Who Were the Sea Peoples?

Mean to people who don't understand statistics but blab on about it all the time. For instance, Steve Sailer apparently does not comprehend "regression to the mean," and treats it as a cause of future events, rather than a tautology: "Still, Hillary is not a good candidate. Regression to the mean suggests she probably won’t have too many days worse than her Labor Day, but Hillary is clearly Trump’s best hope of being elected." So, once again: "regression to the mean" is a tautology . Tautologies can be useful, but they do not cause events in the real world. The truth of the statement "All bachelors are unmarried" does not mean that it is unlikely that John, a bachelor, will not get married next year! (It may be unlikely or not: the point is that this tautology has nothing to do with determining that likelihood.) Real world events "regress to the mean" because, if they don't, what was once the mean will cease to ...

Before Babe Ruth, the highest number of home runs hit by any major league player in a season was 16. When Ruth hit 29 home runs in 1919, it would have been quite sensible to predict that he would regress to the mean and hit fewer the next season. But if he had, "regression to the mean" would not have explained how many home runs he hit. That could only be explained by analyzing each at bat that season, and understanding why Ruth was or was not able to drive a ball out of the park during that at bat. (Of course, we could have a more general explanation as well, e.g., "Ruth was ill," or "Ruth was going blind.") In fact, the next season Ruth hit 54 home runs. Wow, now he really "ought" to have regressed to the mean, and dropped way, way down. Instead, the following season he hit 59 home runs. What was happening was the Ruth was ushering in a new era of home run hitting, such that 16 home runs (the previous all-time high before Ruth) would co...

Daniel Kahneman treats regression to the mean as a form of explanation. (See Thinking, Fast and Slow , pp. 178-183.) He also says that when we see regression to the mean, what we are seeing has "does not have a causal explanation" (p. 178). I say both these contentions are nonsense. (Once again, let me put in my usual caveat: I greatly admire Kahneman's work as an experimental psychologist . But here he is doing philosophy of science: he has left his area of expertise and is forwarding ideas which [I contend] he cannot defend,  and which certainly cannot be decided by any experiment. And I will also note that citing regression to the mean is fine as a way of justifying a prediction .) Why is regression to the mean not an explanation of any empirical fact? Because it is a tautology, and tautologies never explain empirical phenomena. In particular, in this case, regression to the mean always holds, because something won't be the mean unless it is regressed to . Let...