6 ms·

Just playing devil's advocate here, but with the numbers given in the essay (10 out of 250 male entries tagged for deletion vs 2 out of 12 female entries) I got

Just playing devil's advocate here, but with the numbers given in the essay (10 out of 250 male entries tagged for deletion vs 2 out of 12 female entries) I got a p-value of approximately 10% [1]. By most criteria this wouldn't qualify as statistically significant. This doesn't immediately disqualify the entire essay, but the claim that women are 'over four times more likely to be deleted' is somewhat dubious. [1]: http://www.wolframalpha.com/input/?i=1+-+(1+-+12%2F262)%5E12+-12+(12%2F262)(1+-+12%2F262)%5E11 http://www.wolframalpha.com/input/?i=1+-+(1+-+12%2F262)%5E12...

stareatgoats 9y ago
There are many things that are not statistically significant, but still interesting when it seems likely* that a larger sample would give a similar result. Only 17.47% (as of 26 February 2018) of the English Wikipedia's biographies are about women: https://en.wikipedia.org/wiki/Wikipedia:WikiProject_Women_in_Red https://en.wikipedia.org/wiki/Wikipedia:WikiProject_Women_in... EDIT: I rechecked the quote from the article about the likelihood of deletion and found it to be "in this cohort of Erdős Ones, the pages for women were over four times more likely than men’s to be nominated for speedy deletion." (my italics). FYI.
- dragonwriter 9y ago
  > Only 17.47% (as of 26 February 2018) of the English Wikipedia's biographies are about women: Wikipedia being a tertiary source may, through unbiased processes, simply be reflecting bias in the secondary sources. Biographies of pre-modern women are far less common than of their male contemporaries, IME; that's less true of my modern and especially extremely current figures, but Wikipedia isn't exclusively focused on current figures.
  - posterboy 9y ago
    There is no such thing as a tertiary source. Either you have a primary source, or you have hear say.
    - dragonwriter 9y ago
      You may not like secondary and tertiary sources, but they absolutely are well-defined categories, and Wikipedia's overtly declared mission is to be a tertiary source, which why original research and most direct citation of primary, rather than secondary, sources is restricted by policy.
- dahdum 9y ago
  That % isn't really surprising considering the patriarchal history of the world. You couldn't possibly get to 50% unless you over-represented women in the near present or adjusted notability standards. However if it were 17.47% for current times, that would be a significant bias that needs to be addressed. It still might not hit 50/50 given reduced opportunities for women still today, but it should be moving ever closer.
  - shele 9y ago
    The fraction of women with Erdos number one is already very small, no reason to assume that they are less important than corresponding man with the same Erdos number.
- deleted 9y ago
  [deleted]
dahdum 9y ago
I find it somehow fitting that the women in question would be very likely to tear apart this claim in the same way.
brudgers 9y ago
The conclusion is based on consideration of the entire population of biographical Wikipedia entries with an Erdos Number of One. It is not statistical except in some lay sense that any reference to percentages makes something statistical. Talking about p-values in contexts where one has all the facts and the additional fact that one has all the facts suggests a gross misunderstanding of statistical concepts, their utilities, and the circumstances in which they apply.
- contravariant 9y ago
  I know I'm leaning more towards Bayesian than frequentist, but I vehemently disagree with the notion that applying statistics in this case is meaningless. First of all I at least don't have all the information. If I had it would indeed be meaningless to infer the reason behind the deletion attempts, as I'd know already, but the fact remains that I don't. 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. Second of all, how exactly is this situation different from tossing a coin several times? If I toss a coin 50 times, record the results, and then melt down the coin, surely it'd still be valid to conclude that it was biased if it came up heads 49 times out of 50? Or conversely that it wasn't biased if it came up tails 26 times out of 50? Arguing that statistics only applies to experiments that can be repeated suggests a gross misunderstanding of statistical concepts, their utility and the circumstances in which they apply.
  - 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?
deleted 9y ago
[deleted]
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
    - 9y ago

stareatgoats 9y ago

There are many things that are not statistically significant, but still interesting when it seems likely* that a larger sample would give a similar result. Only 17.47% (as of 26 February 2018) of the English Wikipedia's biographies are about women: https://en.wikipedia.org/wiki/Wikipedia:WikiProject_Women_in_Red https://en.wikipedia.org/wiki/Wikipedia:WikiProject_Women_in... EDIT: I rechecked the quote from the article about the likelihood of deletion and found it to be "in this cohort of Erdős Ones, the pages for women were over four times more likely than men’s to be nominated for speedy deletion." (my italics). FYI.

dragonwriter 9y ago
> Only 17.47% (as of 26 February 2018) of the English Wikipedia's biographies are about women: Wikipedia being a tertiary source may, through unbiased processes, simply be reflecting bias in the secondary sources. Biographies of pre-modern women are far less common than of their male contemporaries, IME; that's less true of my modern and especially extremely current figures, but Wikipedia isn't exclusively focused on current figures.
- posterboy 9y ago
  There is no such thing as a tertiary source. Either you have a primary source, or you have hear say.
  - dragonwriter 9y ago
    You may not like secondary and tertiary sources, but they absolutely are well-defined categories, and Wikipedia's overtly declared mission is to be a tertiary source, which why original research and most direct citation of primary, rather than secondary, sources is restricted by policy.
dahdum 9y ago
That % isn't really surprising considering the patriarchal history of the world. You couldn't possibly get to 50% unless you over-represented women in the near present or adjusted notability standards. However if it were 17.47% for current times, that would be a significant bias that needs to be addressed. It still might not hit 50/50 given reduced opportunities for women still today, but it should be moving ever closer.
- shele 9y ago
  The fraction of women with Erdos number one is already very small, no reason to assume that they are less important than corresponding man with the same Erdos number.
deleted 9y ago
[deleted]

dahdum 9y ago

I find it somehow fitting that the women in question would be very likely to tear apart this claim in the same way.

brudgers 9y ago

The conclusion is based on consideration of the entire population of biographical Wikipedia entries with an Erdos Number of One. It is not statistical except in some lay sense that any reference to percentages makes something statistical. Talking about p-values in contexts where one has all the facts and the additional fact that one has all the facts suggests a gross misunderstanding of statistical concepts, their utilities, and the circumstances in which they apply.

contravariant 9y ago
I know I'm leaning more towards Bayesian than frequentist, but I vehemently disagree with the notion that applying statistics in this case is meaningless. First of all I at least don't have all the information. If I had it would indeed be meaningless to infer the reason behind the deletion attempts, as I'd know already, but the fact remains that I don't. 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. Second of all, how exactly is this situation different from tossing a coin several times? If I toss a coin 50 times, record the results, and then melt down the coin, surely it'd still be valid to conclude that it was biased if it came up heads 49 times out of 50? Or conversely that it wasn't biased if it came up tails 26 times out of 50? Arguing that statistics only applies to experiments that can be repeated suggests a gross misunderstanding of statistical concepts, their utility and the circumstances in which they apply.
- 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?

deleted 9y ago

[deleted]

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
  - 9y ago