypothesis? Second, how much evidence does a positive test provide for each hypothesis against its negation? (...) We respond to each question within a formal Bayesian framework. We construe degree of confirmation as the difference between the posterior probability of the hypothesis and its prior, and the strength of evidence for a hypothesis against its alternative in terms of their likelihood ratio. We find that test specificity—and coinfection probabilities when making inferences about C—were key determinants of confirmation and evidence. Tests with 8) for V against ¬V regardless of sensitivity. Accordingly, low specificity tests could not provide strong evidence in favor of C in all plausible scenarios modeled. We also show how a positive influenza A test disconfirms C and provides weak evidence against C in dependence on the probability that the patient is influenza A infected given that his/her symptoms are not caused by SARS-CoV-2. Our analysis points out some caveats that should be considered when attributing symptoms or death of a positively tested patient to SARS-CoV-2. (shrink)

We introduce a distinction, unnoticed in the literature, between four varieties of objective Bayesianism. What we call ' strong objective Bayesianism' is characterized by two claims, that all scientific inference is 'logical' and that, given the same background information two agents will ascribe a unique probability to their priors. We think that neither of these claims can be sustained; in this sense, they are 'dogmatic'. The first fails to recognize that some scientific inference, in particular that concerning evidential relations, is (...) not (in the appropriate sense) logical, the second fails to provide a non-question-begging account of 'same background information'. We urge that a suitably objective Bayesian account of scientific inference does not require either of the claims. Finally, we argue that Bayesianism needs to be fine-grained in the same way that Bayesians fine-grain their beliefs. (shrink)

The underdetermination thesis poses a threat to rational choice of scientific theories. We discuss two arguments for the thesis. One draws its strength from deductivism together with the existence thesis, and the other is defended on the basis of the failure of a reliable inductive method. We adopt a partially subjective/objective pragmatic Bayesian epistemology of science framework, and reject both arguments for the thesis. Thus, in science we are able to reinstate rational choice called into question by the underdetermination thesis.

The likelihood principle is typically understood as a constraint on any measure of evidence arising from a statistical experiment. It is not sufficiently often noted, however, that the LP assumes that the probability model giving rise to a particular concrete data set must be statistically adequate—it must “fit” the data sufficiently. In practice, though, scientists must make modeling assumptions whose adequacy can nevertheless then be verified using statistical tests. My present concern is to consider whether the LP applies to these (...) techniques of model verification. If one does view model verification as part of the inferential procedures that the LP intends to constrain, then there are certain crucial tests of model verification that no known method satisfying the LP can perform. But if one does not, the degree to which these assumptions have been verified is bracketed from the evidential evaluation under the LP. Although I conclude from this that the LP cannot be a universal constraint on any measure of evidence, proponents of the LP may hold out for a restricted version thereof, either as a kind of “ideal” or as defining one among many different forms of evidence. (shrink)