"If my reasoning is shoddy, but I plug it into some math apparatus, then it'll likely make my problems obviously wrong." That's pretty clearly untrue. I rememb

"If my reasoning is shoddy, but I plug it into some math apparatus, then it'll likely make my problems obviously wrong." That's pretty clearly untrue. I remember reading a study recently where the p value was less than .01 or something like that but where the experimental design was clearly flawed. The correlation wasn't the correlation they thought they had. But because the math looked good and it was easier than actually reviewing the experiment, it was tempting to take the study on face value. I've read Scott's essay before and I understand his argument, but I don't think it works. While, you might be able to avoid some bad reasoning simply by being more systematic, you can also strengthen bad arguments with a faulty application of statistics. What Scott doesn't do is provide an analysis of how often each of these things happens. I'd argue that for each time a quick application of statistics save someone from a bad intuitive judgment, a misapplication of statistics is used to encourage a bad judgment at least one time if not more. Understand that my argument here is not that one should never use statistics or even Bayes theorem, but that a naive or lazy application can be worse than no application.