I was reading a review of Aronson's book on evidence-based TA and the reviewer stated that one sign that data mining bias was not present in an optimized backtest would be that there were few outliers in the backtest results. I take this to mean that your backtest should show that your results are primarily driven by a few really great trades and that the rest of your trades should sort of average out to something near 0. For those who've looked into this in depth, does this seem like a reasonable takeaway? If so, anyone have any sort of heuristic on what this would look like quantitatively? What if I have a backtest that shows that 10% of the trades generate 50% of the profits? Or 20% generate 80% of the profits? My hunch is that there isn't a hard and fast line here, though. Overall, the point seems valid because the alternative would seem to be that your backtest showed that nearly every trade was a home run, which seems more unlikely to be a characteristic of a strategy that would work out of sample.