>Surely, only one is true. This is my general comment about statistics and data analysis without going into the specifics of Freq vs Bayes: As anyone who ever

>Surely, only one is true. This is my general comment about statistics and data analysis without going into the specifics of Freq vs Bayes: As anyone who ever worked with real-world data can tell you, for the most part data analysis is more of an art than exact science. Sure, it's math, and once you pick the right model, there is (usually) one correct way to solve it. The problem is that most mathematical methods come with assumptions that are almost never met in practice, so you have to make a lot of (often fairly arbitrary) decisions about how to go about your data analysis. How do you handle missing data cases? Is your data normally distributed enough to justify the use of some method? Is the sample large enough? Are those residuals in your model diagnostics random enough? Does that trend line look linear enough? What prior information can I use (and how?) to formally add value to the model? Real world is messy.