They are the same in that they both create a situation in which the p-value cannot be trusted. However, in one case this is deliberate. In the other it's a prob

They are the same in that they both create a situation in which the p-value cannot be trusted. However, in one case this is deliberate. In the other it's a problem with the whole enterprise. Also, running multiple tests without correcting for multiple testing (usually by reducing the threshold for significance) is just one form of p-hacking. The more insidious version is when one runs the test after every few participants until random chance makes it "slip over the edge of significance". In that case there might not even be enough variables for multiple testing to have occurred, and it becomes very difficult to detect.