Also this week: why modeling the process that generated the data is the least of your worries, pretty pictures of mammals, and more…
This will blow your mind (unless you’ve heard of it before). Say that you have sample data on three or more variables. They could be anything, they need not be related in any way. You want to estimate the true (population) means of these variables. Your best estimate is the vector of their sample means, right? Wrong. Better estimates can be obtained by shrinking the mean of each variable towards the grand mean of all of them. This is Stein’s Paradox. It’s a famous result in statistics, dating from 1955. It’s totally counterintuitive–until it’s explained properly, and then it makes total sense. And once you get it, you’ll have a much deeper understanding of everything from nonparametric smoothing to empirical Bayes methods. Check out this wonderful, totally non-technical paper on Stein’s Paradox from Brad Efron and Carl Morris. You’ll be glad you did.
Deborah Mayo argues that the replication movement show signs of becoming a bandwagon. As she puts it, “non-significant is the new significant.” A quote to give you the flavor:
The authors seem to think that failing to replicate studies restores credibility, and is indicative of taking a hard-nosed line, getting beyond the questionable significant results that have come in for such a drubbing. It does not. You can do just as questionable a job finding no effect as finding one. What they need to do is offer a stringent critique of the other (and their own) studies. A negative result is not a stringent critique.
Papers that were publicly accused of reporting fraudulent data on the now-defunct website Science Fraud were seven times more likely to be retracted than otherwise-similar papers for which the accusations were privately reported to the relevant authorities. There are various possible explanations, but it sure does look like journal editors often move only in response to negative publicity. Which is unfortunate for various reasons, including the fact that it encourages people to immediately go public with accusations of fraud–accusations that don’t always stand up to scrutiny. But as one of the folks quoted in the linked piece notes, journals need to recognize that a significant fraction of people no longer trust them, and won’t be satisfied by stonewalling.
Via the BBC, a pretty good popular treatment of how human cultural practices have influenced our genetic evolution. Includes discussion of some of the best-studied examples. Good source of examples for undergraduate classes.
Rookie mistakes in statistical analyses of empirical data. Or, “why correctly modeling the data-generating process is the last thing you should be worried about.” From economics, but it applies to ecology too. (ht Worthwhile Canadian Initiative)
And finally, the winning shots from the Mammal Society’s Mammal Photographer of the Year competition, which focuses on British mammals. Dolphin 1, salmon 0. And THE BROWN HARE IS WATCHING YOU.🙂