When Numbers Mislead Us

Arthur Charpentier ()
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Arthur Charpentier: UQAM - Université du Québec à Montréal = University of Québec in Montréal

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Abstract: Believing that there is a single, objective way to describe phenomena using numbers is to forget that data does not "speak" for itself. Collecting data involves making choices: what to measure, how, when, on whom, etc. This implies implicit (or even ideological) assumptions about what counts as a measurable fact. And in any data analysis, what is not measured can be as important as what is observed. When an influential variable is overlooked-whether ignored, neglected, or simply unknown-the apparent relationships between other variables can become misleading. This is known as "omitted variable bias": a hidden effect distorts comparisons and can make a correlation appear where there is none, or mask a real one. Sometimes, introducing this "forgotten" variable can even completely reverse the conclusions that would have been drawn from a naive reading of the data.

Keywords: Simpson's paradox; Ecological fallacy (search for similar items in EconPapers)
Date: 2025-07-07
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