Academic statisticians: there is no shame in developing statistical solutions that solve just one problem

25 Jul 2014 By Rafa Irizarry

I think that the main distinction between academic statisticians and those calling themselves data scientists is that the latter are very much willing to invest most of their time and energy into solving specific problems by analyzing specific data sets. In contrast, most academic statisticians strive to develop methods that can be very generally applied across problems and data types. There is a reason for this of course:  historically statisticians have had enormous influence by developing general theory/methods/concepts such as the p-value, maximum likelihood estimation, and linear regression. However, these types of success stories are becoming more and more rare while data scientists are becoming increasingly influential in their respective areas of applications by solving important context-specific problems. The success of Money Ball and the prediction of election results are two recent widely publicized examples.

A survey of papers published in our flagship journals make it quite clear that context-agnostic methodology are valued much more than detailed descriptions of successful solutions to specific problems. These applied papers tend to get published in subject matter journals and do not usually receive the same weight in appointments and promotions. This culture has therefore kept most statisticians holding academic position away from collaborations that require substantial time and energy investments in understanding and attacking the specifics of the problem at hand. Below I argue that to remain relevant as a discipline we need a cultural shift.

It is of course understandable that to remain a discipline academic statisticians can’t devote all our effort to solving specific problems and none to trying to the generalize these solutions. It is the development of these abstractions that defines us as an academic discipline and not just a profession. However, if our involvement with real problems is too superficial, we run the risk of developing methods that solve no problem at all which will eventually render us obsolete. We need to accept that as data and problems become more complex, more time will have to be devoted to understanding the gory details.

But what should the balance be?

Note that many of the giants of our discipline were very much interested in solving specific problems in genetics, agriculture, and the social sciences. In fact, many of today’s most widely-applied methods were originally inspired by insights gained by answering very specific scientific questions. I worry that the balance between application and theory has shifted too far away from applications. An unfortunate consequence is that our flagship journals, including our applied journals, are publishing too many methods seeking to solve many problems but actually solving none.  By shifting some of our efforts to solving specific problems we will get closer to the essence of modern problems and will actually inspire more successful generalizable methods.