I’ve been reading Nate Silver’s book, The Signal and the Noise: Why so Many Predictions Fail—But Some Don’t, recently. As someone with a deep formal background in disciplinary statistics and research methods, the subject matter is naturally of interest to me. While the book dips into many specific examples—without ever burying readers deeply in the minutiae of formulas and statistical nomenclature—its points are conceptual in nature. Among these precepts, though by no means the most important, is the notion that the trappings of the modeling so widely used for making predictions—data, algorithms, advancing technology, and so forth—are no substitute for human thought.
When I was in graduate school at the University of Chicago, I had the good fortune to be given the opportunity to assist in the design of my degree program. It was a generous mix of standard classes in statistics as well as some less formulaic offerings in the underpinnings of research methodology, including some independent study that focused on some of the limitations of quantitative and qualitative research in the social sciences. (I’m sure this all sounds like a thrill a minute to most of you, but take my word for it—it was some pretty intriguing stuff.)
Among the more conventional coursework was a two-semester offering in multivariate statistics that was taught by a statistician in the Department of Education. Roughly ¾ of the students were doctoral students in education. The remainder consisted of some grad students in the economics department (and me). The ed students were required to take the courses, and most of them weren’t particularly happy about it. I can’t say that I blamed them, since most were headed for careers in education academe or in secondary school administration and thus regarded statistics as superfluous to their future endeavors. The university required that grad students in ed take statistics because of the huge output in educational research in the United States. Tens of billions of dollars are devoted to research in the field of education every year and the Department of Education felt its students ought to know more than a thing or two about it.
In any event, when we reached the point of the itinerary where we began dealing with tests of statistical significance, a lot of the education students felt that perhaps the effort had been worth it after all. Here was something akin to a magic silver bullet. A test of significance, a lot of these folks decided, was a sure fire way of determining whether effects were “real” or not. Just run the appropriate test on your data and you had a hard and fast answer; your hypothesis could be accepted or rejected and that was that.
Would that it were so.
I’d been around the block with statistics before and I knew better and the professor, of course, cautioned against the sort of attitude that a lot of the students had developed by explaining (stop me if this sounds familiar) that the tests were just a tool and were no substitute for keen thought on the part of the investigator.
I’m sure many of you are asking a pointed question right now: what the hell has any of this got to do with photography? Strictly speaking, the answer is nothing, but humor me for a moment and perhaps you’ll see the analogy.
Many photographers fall into the habit of turning image-making into little more than a rote exercise. This shows up in the form of letting their camera make decisions for them (exposure, picture modes, focus, etc.) as well as falling into the trap of habitual perspectives and other repetitive aesthetic considerations. The very term “point-and-shoot” more or less encourages this kind of behavior. Just whip out your camera and hit the button; no muss, no fuss. The camera is so sophisticated, it saves you the trouble of making all these extraneous decisions.
I’m sure you can see where I’m going with this: the decisions aren’t extraneous; in fact, they’re what the art of photography is all about. Of course, there are types of shots that lend themselves very nicely to this approach—candid pictures of kids and pets, for instance. But for the kind of images that I normally discuss on this blog—nature—using this snapshot approach is selling yourself short.
No matter how advanced the feature set, the camera is still just a tool—a means to an end. A camera’s advanced feature set is no substitute for taking the time to think about what you want to achieve and using the camera to make it happen—even if the best way to achieve what you’re after is to use most or all of those advanced features. And make no mistake—it’s not about the style of camera. You can use the most expensive cameras on the market as glorified point-and-shoots and you can use the least expensive digicams in thoughtful, creative, expressive ways.
Regardless, there’s no shortcut, no technological substitute, for self-application. The art of photography, in the end, is about using cameras, lenses and accessories as a means to express yourself, and self-expression requires self-application. In the end, there’s no substitute for you.