In Sunday Book Review's Up Front: Steven Pinker section of the New York Times, it was interesting to read about Malcom Gladwell's comment on "getting a master's degree in statistics" in order "to break into journalism today". This has been a great year for statistics considering Google's chief economist, Hal Varian, comment earlier this year: “I keep saying that the sexy job in the next 10 years will be statisticians”, and the Wall Street Journal's The Best and Worst Jobs survey which has Mathematician as number 1, and Statistician as number 3.
What really caught my attention in Sunday's Up Front was Prof. Steven Pinker's, who wrote the review on Gladwell's new book "What the Dog Saw", remark when asked "what is the most important scientific concept that lay people fail to understand". He said: “Statistical reasoning. A difficulty in grasping probability underlies fallacies from medical quackery and stock-market scams to misinterpreting sex differences and the theory of evolution.”
I agree with him but I believe that is not only lay people that lack statistical reasoning, but as scientists and engineers we sometimes forget about Statistical Thinking. Statistical Thinking is a philosophy of learning and action that recognizes that:
- All work occurs in a system of interconnected processes,
- Variation exists in all processes, and
- Understanding and reducing variation is key for success
Globalization and a focus on environmental issues is helping us to "think globally", or look at systems rather than individual processes. When it comes to realizing that variation exists in everything we do, we lose sight of it as if we were in a "physics lab where there is no friction". We may believe that if we do things in "exactly" the same way, we'll get the same result. Process engineers know first hand that doing things "exactly" the same way is a challenge because of variation in raw materials, equipment, methods, operators, environmental conditions, etc. They understand the need for operating "on target with minimum variation". Understanding and minimizing variation bring about consistency, more "elbow room" to move within specifications, and makes it possible to achieve six sigma levels of quality.
This understanding of variation is key in other disciplines as well. I am waiting for the day when financial reports do not just compare a given metric with the previous year, but utilize process behavior (control) charts to show the distribution of the metric over time, giving us a picture of its trends, of its variation, helping us not to confuse the signals with the noise.