A Plea for History and Philosophy of Statistics and Machine Learning (2506.22236v1)

Abstract: The integration of the history and philosophy of statistics was initiated at least by Hacking (1965) and advanced by Mayo (1996), but it has not received sustained follow-up. Yet such integration is more urgent than ever, as the recent success of artificial intelligence has been driven largely by machine learning -- a field historically developed alongside statistics. Today, the boundary between statistics and machine learning is increasingly blurred. What we now need is integration, twice over: of history and philosophy, and of the field they engage -- statistics and machine learning. I present a case study of a philosophical idea in machine learning (and in formal epistemology) whose root can be traced back to an often under-appreciated insight in Neyman and Pearson's 1936 work (a follow-up to their 1933 classic). This leads to the articulation of a foundational assumption -- largely implicit in, but shared by, the practices of frequentist statistics and machine learning -- which I call achievabilism. Another integration also emerges at the level of methodology, combining two ends of the philosophy of science spectrum: history and philosophy of science on the one hand, and formal epistemology on the other hand.