Approaching Statistics as a Language
Not long ago, I couldn’t abide statistics. I did respect it, but in much the same way a gazelle respects a lion. Most of my early experiences with statistics indicated that close encounters resulted in pain, so I avoided further contact whenever possible.
So how is it that today I write about statistics? That’s simple: it merely required completely reinventing the way I thought about and approached the discipline. When I decided to approach it as a language rather than a purely mathematical set of skills, the doors opened.
Why does my experience matter to you? If you're a statistician yourself, you know all too well the typical reactions people have when they learn we work with statistics and data analysis: blank stares, uncomfortable silence, horrible jokes, or some variant of, “Oh, how nice." Followed quickly by, "Excuse me, I'm going somewhere I don’t have to talk about statistics.”
People react this way because they’re intimidated by statistics. Maybe you're intimidated by statistics. I certainly used to be...I thought it was too hard to understand. I thought that I'd forgotten what I needed to know. Sometimes I suspected that maybe I just wasn't smart enough to get it. Then I realized I was in a Tower of Babel situation: I just didn't speak the language of statistics.
Maybe my experience in actually coming to love statistics will resonate with you. Approaching statistics as a kind of conceptual language—rather than a peculiarly ambivalent branch of mathematics—may offer a path to make data analysis more accessible to more people, or at least help us do a better job of communicating with our fellow humans who don’t love statistics.
Stalked by Statistics?
Straight out of college, I was hired as a feature writer for a science magazine. A few years later I was editing the magazine myself. But in some respects I felt like a gazelle glimpsing a lion’s tail in the grass: my environment delivered constant reminders that statistics existed. The science journals were full of them, scientists cited statistics constantly, and I needed to write about them in every article I did.
I realized I needed to confront my dysfunctional relationship with statistics. So as a seasoned, professional editor, filled with trepidation, I enrolled in a basic statistics course. Now I felt like a gazelle trying to tiptoe quietly through the lion’s den. I was terrified but determined to pass, at least. When I received an A, I couldn’t believe it. What had changed?
I realized I no longer saw statistics through a mathematical lens. I had come to recognize statistics as a way to describe, understand, and communicate about the world, just like other languages.
Calculations and Concepts
Once I began thinking of statistics as a language that enriches how we know and experience life, it immediately became less threatening. I enrolled in subsequent statistics courses, and completed a master’s degree in applied statistics almost before I realized it.
Mathematics was a core element of these studies, of course, but I loved that simply solving equations wasn’t the ultimate goal: the meaning of the solution was what counted, and the numbers were just a tool to get there. I had never enjoyed math, but I loved statistics. The difference was that in statistics, doing the math correctly is only the beginning.
The real effort comes next: understanding, interpreting, and communicating the implications of our results, including any conditions, caveats, and shortcomings. Given that statistics deals with probability, every analysis has elements of ambiguity and uncertainty. Our models are never complete. There is always another factor to consider, another way to evaluate and dissect the data, another sample to take, or another method that could be applied. That's not unlike the study of literature, where there is always another lens through which to refract the text, another frame of reference through which it can be interpreted.
Statisticians know the challenges involved in communicating what it is we do. Many people see statistics as inaccessible, esoteric, and intimidating—and in fairness, many statistical concepts are difficult to grasp.
Maybe it’s incumbent on us to be better translators for this strange language we’ve adopted. One of the ways we've tried make data analysis more accessible for more people is by adding the Assistant to Minitab Statistical Software, so people can get their statistical results in plain language.
What else could we, as companies and as individuals, be doing to make more people more comfortable with our data-driven world?