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I know you're probably not implying regression to the mean is causational, but that was my initial reading so I want to clarify for those who may not be familiar with the concept. Regression to the mean is simply that any given datapoint is most likely to be the mean, or close to it. This means that any exceptional data point,up or down, can be expected to be followed up by the mean. The example of this being misinterpreted that I am familiar with is that of a flight instructor's belief on training. When a pilot performed well, they wouldnt comment. If a pilot performed poorly, they would be punished. They believed this was better because when they praised a pilot, they would usually do worse the following run, and when they punish them they do better. This isn't technically wrong, they are just ascribing causation where there is none. I think I saw this example in Signal vs. Noise, but I'm not sure. Basically, regression to the mean isn't a reason to pick someone who did poorly, it's a reason that that person will do no worse or better than they do normally. |
Flip 100 coins. Take the ones that 'failed' (landed tails) and scold them. Flip them again. Half improved! Praise the ones that got heads the first time. Flip them again. Half got worse :(
Clearly, scolding is more effective than praising.