Hypothesis Testing in Scientific Practice: An Empirical Study

• Published in: International studies in the philosophy of science Vol. 33; no. 1; pp. 1 - 21
• Main Author: Mizrahi, Moti
• Format: Journal Article
• Language: English
• Published: Abingdon Routledge 02.01.2020; Taylor & Francis Ltd
• Subjects: Bayesian analysis; Bayesian confirmation; Confirmation; Corpus linguistics; Data science; Deduction; Hypotheses; Hypothesis testing; hypothetico-deductive method; Induction; Life sciences; Linguistics; Philosophers; Science; Scientific papers; Social sciences
• ISSN: 0269-8595; 1469-9281
• DOI: 10.1080/02698595.2020.1788348

It is generally accepted among philosophers of science that hypothesis testing (or confirmation) is a key methodological feature of science. As far as philosophical theories of confirmation are concerned, some emphasize the role of deduction in confirmation (e.g. the H-D method), whereas others emphasize the role of induction in confirmation (e.g. Bayesian theories of confirmation). The aim of this paper is to contribute to our understanding of scientific confirmation (or hypothesis testing) in scientific practice by taking an empirical approach. I propose that it would be illuminating to learn how practicing scientists describe their methods when they test hypotheses and/or theories. I use the tools of data science and corpus linguistics to study patterns of usage in a large corpus of scientific publications mined from the JSTOR database. Overall, the results of this empirical survey suggest that there is an emphasis on mostly the inductive aspects of confirmation in the life sciences and the social sciences, but not in the physical and the formal sciences. The results also point to interesting and significant differences between the scientific subjects within these disciplinary groups that are worth investigating in future studies.