Standard deviation as outlier detection I have a quite basic question: A standard deviation is defined such that around ~66 % of the data lies within it. And around ~99 % within three standard deviations.

When I wanna' use the standard deviation as an outlier detection, I struggle with this definition as there will always be outlier. But I probably have a misunderstanding somewhere, somehow..   

In other words: 3 standard deviations contain always about 99 % of the data, hence there will always be $ outlier &gt; 3 std $, no matter if they are outlier.

What is the issue I don't see?
Standard deviation isn't an outlier detector. It can't tell you if you have outliers or not.

What it will do is effectively remove outliers that do exist, with the risk of deleting a small amount of inlying data if it turns out there weren't any outliers after all. As such, I think it's useful as a "quick-and-dirty don't want to spend too much time on this problem" method of ensuring your dataset only contains inlying data points. As long as you understand the risk of potentially binning 1% of inlying datapoints, I think it's fine.