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.
What is the “fake model where we know the claim is false”? this isn’t part of my (amateur) understanding.
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The null hypothesis. e.g. if you want to say "the mean of these two populations is different" your null hypothesis might be that they are drawn from the same normal distribution, which is a fake model that you are testing the argument on.
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Ah. Well, I don’t think this is right: that’s not what the null hypothesis does, and you don’t generally know that the null hypothesis is false. But I’m not expert on this, so I will shut up now!
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