Your interview with Jules Pieri on the morning of May 27th concerning Title IX for business missed a crucial point. Pieri assumes that such a large gap in venture capitalist money (only 4% to women) is due to sexism.
But men tend to pursue degrees in the sciences, such as engineering and computer science. These skills more easily translate into start-up ideas compared to the softer sciences women tend to major in. Even a study NPR reported on in March found investors chose businesses proposed by men 68% of the time, not 96%. There's a lot more going on than the raw averages suggest.
Venture capitalists make money by investing in good ideas. But at the heart of Pieri's Title IX proposal is a bizarre notion: these capitalists care more about being sexist than about being profitable.
I am deeply disappointed you didn't question her on this all-too-common over-simplification.
Sincerely,
David Youngberg
Asst. Professor of Economics
Montgomery College
Showing posts with label Statistics. Show all posts
Showing posts with label Statistics. Show all posts
Tuesday, May 27, 2014
95.8% of Averages Are Nonsense
Sent to Marketplace today:
Thursday, September 27, 2012
Billionaires Per 10 Million
The US tops the list of countries with the most billionaires but who cares about the raw numbers? I'm much more interested in the number of billionaires per person. Or per ten million people in this case. That makes it easier (though some countries on this list don't have ten million people...but that just highlights how billionaire-friendly they are). Here's where I got my countries by population data. I removed Hong Kong's population from China's as I assume that their billionaires weren't included in China's billionaire count.
| Country | Billionaires per 10m |
| Hong Kong | 91.4 |
| Switzerland | 71.3 |
| United Kingdom | 22.6 |
| Germany | 16.8 |
| United States | 15.3 |
| Canada | 11.4 |
| Russia | 6.8 |
| Brazil | 2.5 |
| China | 1.1 |
| India | 0.9 |
Thursday, April 05, 2012
How Common Are Functional Families?
Happy families are all alike; every unhappy family is unhappy in its own way.
Before I begin, let me emphasize I'm talking in very large generalizations. This is for clarity purposes; please don't mistake my simplifications for being rude.
I was born and raised in a "functional family." I always got enough to eat, I got help with school work, and my parents showed love and affection. They never hit my brother or myself and their fights were rare and civil. I had an excellent upbringing (thanks, by the way, Mom and Dad). For my brother's and I's part, we were good kids. We weren't angels by any means, but we stayed out of major trouble, did well in school, etc.
So when I wonder if most families are functional--perhaps little a better, perhaps a little worse than mine--I naturally think back to my childhood and conclude my childhood was typical. This is, of course, a poor way to draw a general conclusion. You can tell because when I would ask my friends who come from dysfunctional families, they would make the opposite conclusions: "we're not unusual, David, you are." They are, in all likelihood, making the same mistake I am.
So I might say "But we had many family friends who are also functional families." Of course, functional families like to hang out with other functional families so that's not very good. It leads to dysfunctional families drawing one of two conclusions:
(a) They were somewhat dysfunctional, which means they would be friends with other somewhat dysfunctional families (functional families won't have them and the somewhat dysfunctional won't hang out with the really dysfunctional one). Thus they cite the other families they know and use that as more evidence of how common people like them are.
(b) They were really dysfunctional, which means, based on this rough model, that they will have few to none family friends. So they would probably say "look, my family was really bad; it seems reasonable that something somewhat better than what I experienced would be the norm."
(This says nothing about the families that are functional to a level that's annoying. As this episode of South Park suggests, such families would have trouble finding outside friends as well.)
So how common are functional families? I have no idea and unless someone did a random sample and came up with a good measure of functionality (good luck), then I'd say no one really knows. But what learning about others' families have taught me is that while the average is hard to figure, the standard deviation is huge.
Before I begin, let me emphasize I'm talking in very large generalizations. This is for clarity purposes; please don't mistake my simplifications for being rude.
I was born and raised in a "functional family." I always got enough to eat, I got help with school work, and my parents showed love and affection. They never hit my brother or myself and their fights were rare and civil. I had an excellent upbringing (thanks, by the way, Mom and Dad). For my brother's and I's part, we were good kids. We weren't angels by any means, but we stayed out of major trouble, did well in school, etc.
So when I wonder if most families are functional--perhaps little a better, perhaps a little worse than mine--I naturally think back to my childhood and conclude my childhood was typical. This is, of course, a poor way to draw a general conclusion. You can tell because when I would ask my friends who come from dysfunctional families, they would make the opposite conclusions: "we're not unusual, David, you are." They are, in all likelihood, making the same mistake I am.
So I might say "But we had many family friends who are also functional families." Of course, functional families like to hang out with other functional families so that's not very good. It leads to dysfunctional families drawing one of two conclusions:
(a) They were somewhat dysfunctional, which means they would be friends with other somewhat dysfunctional families (functional families won't have them and the somewhat dysfunctional won't hang out with the really dysfunctional one). Thus they cite the other families they know and use that as more evidence of how common people like them are.
(b) They were really dysfunctional, which means, based on this rough model, that they will have few to none family friends. So they would probably say "look, my family was really bad; it seems reasonable that something somewhat better than what I experienced would be the norm."
(This says nothing about the families that are functional to a level that's annoying. As this episode of South Park suggests, such families would have trouble finding outside friends as well.)
So how common are functional families? I have no idea and unless someone did a random sample and came up with a good measure of functionality (good luck), then I'd say no one really knows. But what learning about others' families have taught me is that while the average is hard to figure, the standard deviation is huge.
Wednesday, November 10, 2010
The Significance of Significance
William Easterly admits to being sloppy with his statistical reporting.
Suppose you and some friends are out partying but your friend Bob didn't show up. Where's Bob? It's late: Bob's probably at home. Bob being at home is your null hypothesis. (When I first learned about null hypothesis, I learned it as the theory that nothing interesting's going on. It's more complex than that but that will suit us for our purposes.)
You decide to call Bob to figure out if he can come party with you. Granted, Bob might be busy playing poker or getting drunk at his favorite bar. But he also might be home and it's a lot easier to get Bob to do something when he isn't doing anything.
If Bob tells you he's at home, you can accept the null hypothesis. Bob is indeed at home. (Technically, you never actually accept the null due to mathematical constraints but ignore that to build the intuition.) If Bob tells you he's in the gutter somewhere, at a strip club, or doing something else "interesting," you reject the null hypothesis. But if Bob doesn't pick up the phone, if it just rings and rings and rings, then you fail to reject the null hypothesis. This is not the same thing as accepting the null. Bob could be asleep in bed OR he could be in jail after having just spray painted a cop's car while wasted on vodka. You just don't know.
That confusion, that not getting an answer is the same thing as getting something boring, is the confusion William Easterly made. The Rajan-Subramanian paper didn't get statistical significance when it came to aid's relation to growth which is the same as the phone not picking up. To quote Easterly once more, "Absence of Evidence does not constitute Evidence for Absence."
This is an important point, but it's not intuitive. Let me take a moment to interpret.Aid policy was based on the premise that aid raises growth, but …{a major} study of this question was saying that this premise was false.This quote refers to the Rajan-Subramanian paper (later published in a peer-reviewed journal) that was unable to reject the hypothesis of a zero effect of aid on growth. As I never tire of pointing out, we often get our conditional probabilities mixed up. Based on standard statistical methodology, the (1) probability of failing to reject the zero effect hypothesis is high when the effect is indeed zero. Unfortunately, the author of the quote incorrectly thinks this implies the opposite probability is high — (2) the likelihood that the effect is indeed zero when you fail to reject the hypothesis of zero. This likelihood can actually be quite low even if the first probability is high.
Suppose you and some friends are out partying but your friend Bob didn't show up. Where's Bob? It's late: Bob's probably at home. Bob being at home is your null hypothesis. (When I first learned about null hypothesis, I learned it as the theory that nothing interesting's going on. It's more complex than that but that will suit us for our purposes.)
You decide to call Bob to figure out if he can come party with you. Granted, Bob might be busy playing poker or getting drunk at his favorite bar. But he also might be home and it's a lot easier to get Bob to do something when he isn't doing anything.
If Bob tells you he's at home, you can accept the null hypothesis. Bob is indeed at home. (Technically, you never actually accept the null due to mathematical constraints but ignore that to build the intuition.) If Bob tells you he's in the gutter somewhere, at a strip club, or doing something else "interesting," you reject the null hypothesis. But if Bob doesn't pick up the phone, if it just rings and rings and rings, then you fail to reject the null hypothesis. This is not the same thing as accepting the null. Bob could be asleep in bed OR he could be in jail after having just spray painted a cop's car while wasted on vodka. You just don't know.
That confusion, that not getting an answer is the same thing as getting something boring, is the confusion William Easterly made. The Rajan-Subramanian paper didn't get statistical significance when it came to aid's relation to growth which is the same as the phone not picking up. To quote Easterly once more, "Absence of Evidence does not constitute Evidence for Absence."
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