Do Female Executives Drive Start-up Success?

Maybe it's because people they do business with want to sleep with them.

That is completely missing the Bayesian worldview, which says you should assign a distribution to the probabilities. A uniform distribution of probabilities, 0-100%, is not perfect, captures the uncertainty better than a point distribution at 50%.

I am holding a pencil right now. Is the eraser at a higher altidude than the tip? Maybe, maybe not. Is that 50/50? Would you be willing to make a bet from me at 1:1 odds, that the the answer is yes?

Thank god for this comment. "Charmed our way." haha

Yeah, I know it is cool to throw the word Bayesian around. But it is a lot cooler when it is done by someone who actually knows enough about statistics to be making sense. You clearly don't. Adding your comments about the sloppiness of others makes it just ridiculous.

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.

Wow. Way to take those bits out of context.

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.