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[–] 4 points 5 days ago (1 child)

You don’t need to look up how a p value is calculated, the calculation depends entirely on your model and hypothesis assumptions.

The best way I’ve heard a p value described was “the degree to which the data are embarrassed by the null hypothesis”. It’s the most succinct, correct, and easy to remember description.

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  • [–] 2 points 5 days ago (2 children)

    P value just tells you how likely the difference between two data sets is just random chance. Having said that, there are many ways to p hack to achieve significant values.

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  • [–] 1 point 5 days ago

    I’m probably being too pedantic, but that’s exactly correct, though it also captures the spirit and practically most common cases.

    Just change “two datasets” to “a statistic”, because hypothesis tests can be pretty general.

    I don’t love P values, they’re not quite an intuitive probability, they bake in a lot of assumptions, they can be deceptively over or under sensitive, and they’re hard to explain. As a tool they’re useful but they always need context and planning.

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