If you do experiments and you're always right, then you aren't getting enough information out of those experiments. You want your experiment to be like the flip of a coin: You have no idea if it is going to come up heads or tails. You want to not know what the results are going to be.

-- Peter Norvig, in an interview about being wrong. When I saw this, I thought it sounded a lot like entropy pruning in decision trees, where you don't even bother asking questions that won't make you update your probability estimates significantly. Then I remembered that Norvig was the co-author of the AI textbook that I had learned about decision trees from. Interesting interview.