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P-values work great when they’re super low, experiments run at a human-scale frequency, and hypotheses are extremely precise in their predictions, e.g. some physics.

If you run an experiment a day and get p < 10^-9, your priors, your multiple hypothesis correction, even your interpretation of p-values approximately don’t matter. Running social sciences experiments with p < 0.05 threshold is where things get weird.



Did you read the article?

>even your interpretation of p-values approximately don’t matter

"Small number means good" is not a sufficient working understanding of p-values for doing science.


But what if it’s really small?


It's completely irrelevant if you don't understand how to interpret it. It is not a number which tells you how correct your hypothesis is.

That is literally mindless statistics. Which coincidentally is the name of the article I talked about. Did you read it?

(In a social science, if your p-value is 1E-5 or something, the most likely interpretation is that you are doing something very wrong)


I did. My statement was hyperbolic. More directly: P-values are more resilient to misuse at their extremes.


ok




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