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.
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.