I'd like to try out an explanation of something on you all: A p-value evaluates an argument that the data proves some claim, by applying the same argument to a fake model where we know the claim is false. The p-value is how often we would erroneously conclude it was true anyway.
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Disclaimer: I am not AT ALL expert in stats, so you should listen to someone who knows what they are talking about instead. However, I’m still pretty sure your definition is wrong.
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What p values *actually* do is remarkably subtle, and (more importantly) usually not at all what you want, which is why no one can ever remember. If you want to get it right, you either need to understand it deeply (better!) or copy a definition verbatim from a trusted source.
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