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OT: How did you compute this p-value directly? I have never seen this approach and would be really grateful if you could point me towards a source for this appr

by c06n 9y ago

OT: How did you compute this p-value directly? I have never seen this approach and would be really grateful if you could point me towards a source for this approach.

contravariant 9y ago
I just used the binomial distribution with n=12, p=12/262 (p = number of articles with deletion attempts / total number of articles). Then I just calculated the probabilities of 0 and 1 deletions and subtracted those from 1 to get the probability of 2 or more deletion attempts.
- c06n 9y ago
  great, thanks!
- pred_ 9y ago
  Although it will give you more or less reasonable results, I don't see a null hypothesis for which this approach does not ultimately become flawed: Assuming that you are testing the hypothesis that the two proportions agree, then, for instance, using a fixed binomial parameter of 12/262 becomes wrong for any number of deletion attempts different from 2; that is, part of the assumed model includes the outcomes you are testing: The case "3 deletions", which is part of the "2 or more extreme" probability you calculate, would have led to a different value of $p$. Various statistical tests exist for taking this into account. With Fisher's exact test [0], we find a $p$-value close to yours: In [6]: from scipy.stats import fisher_exact, binom In [7]: binom(12, 12/262).sf(1) Out[7]: 0.10211478280975775 In [8]: fisher_exact([[2, 10], [10, 240]])[1] Out[8]: 0.098322319292822702 However, had we not had such a large male sample size, the values might differ greatly; say, for instance, that we had had 3 deletion attempts out of 33 articles on males instead. Then we find In [12]: binom(12, 5/45).sf(1) Out[12]: 0.39171131326925679 In [13]: fisher_exact([[2, 10], [3, 30]])[1] Out[13]: 0.59808767522891315 [0]: https://en.wikipedia.org/wiki/Fisher's_exact_test https://en.wikipedia.org/wiki/Fisher's_exact_test
  - contravariant 9y ago
    That's fair, although I believe my method is justified precisely because there was such a large male sample size. I suppose I could have made a more conservative estimate by not using the female samples in the calculation of 'p'. I also don't quite like the way Fisher's test fixes the total number of deleted articles, but I guess a more sophisticated test assuming only the total number of female and male articles to be fixed could get a bit complicated.

by c06n 9y ago

OT: How did you compute this p-value directly? I have never seen this approach and would be really grateful if you could point me towards a source for this approach.

contravariant 9y ago

I just used the binomial distribution with n=12, p=12/262 (p = number of articles with deletion attempts / total number of articles). Then I just calculated the probabilities of 0 and 1 deletions and subtracted those from 1 to get the probability of 2 or more deletion attempts.

c06n 9y ago
great, thanks!
pred_ 9y ago
Although it will give you more or less reasonable results, I don't see a null hypothesis for which this approach does not ultimately become flawed: Assuming that you are testing the hypothesis that the two proportions agree, then, for instance, using a fixed binomial parameter of 12/262 becomes wrong for any number of deletion attempts different from 2; that is, part of the assumed model includes the outcomes you are testing: The case "3 deletions", which is part of the "2 or more extreme" probability you calculate, would have led to a different value of $p$. Various statistical tests exist for taking this into account. With Fisher's exact test [0], we find a $p$-value close to yours: In [6]: from scipy.stats import fisher_exact, binom In [7]: binom(12, 12/262).sf(1) Out[7]: 0.10211478280975775 In [8]: fisher_exact([[2, 10], [10, 240]])[1] Out[8]: 0.098322319292822702 However, had we not had such a large male sample size, the values might differ greatly; say, for instance, that we had had 3 deletion attempts out of 33 articles on males instead. Then we find In [12]: binom(12, 5/45).sf(1) Out[12]: 0.39171131326925679 In [13]: fisher_exact([[2, 10], [3, 30]])[1] Out[13]: 0.59808767522891315 [0]: https://en.wikipedia.org/wiki/Fisher's_exact_test https://en.wikipedia.org/wiki/Fisher's_exact_test
- contravariant 9y ago
  That's fair, although I believe my method is justified precisely because there was such a large male sample size. I suppose I could have made a more conservative estimate by not using the female samples in the calculation of 'p'. I also don't quite like the way Fisher's test fixes the total number of deleted articles, but I guess a more sophisticated test assuming only the total number of female and male articles to be fixed could get a bit complicated.