All of my research so far has been either theory/simulation or descriptive (e.g., this is how much this project did cost). After going to a lecture on someone's

All of my research so far has been either theory/simulation or descriptive (e.g., this is how much this project did cost). After going to a lecture on someone's empirical research two weeks ago, I found myself asking why I have always been kind of turned off by statistical projects. I came up with two answers: One: at this state in my career I need to publish a lot. There is kind of a speed limit on how fast you can publish empirical work, but with theory the upper bound on your publication rate is how hard you work and how creative you are. Two: I feel more honest about my theoretical work. My models and simulations are so obviously contrived that there is no danger of anyone mistaking the map for the territory. By contrast, if you come up with some effect of size X in an empirical paper it might be reported in the media as some kind of golden truth like the charge of an electron. I think if I spent like 12 or 15 months on an empirical project, I also would be so desperate to get a result out of it as to fudge the result. Even among intuitive relationships, the chance you can honestly and provably identify an interesting relationship is really low. For this reason, I read theory and description from all kinds of researchers, but I tend to only be interested in the empirical work from the best-of-the-best. In my field, I think some of the best work is from Rebecca Diamond at Stanford, and one of her most recent papers had a negative result. I also really discount job market papers: these people spent years on a single project and their whole future is riding on finding something cool, so the incentive to fudge is just extraordinary. It's kind of like trusting a judge to try a case in which his son is the defendant. No matter what he told me about how fair he'll be, I wouldn't believe him.