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I once saw an advert claiming that a pregnancy test was “over 99% accurate”. This inspired me to invent an only-slightly-worse pregnancy test, which is over 98% accurate. My invention is a rock with “NOT PREGNANT” scrawled on it: when applied to a randomly selected human being, it is right more than 98% of the time. It is also cheap, non-invasive, endlessly reusable, perfectly consistent, immediately effective and impossible to apply incorrectly; this massive improvement in cost and convenience is obviously worth the ~1% decrease in accuracy.
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This is a general problem with the measure of accuracy. In binary classification, with two events and , "accuracy" is broadly defined as the probability of the "if and only if" biconditional, . Which is equivalent to . It's the probability of both events having the same truth value, of either both being true or both being false.
In terms of diagnostic testing it is the probability of the test being positive if and only if the tested condition (e.g. pregnancy) is present.
The problem with this is that the number is strongly dependent ...
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I think they meant over 99% when used on a non-randomly selected human who's bothering to take a pregnancy test. Your rock would run maybe 70% or so on that application.
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Also see Scott Alexander's Heuristics That Almost Always Work.