E.g. the underlying First Round's analysis likely has no statistical significance. Assuming the power law distribution of outcomes top 5 outcomes will account for 97% of value. So we now have a study with n=5.
To make the point let's apply this to YC's own portfolio. Assuming Dropbox, AirBnb and Stripe represent 75% of its value, we'll learn that YC is incredibly biased against:
* MIT graduates
* brother founders
* founding teams that do not have female founders
* and especially males named Drew
See my post
https://news.ycombinator.com/item?id=10484602
where are distribution-free. So "power law", Gaussian, anything else, doesn't matter.
"C" the applicants you're looking at have roughly
equal distribution of ability.
If we take two dart boards (one for female -, one for male founders) as a visual, where hitting near the bull's eye counts as "startup success".
If we take "C" to be true, then the darts would be thrown at random.
Now we draw a circle around the bull's eye. Anything landing in this circle we fund. If this circle has a smaller radius on the female dartboard, than on the male dartboard, then evidently the smaller female circle will contain more darts closer to the target (better average performance) than the larger radius male circle.
But then we do not even need performance numbers: Smaller radius circles will have less darts in them. Using "C" we only need to know that the male-female accept ratio is not 50%-50% for us to have found a bias.
In short: If you see a roughly equal distribution of ability, and (for simplicity) a roughly equal number of female to male fundraisers, then you should always have a roughly equal distribution of female to male founders in your portfolio, performance be damned.
The technique is still useful for when you do not have these female vs. male accept ratio's, and a VC publishes only success rates, but this information on ratio's is often more public than success rates/estimates.
The issue with founder funding is there are fewer female applicants than male applicants, and the applications aren't published.
I feel that a different number of darts is salvageable for this logic, but having thought about this blog post some more, I feel bias is inherently non-compute-able. Our decision on how to compute influences our results.
What PG did for me was show that there is no Pascal's wager in statistics: All outcomes/data/measurements/views are equally likely. The view that the female variable alone is able to divide skill/start-up success is weak. The assumption of non-uniform points is weak. The assumption of no variance/unequal rankings is weak. The assumption that a non-random sample is significant is weak. The assumption that VC's are unbiased in their selection procedure is weak. The assumption that nature/environment favors skilled women is weak. The assumption that decisions of who to fund does not influence future applicants. The assumption that women are still selected for capability is weak. The assumption that women ignore nature/environment and keep focusing on start-up capability is weak. It is much more likely that any other thing happens. PG's alternative is certainly a sane one, but one of many.
Perhaps women perform better because, while VC offers the same chance to men and women, they are better at picking capable women than capable men. Bias in favor of capable women.
Perhaps women perform better because, they are naturally better than men.
Perhaps women perform better because, VC is biased against women, and only the strong survive.
Perhaps women perform better because, affirmative actions to remove the inequality in performance (perceived bias) actually increased our objective bias.
Perhaps women perform better because, VC is bad at picking capable women, so they pick incapable women, of which there happen to be a lot more.
Perhaps women perform better because, now the smart and capable women start to act like the mediocre ones (bad funding decisions influence actors looking for reward)
Perhaps women perform better because, nature is "biased" against older risk-averse, but available, men and, older, unavailable women who have children, and nature favors both young males (who have to compete with the old males) and females (who compete only among themselves).
Perhaps women perform better because, our sampling method was biased.
Perhaps women perform better because, our measurements were 5 years old and we are seeing an old static state of a highly complex dynamic system.
Perhaps women perform better because, they are more variant. The good ones are really good and the bad ones are really bad, making it easier on VC's to pick the cream of the crop.
All I know is how little I know. That (algorithmic) bias is an important subject, worth thinking about, and that we need very smart people working on this subject. I would never have gotten away with upvotes on my posts in this thread if the subject was cryptography. I clearly know very little about both subjects (and only now I know that, which I hope is at least a start).
PG showed that we (I), perhaps too easily, go along with the status quo: Our measurements are all correct, our conclusions are all correct. While, if you think about it.. objectively I agree that women and men are equal in capability. If you believe this to be so, then you may have a selection bias, if you observe that men and women perform differently.
I think the least all views could do is to make sure the environment for female founders to flourish is healthy and in line with skill/capability. Then let nature do its thing.
P.S.: If we know that females actually perform better than males, what is the ethical thing to do? Fund even more female founders and make it harder for men? It would make you richer. Affirmative action? It would not remove a bias, it would introduce one.
For example, if a VC funds all male founders but flips a coin to decide whether to fund each female founder, the test would fail to detect overwhelming bias.
Obviously that specific scenario is not realistic, but I believe something like this is plausible enough: A VC funds all male founders who are considered promising, and all female founders who are considered promising AND went to school with one of the partners.
And it's not hard to imagine a plausible scenario in which the test would give false positives rather than false negatives.
This essay was based on these two lines from here: http://10years.firstround.com/#one
> That’s why were so excited to learn that our investments in companies with at least one female founder were meaningfully outperforming our investments in all-male teams. Indeed, companies with a female founder performed 63% better than our investments with all-male founding teams
The comparison is not clear, but is not women versus men, but between companies with X number of males plus at least one female founder, versus those with zero female founders and Y male founders.
If we skip a step and take this fact as having some predictive value, it could be lots of things, including off-the-top-of-my-head:
1. Bias against women - which extends to teams that include men, e.g. the bias against woman exists in the presence of male co-founders.
2. That the personality traits shared by groups where women co-found startups with men are positively correlated with success. It is quite possible that these groups have much better EQ, while still retaining the IQ to impress the required amount to be selected.
3. That startups with at least one female select, and I am using this term in a very stereotyped way, "not-white-male" startups. Many Unicorn startups, from Atlasssian to Dropbox, specialise in problems faced by, again for wont of a better term, "white males". Given the mantra of solving problems we have ourselves, it is possible that mixed groups choose less male subjects. As men have been the founders of the majority of startups to date, there must be a plethora of such startup ideas left untouched. One example is DIAJENG LESTARI who started https://hijup.com/, described as "A pioneering Muslim Fashion store. We sell fashion apparel especially for Muslim women ranging from clothing, hijab/headscarf, accessories, and more." Little to no chance the archetypal "white male hacker founder" has that idea.
That's three ideas off the bat, only one of which is bias. It could still be bias, but I feel that points 2 & 3 are at least good candidates for exploration. Personally, I think there must be a lot of low hanging fruit in ideas not aimed at men, and female founders seem ideally poised to have those ideas.
For details, see my post