>"The problem is that a study that's significant at p = 0.05 but not p = 0.01 is worth something to science. It indicates something worth doing several more stu

>"The problem is that a study that's significant at p = 0.05 but not p = 0.01 is worth something to science. It indicates something worth doing several more studies on" I still don't understand this one. The null model is (almost) always false. In this situation a small p-value is only a matter of sample size and measurement precision. How can it possibly indicate you should work more on something? One thing I think may make sense is to get a bunch of p-values and then choose the lowest 10 or whatever to focus on for further work. There is no reason to include an arbitrary cut-off here though.