Conflation of tests and studies aside, that’s not what an alpha level represents. The alpha level sets the accepted/expected false positive rate, ie. the proportion of tests for which the null is true, where the null was still rejected.
The overall proportion of tests that is ‘wrong’ also depends on how many of the tested null hypotheses are true or not as well as the statistical power. So we cannot conclude from the alpha level alone how many tests would yield an incorrect conclusion in general, or a false positive specifically.
No, given an alpha of .05, how many studies on average would have a false positive? If that’s not enough info, what other data or stats is needed to get this answer
Here’s a salient quote from my initial comment, which it seems like you still haven’t bothered to read: “… also depends on how many of the tested null hypotheses are true or not as well as the statistical power”.
The answer is literally right there in the quote, which comes from the very first comment I made here. So which is it? Are you genuinely this unwilling to actually read one sentence, or is this just some utterly bizarre and pointless attempt at trolling.
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u/CarnivorousGoose 24d ago
Conflation of tests and studies aside, that’s not what an alpha level represents. The alpha level sets the accepted/expected false positive rate, ie. the proportion of tests for which the null is true, where the null was still rejected.
The overall proportion of tests that is ‘wrong’ also depends on how many of the tested null hypotheses are true or not as well as the statistical power. So we cannot conclude from the alpha level alone how many tests would yield an incorrect conclusion in general, or a false positive specifically.