Oh No! I Got the Wrong Sign! What Should I Do?

Graduate students, this paper has a handy dandy checklist of potential explanations for "the wrong sign" in empirical research. A related issue is an estimate which is way too big to be believable. I really like the discussion of data mining. When is it a sin and when is it a way to understand the world using data? I also really like the conclusion that many times a wrong sign is a blessing, not a curse. It forces us to really think hard about our theory, data, identification strategy, etc. Hmm...maybe all of our papers would be better if we always started off by getting the wrong sign. Maybe a good rule of thumb is to look back at this checklist not only when you get the wrong sign, but also when you happen to get the right one of reasonable magnitude.