“Comment by Peter Norvig on “Being Good at Programming Competitions Correlates Negatively With Being Good on the Job””, 2020-12-15 (; similar):
…A few days ago I watched “How Computers Learn” talk by Peter Norvig. In this talk, Peter talked about how Google did machine learning and at one point he mentioned that at Google they also applied machine learning to hiring. He said that one thing that was surprising to him was that being a winner at programming contests was a negative factor for performing well on the job. Peter added that programming contest winners are used to cranking solutions out fast and that you performed better at the job if you were more reflective and went slowly and made sure things were right…
I regret causing confusion here. It turns out that this correlation was true on the initial small data set, but after gathering more data, the correlation went away. So the real lesson should be: “if you gather data on a lot of low-frequency events, some of them will display a spurious correlation, about which you can make up a story.”
[The null correlation likely reflects the usual attenuation in screening scenarios from power/n of rare traits like programming competition victories, range restriction, and Berkson’s paradox.]