I think he's saying that since the distribution follows a log-normal, the mean is harder to pin down than if it were a normal due to the higher variance. From t

I think he's saying that since the distribution follows a log-normal, the mean is harder to pin down than if it were a normal due to the higher variance. From the CLT, the variance of the mean statistic is var(X)/sqrt(N) -- the var(X) is higher and he'd expect from an assumed normal of X. The other point he makes is that the mean is not very representative in this wide log-normal scenario, and optimizing for the difference between min and mean is more relevant. My 2 cents. Also to nitpick on the article: "It is not possible, in a normal distribution, to be multiple times the standard deviation away from the mean." I guess he meant probable :)