Statistics are like bikinis. What they reveal is suggestive, but what they conceal is vital. ~Aaron Levenstein
I’ve been reading more of the Scott Walker recall election coverage, and was struck by the frequent references to Walker being “the first governor to survive a recall election”. Of course this made me curious how many governor’s had been recalled. I remembered the California governor a few years back, so I had been imagining it would be at least a dozen or so.
I had to laugh at my own sampling bias. My assumptions were pretty understandable….I’ve been of voting age since 1999, and in that time this has happened twice. Therefore it was reasonable to assume this happened at least occasionally. I figured about once every 10 years, which would be 23 or 24 in American history. I was pretty sure not every state had a recall option, so I halved it. 12 felt good.
This is the problem when data leaves out key points….it relies on our own assumptions to fill in the details. Engineers are normally trained to get explicit with their assumptions when estimating, as evidenced by the famous Fermi problem. However, even the most carefully thought through assumptions are still guesses.
That’s why it’s important to remember the quote above: what you’re shown is important, but it’s not half as interesting as what’s hidden.