Hosni, H., & Landes, J. (2025). What follows from all that data? Logic in the methodology of data-intensive and AI-driven science. Journal of Applied Logics: The IfCoLog Journal of Logics and their Applications, 12(6), 1593–1610.
Abstract: There is no foreseeable future in which science is not about data and the inferences data license. For centuries, logic has been the tool to analyse infer- ence. And yet, logic is vastly underappreciated in the current methodology of data-driven science, as we argue in this paper. We first outline two historical reasons behind this mismatch, then highlight the need to bridge it by examining a widely used form of scientific inference: Null Hypothesis Significance Testing. Finally, we argue that the question: what follows from data? is ripe to be tack- led by logicians. We submit that this will help lay a sound methodological foundation for the practice of data-intensive and AI-driven science.