Out of sample tests

I am a big fan of so called "out of sample" tests. When researchers find some anomaly within a data set and then others test for the presence in the same data set, we really do not learn much if they find the same thing. But when a new data set is used for the test, we have a much better understanding of the possible anomaly.In the current JFQA there is just such an article by Richard Grossman