Goodhart’s law is a principle in statistics that states that as the number of variables in a statistical model increases, the variability of the estimated coefficients decreases.
This principle is named after Geoffrey Goodhart, who first proposed it in 1967. It is often used to reduce the variability of estimates of coefficients in multiple regression models.
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