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Berkson's paradox

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Berkson's paradox, also known as Berkson's bias, collider bias, or Berkson's fallacy, is a result in conditional probability and statistics which is often found to be counterintuitive, and hence a veridical paradox. It is a complicating factor arising in statistical tests of proportions. Specifically, it arises when there is an ascertainment bias inherent in a study design. The effect is related to the explaining away phenomenon in Bayesian networks, and conditioning on a collider in graphical models.

Berkson's paradox - Wikipedia Jump to content From Wikipedia, the free encyclopedia Tendency to misinterpret statistical experiments involving conditional probabilities This article includes a list of references , related reading , or external links , but its sources remain unclear because it lacks inline citations . Please help improve this article by introducing more precise citations. ( March 2023 ) ( Learn how and when to remove this message ) An example of Berkson's paradox: Top: a graph where talent and attractiveness are uncorrelated in the population. Bottom: The same graph truncated t

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