A non-Bayesian approach to scientific inference on treatment-effects - Subrato Banerjee; Benno Torgler (Center for Research in Economics, Management and the Arts (CREMA), 2020)
Because the use of p-values in statistical inference often involves the rejection of a hypothesis on the basis of a number that itself assumes the hypothesis to be true, many in the scientific community argue that inference should instead be based on the hypothesis' actual probability conditional on supporting data. In this study, therefore, we propose a non-Bayesian approach to achieving statistical inference independent of any prior beliefs about hypothesis probability, which are frequently subject to human bias. In doing so, we offer an important statistical tool to biology, medicine, and any other academic field that employs experimental methodology.
Keywords: 330; Statistical inference; experimental science; hypothesis testing; conditional probability