[jmount]

There is a technical reason to prefer Mean Square Error derived measures (like RMSE and standard deviation) in some situations (such as machine learning and value estimation): when minimizing one of these measures you tend towards the mean and get expected values correct. Expected values are additive: so they roll up nicely (get the individuals right and you also have the group).

My example tends to be lottery tickets. You minimize MAD by saying they are all worth zero (which is pretty much my opinion). But then you don't get the value of the lottery by summing up all the ticket values. You do/should get get with mean/expectation based estimates.

More of my writing on this: http://www.win-vector.com/blog/2014/01/use-standard-deviatio... . Though I am also a fan of quantile regression (it just solves different problems).