Statistical Inference as Severe Testing–Review

‘What can psychology’s statistics reformers learn from the error-statistical perspective?’

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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

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Replying to a review of Statistical Inference as Severe Testing by P. Bandyopadhyay

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Notre Dame Philosophical Reviews is a leading forum for publishing reviews of books in philosophy. The philosopher of statistics, Prasanta Bandyopadhyay, published a review of my book Statistical Inference as Severe Testing: How to Get Beyond the Statistics Wars (2018, CUP)(SIST) in this journal, and I very much appreciate his doing so. Here I excerpt from his review, and respond to a cluster of related criticisms in order to avoid some fundamental misunderstandings of my project. Here’s how he begins:

In this book, Deborah G. Mayo (who has the rare distinction of making an impact on some of the most influential statisticians of our time) delves into issues in philosophy of statistics, philosophy of science, and scientific methodology more thoroughly than in her previous writings. Her reconstruction of the history of statistics, seamless weaving of the issues in the foundations of statistics with the development of twentieth-century philosophy of science, and clear presentation that makes the content accessible to a non-specialist audience constitute a remarkable achievement. Mayo has a unique philosophical perspective which she uses in her study of philosophy of science and current statistical practice.[1]

Bandyopadhyay

I regard this as one of the most important philosophy of science books written in the last 25 years. However, as Mayo herself says, nobody should be immune to critical assessment. This review is written in that spirit; in it I will analyze some of the shortcomings of the book.
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