https://doi.org/10.1140/epjp/s13360-022-03629-z
Regular Article
Six textbook mistakes in data analysis
1
Department of Physics, University of Guelph, N1G 2W1, Guelph, ON, Canada
2
Office of Teaching and Learning, University of Guelph, N1G 2W1, Guelph, ON, Canada
Received:
21
September
2022
Accepted:
23
December
2022
Published online:
12
January
2023
This article discusses a number of incorrect statements appearing in textbooks on data analysis, machine learning or computational methods; the common theme in all these cases is the relevance and application of statistics to the study of scientific or engineering data; these mistakes are also quite prevalent in the research literature. Crucially, we do not address errors made by an individual author, focusing instead on mistakes that are widespread in the introductory literature. After some background on frequentist and Bayesian linear regression, we turn to our six paradigmatic cases, providing in each instance a specific example of the textbook mistake, pointers to the specialist literature where the topic is handled properly, along with a correction that summarizes the salient points. The mistakes (and corrections) are broadly relevant to any technical setting where statistical techniques are used to draw practical conclusions, ranging from topics introduced in an elementary course on experimental measurements all the way to more involved approaches to regression.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
