A bit more on Dragons and probabilities etc

I had some weird conversations yesterday about Dragon Award stats. One was a brilliant take down of my figure that 10 men out of 10 had won Dragon Awards from 2016 in the two headline categories. Aha! Four years and two categories is only EIGHT! Yeah but it really is ten men. James S A Corey is actually two people and, even harder to believe, apparently John Ringo and Larry Correia are different. Mind you…if I only count Larry Correia once (because he is the same person whichever year he’s in) then it is back to 8 again…You’ll note that however we count it the answer comes out the same: 100% have gone to men in the two headline categories.

The discussion does raise a relevant point about why statistics is hard. Even a basic stat like a count of how many out of how many requires engaging your brain and thinking carefully about what you are counting. It was suggested that I should have said 10 men out of 8 awards…which I guess makes it clearer what was being counted but is horrible arithmetically. It looks like “10 out of 8” i.e. 125% which is nonsense because we are diving two different things and creating a derived unit of men per awards.

I’ll point people back to this post https://camestrosfelapton.wordpress.com/2019/08/10/dragon-award-by-gender/ and this post https://camestrosfelapton.wordpress.com/2019/08/11/more-dragon-stats/ where I talked in more detail about what I counted and how.

To round off that previous gender post here is an equivalent graph of winners by gender in the book category:

Like the graph in the previous post of finalists, I’m using counts by gender which reduces the gender disparity by only counting two joint authors of the same gender as 1 but two joint authors of different genders as 1 each per gender. Same caveats about gender as a binary classification apply as with the earlier post.

Worst year was 2017 which was also peak Rabid Puppy influence.

A couple of conceptual questions have come up that are related. I was asked elsewhere what the chance was of so many authors on Brad’s list winning. A different question with the same kind of issue was asked by James Pyles – basically what was the chance of N.K.Jemisin winning a Hugo three times in a row.

Both questions aren’t something that can easily be answered and they sort of miss the point of the kind of comparisons against chance you might do with gender. With the Brad list these were people who were plausible winners, the outcome wasn’t surprising. There’s no expectation that the result of an award is a random event when looking at individuals – the same is true with Jemisin. We could say, well there’s 7 billion people on earth and one winner so the chance is 1/7 billion and the chance of winning three times is (1/7 billion)^3 and then concluding that everything is impossible but the comparison is silly.

Comparing with chance is there to test a kind of hypothesis: specifically whether the result is plausibly the result of chance. If the probability is tiny then we can reject that it happened by chance. We already know that somebody winning a Dragon or a Hugo isn’t by chance because names aren’t picked out of a hat.

So why compare gender of winners to chance events if we know winning isn’t a chance event? Good question. Because, we are testing another level of hypothesis. With gender, the hypothesis could be stated as ‘gender is an irrelevant variable with regard to winning award X’.

Consider this. Imagine if all Dragon (or Hugo) winners were born on a Tuesday. That would be remarkable. Day of the week surely isn’t connected to whether you win an award or not! We might reasonably expect only one-seventh of winners to be born on a Tuesday. We might do extra research to see if across all people if day-of-the-week is evenly distributed. We might fine tune that further and consider only English speakers or only Americans etc. The point being that if day-of-the-week departed from chance then we would reject that day-of-the-week is irrelevant.

If we did find that, it wouldn’t tell us why or how day-of-the-week was relevant. One response I’ve seen to producing gender stats is people saying that they don’t pay attention to author’s gender when voting. Even if we ignore subconscious influences and take that at face value, all that does is remove one possible cause of a gender disparity, it doesn’t make the gender disparity go away.

Another response is that looking at gender stats is ‘politics’. Well, yes, it is but it is relevant even if we otherwise lived in a gender neutral utopia. Again, imagine if Tuesday-born people won far more sci-fi awards than other people — that would be fascinating even though we don’t live in a world of Tuesday-privilege.