![]() Typically in an effort to find something to write about people will do things that make them more likely to reject by continuing to act as if they had not done those things, they're computing p-values in a way that doesn't account for their actions, making their p-values calculated in the way they do lower than they should be. ![]() Under p-hacking that distribution is no longer uniform. Specifically, for a continuously distributed test statistic if H0 is true, the p-values should be uniformly distributed between 0 and 1 (so that for a test at level alpha, the proportion of the p-values that are ≤ alpha is alpha ie the rejection rate under H0 is actually the alpha we said it was, for any alpha). P-hacking is undertaking a strategy (whether deliberately or not) which produces p-values that don't have the correct properties, typically moving a correctly-calculated p-value lower than what it was specified to be. It's not clear quite what you're getting at. R-bloggers - blog aggregator with statistics articles generally done with R software.
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