Normality of variables in correlation test and simple linear regression

A correlation test and a simple linear regression test both answer the same question, which is

Is there a good chance that my data comes from a population where the correlation is positive rather than negative / where the regression line has a positive slant rather than negative?

Both are based on the Least Squares method, and output the same p-value. They're basically the same test with a slightly different wording of the interpretation (also apart from the fact that Linear Regression allows you to make predictions which correlation alone can't).

I would therefore expect similar assumptions in both tests. Yet normality of variables is a standard requirement in the correlation test, and is not required in the linear regression test.

Why is that?

• Note that the linear regression test assumes the $x$ to be fixed, so you're right that the requirements are not the same (which is remarkable enough),but it's not the case that the linear regression test requires less. It requires something that looks different at first sight. Oh, and the regression test assumes the errors to be normal (unless asymptotic arguments are used), though not both variables. Commented Feb 10, 2020 at 15:24
• @Lewian Yes, it is remarkable that the regression line is not the same if $x$ and $y$ are switched, although the $R^2$ will be the same. So you're saying that somehow the assumptions are actually equivalent for the two tests? Commented Feb 10, 2020 at 15:40
• No, they're certainly not. Nice question. Commented Feb 10, 2020 at 15:54

• As far as I know, the t-distributions of the test statistics for both the standard correlation test under bivariate normal assumption, and the test for testing a simple linear regression coefficient to be zero under normal assumption for errors only given fixed $x$-values are both precise, so the answer to the original question has nothing to do with the central limit theorem, I'd think. Commented Feb 10, 2020 at 15:22