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    $\begingroup$ There is no error in the original formula: see en.wikipedia.org/wiki/Pearson_correlation_coefficient. You will discover your formula doesn't seem to work, either, if you set n <- 3 instead of 100 and r <- 0 instead of 0.8. There are several problems, one of which is that the formula of the question is a reasonable standard error only when $r$ is close to zero. Another is that the sampling distribution can be highly skewed, rendering any SE less than useful: that's why better analyses re-express $r$ with the Fisher transformation. $\endgroup$ Commented Nov 6, 2018 at 16:02
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    $\begingroup$ I think that n=3 is a too special case to judge. I don't know, maybe n-2 is not the good divisor, maybe it is n-3. Beyond that, the two formulae give the same result for r=0. $\endgroup$ Commented Nov 6, 2018 at 17:05
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    $\begingroup$ Please explain the downvotes. In addition, I would like to know where I can find the formula of the standard error of the raw correlation coefficient (not the Fisher transform) in the Wikipedia page. Logically it should be in this section: en.wikipedia.org/wiki/Pearson_correlation_coefficient#Inference, but I am unable to see it. $\endgroup$ Commented Oct 10, 2019 at 15:36
  • $\begingroup$ First of all: 'Checking' by simulation is not a mathematical proof. Second, just because it is not on wikipedia it does not mean it is not existent. It just means no one has yet copy/paste information to it. $\endgroup$ Commented Jul 10, 2022 at 8:19
  • $\begingroup$ Yes checking by simulation is not a mathematical proof, but if simulations do not match a formula but match another there are reasons to think that the other formula is better. For Wikipedia it is just the other guy who suggested me to check the formula there, I just answered that I was unable to find it, I did not deduce anything more. But now the formula of the standard error has been added, and guess what, that's not the formula of the question but the one of my answer which is written there. $\endgroup$ Commented Jul 22, 2022 at 19:45