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.
I am reasonably sure this is incorrect. See: http://rsos.royalsocietypublishing.org/content/1/3/140216 …
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Hmm. I will read the paper, but I'm reasonably sure it's correct and is just a restatement of the definition of a p-value. Possibly there's some subtlety of interpretation here though.
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What is the “fake model where we know the claim is false”? this isn’t part of my (amateur) understanding.
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For a catalog of ways p values are commonly misunderstood, see: https://link.springer.com/article/10.1007/s10654-016-0149-3 …
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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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