Friday, March 6, 2009

Simplicity is no substitute for correctness, but simplicity has an important role

The test of a good procedure is how well it works, not how well it is understood. -- John Tukey
Perhaps I'm abusing Tukey's quote here, because I'm speaking of situations where the theory of the less understood methodology is fairly well understood, or at least fairly obvious to the statistician from previous theory. I'm also, in some cases, substituting "how correct it is" in place of "how well it works."

John Cook wrote a little the other day on this quote, and I wanted to follow up a bit more. I've run into many situations where a more understood method was preferred over one that would have, for example, cut the sample size of a clinical trial or made better use of the data that was collected. The sponsor simply wanted to go with the method that was taught in the first year statistics course because it was easier to understand. The results were often analysis plans that were less powerful, covered up important issues, or simply wrong (i.e. exact answer to the wrong question). It's a delicate balance especially for someone trained in theoretical statistics corresponding with a scientist or clinician in a very applied setting.

Here's how I resolve the issue. I think that the simpler methods are great for storytelling. I appreciate Andrew Gelman's tweaks to the simpler methods (and his useful discussing on Tukey as well!), and think basic graphing and estimation methods serve a useful purpose for presentation and first-order approximations of data analysis. But, in most practical cases, they should not be the last effort.

On a related note, I'm sure most statisticians know by know that they will have the "sexiest job" of the 2010 decade. The key will be how well we communicate our results. And here is where judicious use of the simpler methods (and creative data visualization) will make the greatest contributions.