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There’s a lot to like about this section, but my experience as a clinical psychologist inclines me to approach this topic differently. The irregular features are instead aspects of the data that do not generalize and so may mislead us. Regular features are useful, given an objective of our choice. The regular features are the targets of our learning, because they generalize well or answer a question of interest.
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What this means is that there are both regular and irregular features in every sample. Overfitting occurs when a model learns too much from the sample. The great Frank Harrell has complied A litany of problems With p-values and you might consult the recent statement on \(p\)-values by the American Statistical Association (Wasserstein & Lazar, 2016).Ħ.1.1 More parameters always improve fit. If you’d like to learn more from a Bayesian perspective, you might check out the first several chapters (particularly 10–13) in Kruschke’s ( 2015) text and my ( 2020 a) ebook translating it to brms and the tidyverse. McElreath spent little time discussing \(p\)-values and null hypothesis testing in the text. …Whatever you thing about null hypothesis significance testing in general, using it to select among structurally different models is a mistake– \(p\)-values are not designed to help you navigate between undercutting and overfitting. Statisticians sometimes call this stargazing, as it is embodied by scanning for asterisks ( \(^\)) trailing after estimates… The most common form of model selection among practicing scientists is a search for a model in which every coefficient is statistically significant.
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