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This is the title of Brian Haig’s recent paper in Methods in Psychology 2 (Nov. 2020). Haig is a professor emeritus of psychology at the University of Canterbury. Here he provides both a thorough and insightful review of my book Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars (CUP, 2018) as well as an excellent overview of the high points of today’s statistics wars and the replication crisis, especially from the perspective of psychology. I’ll excerpt from his article in a couple of posts. The full article, which is open access, is here.
Abstract: In this article, I critically evaluate two major contemporary proposals for reforming statistical thinking in psychology: The recommendation that psychology should employ the “new statistics” in its research practice, and the alternative proposal that it should embrace Bayesian statistics. I do this from the vantage point of the modern error-statistical perspective, which emphasizes the importance of the severe testing of knowledge claims. I also show how this error-statistical perspective improves our understanding of the nature of science by adopting a workable process of falsification and by structuring inquiry in terms of a hierarchy of models. Before concluding, I briefly discuss the importance of the philosophy of statistics for improving our understanding of statistical thinking.
Brian Haig
Keywords: The error-statistical perspective, The new statistics, Bayesian statistics, Falsificationism, Hierarchy of models, Philosophy of statistics Continue reading →
putting it too strong, but it is the case that for decades now various camps and subcamps have been exchanging heated arguments about the right statistical methodology. That these skirmishes are not just an academic exercise is clear from the widespread use of statistical methods, and contemporary challenges that cry for more secure foundations: the rise of big data, the replication crisis.
Error Statistics Philosophy 