Believing there is a single, objective way to describe phenomena through numbers is to forget that data doesn’t “speak” on its own. Collecting data means making choices: what to measure, how, when, on whom, etc. This involves implicit (even ideological) assumptions about what counts as a measurable fact. And in any data analysis, what isn’t measured can be just as important as what is observed. When an influential variable is omitted—ignored, overlooked, or simply unknown—the apparent relationships between other variables can become misleading. This is known as the “omitted variable bias”: a hidden effect distorts comparisons and may create a correlation where there is none, or obscure a real one. Sometimes, introducing this “forgotten” variable can completely reverse conclusions drawn from a naive reading of the data. This corresponds to Simpson’s paradox.
A brief article on Simpson’s paradox, written as a book chapter (for a book that will be published in French in the Fall, or in the Winter), is now available.

OpenEdition suggests that you cite this post as follows:
Arthur Charpentier (July 10, 2025). When Numbers Mislead Us. Freakonometrics. Retrieved December 6, 2025 from https://doi.org/10.58079/14bg5