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While I do agree that false positive and false negative are better names, they do have one shortcoming in comparison:

Classical statistics suffers from the inference problem, where instead of "tested positive for presence" you have to say "tested negative for absence". So a type I error is a false negative as much as it is false positive, which can get confusing.




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