Simple methods are great, and “simple” doesn’t always mean “stupid” . . .
Here’s the mini-talk I gave a couple days ago at our statistical consulting symposium. It’s cool stuff: statistical methods that are informed by theory but can be applied simply and automatically to get more insights into models and more stable estimates. All the methods described in the talk derived from my own recent applied research.
For more on the methods, see the full-length articles:
A message for the graduate students out there
Research is fun. Just about any problem has subtleties when you study it in depth (God is in every leaf of every tree), and it’s so satisfying to abstract a generalizable method out of a solution to a particular problem.
P.S. On the other hand, many of Tukey’s famed quick-and-dirty statistical methods don’t seem so great to me anymore. They were quick in the age of pencil-and-paper computation, and sometimes dirty in the sense of having unclear or contradictory theoretical foundations. (In particular, his stem-and-leaf plots and his methods for finding gaps and clusters in multiple comparisons seem particularly silly from the perspective of the modern era, however clever and useful they may have been at the time he proposed them.)
P.P.S. Don’t get me wrong, Tukey was great, I’m not trying to shoot him down. I wrote the above P.S. just to remind myself of the limitations of simple methods, that even the great Tukey tripped up at times.