Earlier quoted context omitted.
continuing my foolish musings I would have thought originally that getting the mean of the sample and multiplying by 2 would have been a good estimate. But one problem with that is that there is no possibility of the maximum being lower than anything in the sample so there has to be an allowance for uncertainty upwards. Is that the reason for adding 1 to s?
You can show that the max is a comprehensively better estimator than twice the mean. This is one of the rare cases where you can outperform the mean by a large margin. The standard deviation of the mean-based estimator, which measures its accuracy, will go down like 1/sqrt(n) where n is the number of samples in the finite set. This is the rate standardly seen in lots of estimation problems. But the standard deviation…
EDIT : So there's about a fifty fifty chance that the second observation will be bigger than the first. After the second observation the range of possible future observations will be divided into 3 zones, 1 below the lowest of the previous 2 observations 1 above the higher and 1 between the two previous observations, so there is now only a 1/3 possibility of the next observation being above the higher of the 2 previous observations. Next time that drops to 1/4 then 1/5, so that's how the standard deviation decreases proportional to 1/n