When I first started working for Big Aerospace, I was shocked at the standard approach to presenting experimental or test results. A single data point at a particular frequency or temperature was taken as gospel. Every distribution was normal. Steps were to taken to avoid multiple measurements. And I have yet to see, in my four and a half years of being surrounded by rocket scientist types, a plot, graph, or Powerpoint presentation with error bars. I find that hilarious, since it seems like every single engineer I meet has one color of belt or another in Six Sigma-jitsu.
A mathematician friend of mine from undergrad wound up applying to medical school a couple of years ago and is about halfway through his first year at a school just down the street from me (probably my first choice, too). We hooked up a couple of weeks ago for lunch and he talked about how a lot of medical research takes, at best, a shoddy approach to statistics, although his description was a bit more florid. I had to suppress a small laugh when I told him the same sort of thing happens all over the place in engineering companies too. I have to admit that a lot of scientific research gets a pass on statistical and logical rigor. It shouldn’t happen. The scientific community really should police itself, but it doesn’t. Too often, the purported research from people like Hwang Woo-Suk or Andrew Wakefield, gets through the filter for various reasons. I’m getting a little bit off-track here and I don’t want this to turn into a rant about the failures by the gatekeepers of scientific fact – and I have to get back to work.
Inspired by this post by Petulant Skeptic on the perils of p-value, I decided to take it upon myself to start teaching myself statistics at work. This is partially because I don’t want to be a moron when it comes to statistics as a physician someday and also because I’m working on a couple of projects at work which really do require a statistical approach. Sadly, everyone I’ve turned to at my company for a basic discussion on statistics, particularly measurement and experimental uncertainty, hasn’t really had a clue what I was asking about or why it was important. That, I suppose, brings me to the real point of my post, which was to share a useful link on uncertainty which I ran across provided by NIST. I found it a useful read and figured others might be interested.