| >> I made no such assumption. >>You literally wrote "all else equal" in your comment. That's different from the assumption that all competitors are equally likely to win. >You're assuming speeds are normally distributed No. Any distribution will work. >Where is this data that you're citing here? I didn't say anything about data. I said your argument has a blatant mathematical flaw. You said "why would it matter" in response to "if 90% of the competitors [are men]". It absolutely matters. Even if you do turn out to be correct about women being worse at this sport, you are only right in the broken clock sense. >Put another way, if Michael Phelps is racing he's going to win. You can only win by beating him, the rest of the field doesn't matter. The people who show up to the race are coming out of some distribution. Michael Phelps isn't showing up to every race. The probability that you win the race comes down to how fast you are vs. the max of n samples from the distribution of runners. The list of the winner of some annual marathon is a really shitty piece of evidence. Out of all the racers and times taken, it gives us data on exactly one of them. It is especially useless to try and breakdown running ability by demographic because it doesn't even tell us how much data we have on each demographic of interest. If you don't see why just citing the list of marathon winners fails to reject the hypothesis that women and men are about equal at ultra-marathons, then you don't understand what makes for a good data-supported argument. |
If you're a data nerd and a runner armed with this knowledge, it will have occurred to you to wonder if distance (in time) from the winner is correlated with gender and field size. It is not. Thus, you're proposing that we "normalize" for something which is shown to not have an effect on who wins a race.