Beyond P<0.05 : Scientific Inference in Accounting Research

Although the limitations of p-values are well known, researchers continue to (mis)use them for lack of awareness of the alternatives. This monograph comprehensively explains the process of scientific inference, of which the p-value is just one component. The inference process is broader, consisting of research topic choice, assessing plausibility of the tested theory, choosing research designs having high diagnostic ability, and interpreting the results. The best researchers have always followed this process, and the monograph shows how it follows the Bayesian scheme of inference, although not quantified numerically. Aimed at Ph.D. students and rising scholars, the monograph presents the issues in a non-technical language, with plenty of examples from accounting and other disciplines. It helps scholars to choose and motivate their research topic, identify better research designs, and communicate their findings. Technical topics covered include well-known ones such as confidence intervals, as well as less-well-known ones such as Bayesianized p-values, false discovery rates, and hierarchical Bayesian models. They are presented in a non-technical manner, with applications. All computer code used is available online. Qualitative topics covered include how to choose research topics and designs, and write the paper like a story. The monograph urges researchers to choose settings with a high signal-to-noise ratio, rather than get statistical power through large samples. Overall, the monograph makes a compelling case for using the broader approach to scientific inference as a way of going beyond the limitations of p-values, and offers practical advice with plenty of examples on how to do so. It is written in an engaging and accessible style, making it a valuable resource for both novice and experienced researchers in accounting and related fields