When Numbers Mislead Us

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.