That's only one of many things you can do in statistics, and it's called the "maximum likelihood" estimator. By competent statisticians it's given only the importance that it's due: If you want to know which parameter makes the known outcome as probable as it can be, according to your model, that's it. —Dan Steve Witham wrote: ----- ... From my impression of statistics, what you do is start with a general model of how things work, which has hidden parameters, and then given an actual outcome, try to derive the hidden parameters most likely to give that outcome. (I actually don't understand why the peak of the curve is given such importance.) ... -----