The basis of statistics is math, and I love math. I’ve always loved math. Since I was in grade school (which is as long as I can remember) I’ve loved math. Or maybe it’s just that I love solving problems. Whatever it is, there’s one thing for sure: logic, numbers, and/or data are involved. The thing is, as researchers we want to know that our analyst, statistician, or whomever has crunched the numbers to get insight into the data has done so accurately and thus has provided THE correct answer. I disagree with that. While it’s important to ensure high quality, as a statistician, I have to say, I don’t have THE answer. That’s because there’s about one part math and about three parts creativity involved in getting AN answer (and hence insights) in any situation involving statistical analysis. But frequently, we statisticians have to hide that creativity. And therein lies the challenge. Statistics and modeling are about providing insights. There typically isn’t one single answer or model for any given data set or problem. But if we statisticians provide multiple answers, then we can appear uncertain. Or worse—it may appear as if we have made an error. So, I’m on a mission to promote the “art” in statistics and to declare to all researchers that, as Fisher (a famous statistician) said, “All models are wrong, but some are more useful than others.” A useful model usually results from close collaboration with the team and communication of the goals and application of the analysis. To me, that’s the difference between merely analyzing data and turning data into a work of art. And I like art.
