Just trying to reconcile something in my head regarding sample distributions as described in the book: 1) To test a 1 rule strategy for 'edge' or statistical significance over random, Aronson recommends using Monte Carlo or Bootstrap to estimate a sample distribution and compare your test to the sample distribution. 2) When testing a number of rules, in this case lets say one rule optimized over many parameters, he recommends performing the MC over all N rules, taking the highest random return over the N rules and including that into the sample distribution.... So my question, if you optimize a strategy, you'll likely pick the rule with highest return (or drawdown, etc.) What if you just utilizing #1 on this optimized rule? How would that be any better then coming up with a single rule and using #1 to test for significance? Or is rule #1 inherently biased (as in you were lucky picking that 1 rule)? Kinda late at night so let me know if this needs clarification?