Last May, Dave at Euri.ca took at crack at expanding Gabriel Rossman's excellent post on spurious correlation in data. It's an important read for anyone wondering whether the core hypothesis of the Big Data movement is that every sufficiently large pile of horseshit must have a pony in it somewhere. As O'Reilly's Nat Torkington says, "Anyone who thinks it’s possible to draw truthful conclusions from data analysis without really learning statistics needs to read this."
* If good looks and smarts are distributed normally, and
If good looks and smarts have nothing to do with each other, and
If movie producers want both smarts and looks
Then, by observing employed actors we’ll assume that looks and smarts have a negative correlation
Even though we constructed this experiment with no correlation
Here’s a graph of 250 randomly generated points (with no correlation). With the red circles representing “actors who are smart and good looking enough to get a job (looks+smarts>2), and lighter blue x’s representing “people who wanted to be actors”
Clearly if we only look at actors with jobs, we’ll see a clearly negative correlation between smarts and good looks. In fact, some brilliant actors are less attractive than an average person, and some gorgeous actors are dumber than an average person. Even more interesting though, is that if we try to rule out bias by looking at aspiring but unsuccessful actors as well, we’ll find that they exhibit a similar correlation...
You’re probably polluting your statistics more than you think
(via O'Reilly Radar)
The Scottish sketch comedy series Limmy’s Show! explores that classic riddle in the best way possible. As someone wrote on Tumblr, “This is me during every moment of math class.”
Swirling a ball in a cup gets it spinning in the direction of the swirl, but adding six more starts them swirling in the opposite direction.
It turns out that folding a pizza slice lengthwise to improve its rigidity is a great example of the “Remarkable Theorem” by Gauss. Cliff Stoll explains.
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