In those days, computer centers were full of noisy, heat-producing behemoths. Students, grad students, post-docs, and professors shuffled about with huge boxes filled with cards. The greatest calamity was when a box fell spewing its contents on the floor. Carefully picking up and reordering the punched cards was an awful job. One card out of order would get the run rejected.
I had a team of punchers and card verifiers working for me on the several projects I had running. Most were very straight forward projects where simple statistical measures were applied to data sets to create tables for a researcher’s thesis. How many of this, how many of that, here’s the beautiful bell curve. For me, it was extra bread and butter while in grad school. As an undergrad, I had taken, and done well in, several computer science courses. That was several more than anyone else in my department at that time. This background made me the go-to guy if you needed computer work. I worked hard to keep it simple because I knew the limits of my limited expertise, and because I, too, had been seduced by how bad data sets could become manipulated into bad statistics.
I recognized Professor Wingate’s symptoms. Reading the manual on the statistical package, Wingate would almost froth at the mouth eyes glazed over and ask me to run a particular procedure. Having read a chapter ahead of him on the manual, I’d suggest that this procedure didn’t apply to his limited data set. He said, “run it.”
The ladies who punched cards and verified were happy to get the continual work. They were independent contractors with no stake in how useful the data was or in the results. But even they knew it was garbage in garbage out on this job. Eleanor, the woman who verified that the punched cards were accurate, advised me to sit back and enjoy the ride. As she told me, “this isn’t Wingate’s first rodeo.” The good doctor was a regular at the computer center, and I only the latest data jockey.
I began to feel guilty about taking his money. I found out that he was a regular at several floating poker games. And, was no stranger to the mind-altering substances that were readily available off-campus.
Being naive, I approached Wingate. I’m no stranger to the offside of life. I’ve been shot at, been on frolicking detours through some of the more salubrious neighborhoods of Hell, and seen many odd places and things. But Wingate was different. I explained to him that I’d only seen the particular glazed look on slots habitues in Vegas. His reply- “which casino, I’ve done then all.” I now knew that I had stepped into a deep pool. We spent an hour trading shots and talking about adventures on the risky side. It was not a conversation I had ever expected to have with a tenured professor at an Ivy League University.
I had spent several years on the wild side, learning about life. But Wingate’s take on similar frolics was different. He was interested in creating reality, not experiencing it. The commonality of our experiences did not unite us; it divided us. From his bookshelf, he drew out a fat volume. Handing it to me, he told me that people process the same information in different ways, and that influenced the reality they perceived. “Shelby Foote says it best-” pointing out a passage to me I read:
“People make a grievous error thinking that a list of facts is the truth. Facts are just the bare bones out of which truth is made.”
I looked at him and realized that I couldn’t continue taking his money, running his bad data sets, and listening to his philosophy over bourbon shots. I had been over the top of reality a few times in the previous decade and wasn’t sure that my grip on reality could take it if Wingate were an ongoing influence. Or, worse if Wingate was correct.
I looked at him and made one final attempt to haul him in from the thin ice. “Professor, if the facts you input are garbage, it’s all garbage output.”
“It’s all a matter of perspective, Wes, just a matter of perspective.”
2 Replies to “GIGO”
And GIGO still goes on with many clinical trials of the big pharma companies; they set what their data should show, even before the sample selection begins.
What was the term used years ago? Lies, lies, and damned statistics.
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