Data, Phenomena, Theories and Explanation

Much of the debate surrounding the well-known distinction between data and phenomena made by Bogen and Woodward has misconstrued their position by attributing to them the claim that theories play no role in relation to data. However, their aim was merely to deny that data are subject to systematic explanation. In this paper, I argue that the original claim that data are not theoretically explained can be interpreted in three ways: data can never be systematically explained; data are not usually or interestingly explained; or data are explained by a different theory to the one that explains the phenomena inferred from them. This plurality of interpretations accounts for the persistent ambiguity in the literature, where discussion often focuses exclusively on the first. After clarifying this ambiguity, I demonstrate that all three interpretations face significant challenges. The first two are undermined by counterexamples, while the third collapses the distinction by re-describing the unexplained data of one theory as the explained phenomena of another. I therefore propose weakening the data–phenomena distinction to capture the contextual difference between the two extremes of a single epistemic process: the mutually integrating and theoretically guided inputs and outputs of scientific knowledge.