Someone named Nathan writes:

I am an undergraduate student in statistics and a reader of your blog. One thing that you’ve been on about over the past year is the difficulty of executing hypothesis testing correctly, and an apparent desire to see researchers move away from that paradigm. One thing I see you mention several times is to simply “model the problem directly”. I am not a masters student (yet) and am also not trained at all in Bayesian. My coursework was entirely based on classical null hypothesis testing.

From what I can gather, you mean the implementation of some kind of multi-level model. But do you also mean the fitting and usage of standard generalized linear models, such as logistic regression? I have ordered the book you wrote with Jennifer Hill on multi-level models, and I hope it will be illuminating.

On the other hand, I’m looking at going to graduate school and I will be applying this fall. My interests have diverged from classical statistics, with a larger emphasis on model building, prediction, and machine learning. To this end, would further training in statistics be appropriate? Or would it be more useful to try and get into a CS program? I still have interests in “statistics” — describing associations, but I am not so sure I am interested in being a classical theorist. What do you think?

My reply: There are lots of statistics programs that focus on applications rather than theory. Computer science departments, I don’t know how that works. If you want an applied-oriented statistics program, it could help to have a sense of what application areas you’re interested in, and also if you’re interested in doing computational statistics, as a lot of applied work requires computational as well as methodological innovation in order to include as much relevant information as possible into your analyses.