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>"The power of statistics is to tell you when a model (the null hypothesis) is (most likely) inconsistent with the data so that you can confidently rule it out"
by nonbel 10y ago
>"The power of statistics is to tell you when a model (the null hypothesis) is (most likely) inconsistent with the data so that you can confidently rule it out"
If you know whether your model is consistent with the data, you know whether it is inconsistent... I think you are talking about some other issue than I am.
The point about deducing the null hypothesis from your explanatory model is that the null hypothesis is precise. In that case you will get a strong test of the model, and it will get stronger as more data gets collected. Using a default null and and vague alternative is the exact opposite. (check the Meehl 1967 paper I linked earlier in this thread).
- lisper 10y ago> If you know whether your model is consistent with the data, you know whether it is inconsistent... No, that's not true. And in fact I got it wrong earlier when I agreed with you that statistics can tell you when data is consistent with a model. They can't. At best they can tell you whether the data are not inconsistent. That sounds like the same thing, but it isn't. It's like the distinction between "not guilty" and "actually innocent." At best (or at worst depending on how you look at it) a statistical test can tell you, "This theory cannot be confidently ruled out on the basis of that data under the following assumptions..." > I think you are talking about some other issue than I am. That is quite possible. [UPDATE:] BTW, I read the Meehl paper, and I completely agree with what he says. So you and I may be in "violent agreement" here.