I'm sure there's just as many well spoken, even-keeled men as there are women, and perhaps women do come out of this test better than men. Maybe because they have to work harder to overcome discrimination, maybe because they charm their way into the mainly male start-up work-force.
Again, it doesn't matter. Don't go out looking for particular genders, because all you're doing is limiting yourself.
tldr: tldr.
And then, just for good measure, the good old "she must've slept her way up the ladder" trope.
No, I can assure you, we (for the large majority, at the very least) have not "charmed our way into the work force". We've worked pretty damn hard to get there. We put up with a lot of bullshit, and yes, we (on average) put up with more bullshit than the average male dev.
1) I said that I don't care whether someone's a woman (aesthetically a more pleasing option to me than a man) or a man, as long as they get the job done.
2) I said that whether someone got somewhere by charm or by working hard, I don't give a rats bottom. As long as they get the job done.
Basically, I'm making your case for you and you're still picking a fight?
Would you rather I said "I'd hire a woman because this article says they're better"?
Screw that. I'd hire whomever I think would get the job done. Period.
I hate discrimination. Either way.
Because he cares about it. What's YOUR problem?
My approach in these matters is Bayesian: Do having female, black, Asian, Indian, etc. executives drive start-up success? The a priori probability I assign to this statement is 0.5, i.e. may or may not. I also employ the My Human Law of Large Numbers, i.e. any "large enough" human population (i) has a Gaussian distribution of any cognitive skill and (ii) the parameters of this distribution is pretty much independent of the particular population sample. I don't have solid proof of this principle and in certain subdomains it may be wrong (e.g. the great cognitive differences between men and women debate, etc.) but I doubt that population differences would be significant.
Now, armed with the simple Bayesian approach and the MHLLN, we can see that most of these articles are BS. The evidence to move the a priori value of 0.5 up or down should be substantial, e.g "extraordinary claims require extraordinary proof". I would be extremely surprised if any gender, racial, etc. factor would derive success of any size company.
Since the above analysis is rather trivial, one then has to ask why these things continue to be written. I think the motivation is usually benign: One sees the dearth of women in startups and wants to show that "it's a good thing". This approach, however, is misguided in that, by making silly arguments or sub-par statistical analysis, it hurts the cause due to the "the lady doth protest too much" effect: many people politely nod, but see through your sloppiness and internally become convinced of just the opposite cause (especially if they are inclined to do so, i.e. if the prior was less than 0.5).
A quote I like a lot is "To be ideological is to preconceive reality." These authors, rather than being objective, have already decided what their results will be and are just filling up the blanks.
First of all 50/50 "better", "equal" is not a valid Bayesian prior. Here is a simple litmus test to demonstrate that. If you have a valid prior, then you can assign an actual probability to particular predictions. The ability to do that is a prerequisite of Bayes' theorem. If you can't generate probabilities, you don't have a prior. Suppose you were shown a random startup with women on board. What would you predict their odds of success to be? You can't give me a figure? Then you didn't have a valid prior!
Here is an actual prior that matches the "50% same or better" description that you gave: We give 50% chance to the theory, "Startups with or without early women have a 10% success rate." And 50% chance to the theory, "Startups with no early women succeed 10% of the time, with succeed 20% of the time." I am not saying that this is a reasonable prior, just a valid one. Though that said, a 2 to 1 advantage for having women on board is roughly in line with the figures in the (admittedly flawed) dataset.
With this prior, what happens if we look at a startup which had early women? Well we assign 50% probability to the theory that there is a 10% success rate, and 50% to the theory that there is a 20% success rate, so we calculate 15% odds of it succeeding. We can calculate probabilities of protection. Litmus test passed.
Now what happens if that startup succeeds? Well from Bayes' theorem, we now would give a 2/3 chance to the theory in which women help and 1/3 to the theory that they do not.
And if it fails? There we move the needle rather less since both theories predict high probability of failure. In fact we'd be giving the women help theory a weight of .4/.85 which is around 47% - so only a 3% shift in our opinions.
Notice something? I came up with a concrete prior that fit your description. And I found that every single data point makes a noticeable shift in the posterior opinions that should be held. This is the exact opposite of your hand waving claim that extraordinary proof is required.
Before you next try to use Bayesian analysis to make your claims seem authoritative, please learn something about the subject. A starting exercise might be to figure out what kind of valid priors actually would result in your extraordinary claims require extraordinary evidence hypothesis.
The 0.5 prior thing was an irrelevant use of the principle of indifference. What I really had in mind was a situation with the null hypothesis that having early women on board has no effect on the success rate of a startup whereas H_1 would be that they do have an effect. However, from my description I think what came out was a prior of the kind P(success | women).
Without using any terminology, intuitively the point I was trying to make (ineptly, as you point out) was this: the likelihood that I assign to the statement that "having women early in a startup increases its succeed rate" is very low, I need to see many cases, form startups working on diverse areas for me to update my likelihood value for this. Why? Because I don't think that a subset of population selected with no clear connection to success will affect the success of a startup. Clearly, if the selection has some obvious connection, e.g. coming from a highly educated family, being good in programming, etc. then it will affect success. It's just not clear to me how being a female or black or gay or Indian, etc. has such a connection. I may, of course, be wrong.
And what about the irony of me calling the kettle black: I don't hold my HN posts to the same standard as research reports from a major company.
Gender has almost nothing to do with it. Your experiences are purely anecdotal.
Period.
Who hires sex/gender? Big companies and organizations. Startups hire specific people who can do specific things, and, quite literally, can't afford to give much of a shit about this stuff.
Do male executives drive start-up success? No.
Do smart people drive start-up success? No.
Do great coders drive start-up success? No.
Do great salesman drive start-up success? No.
Does great funding drive start-up success? No.
Any or all of the above could in certain situations, but not all situations.
If you could mathematically model startup success, you'd probably end up with something that looks and feels a lot like the Google Search ranking algorithm. You'd have possibly hundreds of factors you might tweak up or down over time to try to best fit the data set, but ultimately it's not even close to a guarantee of relevance and it would require some hand tweaking of results at times to be "correct".
If someone could reliably define that you need so much of X, Y, Z factors to be the next Facebook, Google, Dropbox, or even Instagram, they would build a factory to build companies that print money. So far no company has truly built that yet.
Pretty much YC in a nutshell.
I leave you with this thought - diversity, in all its forms, is a strength in business and in life.
Hormonal differences have a large impact on what traits are most commonly found in the "average" woman (or man, depending on what you're looking for), at the very least in regards to increased chance of risk-taking. I think this is pretty firmly established in scientific literature by now. However, any specific person may be above or below another of the opposite sex in any particular category, since the variables are so numerous. Making hiring decisions on the basis of sex would be rather stupid. "You should hire females because we'd like to have more females around" is just as sexist as "you should hire more males because we'd like to have more males around." How about looking for people to hire, instead of specific sets of genitalia?
I also think that technically inclined females sometimes have more in common with technically inclined males than they do with "normal" women. A personality amenable to huddling away comfortably in front of a computer screen with a ton of coffee for long periods of time is probably the most crucial factor to success. That's one of the great things about programming though, it's much more talent-based than it is concerned with biological sex, how much money your parents spent on your degree, or whatever else that shouldn't really be relevant but in too many career fields, is still heavily emphasized.
A more telling study might be to compare success rates of startups with all female teams, versus all male teams. Then at least you remove the bias of having success attract a more gender-diverse team. Although you'd probably need to correct for industry area, since I'm guessing females choose a different set of markets to go after, in aggregate.
Crappy logic aside, I actually do think the original claim that women boost startup success is probably true. It's super valuable having team members who understand 50% of the population (and ~85% of purchasers), and knowing your customers is critical.
The choice to hire diversely is symptomatic of certain types of thinking: 1) Open / wide / flexible thinking. Someone who hires diversely is more likely to find it easier to conceptualize worldviews farther from their own as still relevant. 2) Civic responsiblity / empathy. A desire to be a part of the solution to gender imbalance rather than a part of the imbalance. 3) Long term thinking. Having people who are more different from you on the team is a more effective solution in the long run, because you're likely to cover more bases.
This means that leaders who make the choice to hire diversely are more likely to also be leaders who plan slightly further ahead, are more dextrous in the different ways they could see problems, and able to focus on non-top-down perspectives on the business, such as what the customer might be thinking. (All of this is correlative of course, not a->b.) As a result, leaders who hire diversely are more likely to be already be leaders who are better at running a successful company.
Ie, diversity is a litmus, not a direct cause.
I would be very, very surprised to find if, someday when women run 50% of companies and are in workforce balance, companies with women executives are still more likely to be successful.
EDIT: I do, however, think there is also an edge to leadership that can sometimes come simply from being in a minority, whatever the minority is. Not because there's anything wrong with white men, but simply because there's a unique perspective on problems and indirect causality that you get from being at a cultural disadvantage that, if you manage not to get weighed down by it, ends up being pretty useful in business.
Since gender equality is such a hot and sensitive topic, I can see how it incentivized people to jump to such conclusions.
"Startups who have female first hires typically are those with less discriminatory hiring practices. So the effect is not female hires => higher profits, but rather female hires => startup doesn’t discriminate => startup seeks most efficient outcome => maximizes profits."
Not that women don't drive startup success! But DJ disappoints on statistical analysis.
So the question is really: what kinds of startups have appeal to women?
