Three reasons to be cautious when reading data-driven “explanations”

Photo by Sunder Muthukumaran on Unsplash
Did you know that fairly often, there will be multiple extremely different stories you can tell about identical data, none of which are false? In other words, the mapping from statistical results to true stories about those results is not unique. This leads to a lot of confusion, and it also implies that claims about "the reason" behind a complex social phenomenon should be interpreted with caution. Here are 3 common situations of this happening, each illustrated with realistic political ...
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Robust Good

People often underestimate how brittle attempts to improve the world are. Altruism, at a large scale, often hinges on a chain of assumptions/requirements, such as: A & B & C & D & E One broken link in the chain means it looks like you're doing good but you're not really doing it. For example, to help the world by delivering medicine, the chain might look like this: The medicine reliably cures the disease & the side effects are not worse than being...
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