This paper explores how data serve as evidence for phenomena. In contrast to standard philosophical models which invite us to think of evidential relationships as logical relationships, I argue that evidential relationships in the context of data-to-phenomena reasoning are empirical relationships that depend on holding the right sort of pattern of counterfactual dependence between the data and the conclusions investigators reach on the phenomena themselves.

This article introduces and defends a probabilistic measure of the explanatory power that a particular explanans has over its explanandum. To this end, we propose several intuitive, formal conditions of adequacy for an account of explanatory power. Then, we show that these conditions are uniquely satisfied by one particular probabilistic function. We proceed to strengthen the case for this measure of explanatory power by proving several theorems, all of which show that this measure neatly corresponds to our explanatory intuitions. Finally, (...) we briefly describe some promising future projects inspired by our account. (shrink)

Recently, in attempting to account for explanatory reasoning in probabilistic terms, Bayesians have proposed several measures of the degree to which a hypothesis explains a given set of facts. These candidate measures of "explanatory power" are shown to have interesting normative interpretations and consequences. What has not yet been investigated, however, is whether any of these measures are also descriptive of people’s actual explanatory judgments. Here, I present my own experimental work investigating this question. I argue that one measure in (...) particular is an accurate descriptor of explanatory judgments. Then, I discuss some interesting implications of this result for both the epistemology and the psychology of explanatory reasoning. (shrink)

Recent work on inference to the best explanation has come to an impasse regarding the proper way to coordinate the theoretical virtues in explanatory inference with probabilistic confirmation theory, and in particular with aspects of Bayes's Theorem. I argue that the theoretical virtues are best conceived heuristically and that such a conception gives us the resources to explicate the virtues in terms of ceteris paribus theorems. Contrary to some Bayesians, this is not equivalent to identifying the virtues with likelihoods or (...) priors per se; the virtues may be more accessible epistemically than likelihoods or priors. I then prove a ceteris paribus theorem regarding theoretical consilience, use it to correct a recent application of Reichenbach's common cause principle, and apply it to a test case of scientific reasoning. Explanation and confirmation The heuristic conception of theoretical virtues Abduction and the accessibility of explanatory power Evidential and theoretical consilience A test case: gravitational lensing Conclusion Thus natural science appears completely to lose from sight the large and general questions; but all the more splendid is the success when, groping in the thicket of special questions, we suddenly find a small opening that allows a hitherto undreamt of outlook on the whole. (L. Boltzmann, Theoretical Physics and Philosophical Problems). (shrink)

Bogen and Woodward claim that the function of scientific theories is to account for 'phenomena', which they describe both as investigator-independent constituents of the world and as corresponding to patterns in data sets. I argue that, if phenomena are considered to correspond to patterns in data, it is inadmissible to regard them as investigator-independent entities. Bogen and Woodward's account of phenomena is thus incoherent. I offer an alternative account, according to which phenomena are investigator-relative entities. All the infinitely many patterns (...) that data sets exhibit have equal intrinsic claim to the status of phenomenon: each investigator may stipulate which patterns correspond to phenomena for him or her. My notion of phenomena accords better both with experimental practice and with the historical development of science. (shrink)

The papers collected here are the result of an INTERNATIONAL SYMPOSIUM: Data · Phenomena · Theories: What’s the notion of a scientific phenomenon good for? held in Heidelberg in September 2008. The event was organized by the research group Causality, Cognition, and the Constitution of Scientific Phenomena in cooperation with Philosophy Department at the University of Heidelberg (Peter McLaughlin and Andreas Kemmerling) and the IWH Heidelberg. The symposium was supported by the Emmy-Noether-Programm der Deutschen Forschungsgemeinschaft and by Stiftung Universitat Heidelebrg (...) . The workshop was held in honor of Daniela Bailer-Jones, who died on 13 November 2006 at the age of 37 (cf. my 2007 Daniela Bailer-Jones ). Bailer-Jones was an Emmy Noether fellow, and the symposium was arranged and run by those who were working in her research group at the time of her death: Monika Dullstein, Jochen Apel, and Pavel Radchencko. To them goes the credit for the conception, planning, and carrying out of the symposium. (shrink)

Bogen and Woodward (1988) advance adistinction between data and phenomena. Roughly, theformer are the observations reported by experimentalscientists, the latter are objective, stable featuresof the world to which scientists infer based onpatterns in reliable data. While phenomena areexplained by theories, data are not, and so theempirical basis for an inference to a theory consistsin claims about phenomena. McAllister (1997) hasrecently offered a critique of their version of thisdistinction, offering in its place a version on whichphenomena are theory laden, and hence (...) on which theempirical support for inferences to theories is also,unavoidably, theory laden. In this commentary I arguethat McAllister and Bogen and Woodward are mistaken inthinking that the distinction is necessary, and thatthe empirical support for inferences to theories isnot necessarily theory laden in the way McAllister'saccount entails they are. (shrink)