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By this logic trying to see how likely this was to happen assuming articles were just randomly marked for deletion is a perfectly reasonable test. Maybe I'm mi
by nkurz 9y ago
By this logic trying to see how likely this was to happen assuming articles were just randomly marked for deletion is a perfectly reasonable test.
Maybe I'm misunderstanding Ben's point, but I don't think he's objecting to this as a frequentist. You have correctly calculated that given the small sample size of female candidates, there is about a 10% chance that random deletion of entries would produce a disparity this large.
I think Ben's issue is that since we already know that the deletion process is nonrandom, this tells us nothing we do not already know. The question we're actually interested in is what the factors are that cause deletion requests, not just the ones that correlate with them, and the numerical value you've calculated doesn't give us much insight.
So maybe you could talk more about what you think the calculated p-value actually means, and what we can do with it?
- contravariant 9y agoFrom a Bayesian point of view randomness is always merely a lack of knowledge. In that sense there's no problem with viewing the outcome of a deterministic process as something random. Viewed from that perspective what this p-value means is that merely knowing the proportion of articles that gets marked for deletion is already enough to conclude that it is likely (or at least not impossible) for a group of 21 articles to have at least 2 articles marked for deletion. In that sense the knowledge that of the 21 articles written on women, 2 were marked for deletion doesn't really tell us something that we didn't already know. Of course that doesn't mean we've exhausted all available knowledge, but you're going to have to look further than just how many articles get deleted.