  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.

Data Analysis, Statistics and Probability;Instrumentation and Methods for Astrophysics;Computational Physics