Explaining evidential discordance

Abstract

Abstract Evidential discordance, or the situation where scientific evidence supports contrary hypotheses, claims or theories about a given phenomenon, is common in scientific inquiry (Franklin, 2002; Boyd, 2018; Stegenga, 2009, 2012). Although in many ways useful for scientific progress, discordance also poses distinct epistemic and methodological challenges for science, as it hinders building consensus and causes problems for decision making (Collins, 1985; Hey, 2015a; Maziarz, 2024; Reijula and Blanco Sequeiros, 2024). To address these challenges, philosophers of science have discussed episodes of disagreement in scientific evidence and presented strategies for dealing with discordance (Bokulich, 2020; Ohnesorge, 2021; Reijula & Blanco Sequeiros, 2024). Despite this discussion, the concept of evidential discordance itself has remained overlooked. In this paper, I clarify the concept of discordant evidence, and propose a novel explanation for the emergence of evidential discordance into scientific evidence. I argue that evidential discordance can be explained by disagreement between inferences from data to phenomena between various lines of evidence. The data-to-phenomenon inferences conflict because of how they conceptualize the causal tracks of the phenomenon of interest within data. This explanation for discordance is useful because it unifies a variety of empirical explanations for discordance, including error, noise and bias. I use examples from the social sciences to showcase my arguments but the lessons extend to other areas of philosophy of science as well.

The authors' abstract, as published at the source. Synthese, 2026 · DOI ↗