[compbio]

If feel the addition:

    "C" the applicants you're looking at have roughly 
    equal distribution of ability.
	

makes the reasoning more tautological/weak.

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.

[mdonahoe]

Doesn't this logic assume that there are the same number of darts thrown total at both boards?

The issue with founder funding is there are fewer female applicants than male applicants, and the applications aren't published.

[compbio]

I am sorry for all posts in this thread (including this one). Imagine being PG and reading 200+ negative replies to a blog post you did. I could have reasoned in line with Graham and learned a lot more than when resisting and attacking a viewpoint different than yours.

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.