3 ms·

And what is the right number of Fortune 500 CEOs or women Senators?

RangerScience 10y ago
~250, because the average age of a CEO is 55 and it looks like the gender ratio (at least in the US and Canada) is about 50/50 at that age: http://www.statcan.gc.ca/tables-tableaux/sum-som/l01/cst01/demo10a-eng.htm http://www.statcan.gc.ca/tables-tableaux/sum-som/l01/cst01/d...
- smallnamespace 10y ago
  Is there any natural limit to this? If we take this to its logical conclusion, should every group of humans should form a stratified sample[1] of the population? What should the variables be -- age, gender orientation, sex, religion, disability status, race, color, national origin, pregnancy, marital status, number of pets, ... ? [1] https://en.wikipedia.org/wiki/Stratified_sampling https://en.wikipedia.org/wiki/Stratified_sampling Do you see any downsides to making every group 'representative'?
  - deleted 10y ago
    [deleted]
  - RangerScience 10y ago
    Dear god yes there are downsides. Here's my thinking: First, consider that the sample size vs population size of CEOs is goddamn tremendous, and the size of both pools is pretty substantial. It's one thing to try to select a stratified sample of 10 out of 20; it's another to select a stratified sample 500 out of millions. I would expect the transition point to be around when people stop being people, and start being statistics. Second - It's like dark matter, and then it gets more complicated. Notionally, men and women are equal in capability; given just that, I'd expect to see a sample representative of the population demographics. This is not what is observed, so we can posit a) inequality in capability and/or b) other forces acting on the system. A isn't born out by other experiments, leaving B. So then it becomes a question of: What are those other forces AND are they ones we want to keep? Contrast with age, which clearly has a correlation with inequality in capability, so less of the disparity with the theoretical stratified sample must be explained by B. Of course, shit gets complicated fast; the correlation of skill with age is not only non-linear, but is more like a non-linear probability function of skill given an age; self-selection might at first seem to be a reasonable B force, but then you have to consider what's causing the self-selection, and what effect that should have on the "equally capable" population sizes... At 55, it looks like CEOs should be equally split down the middle. That this is not the case means there's more forces in play than explained by the "theory".

~250, because the average age of a CEO is 55 and it looks like the gender ratio (at least in the US and Canada) is about 50/50 at that age: http://www.statcan.gc.ca/tables-tableaux/sum-som/l01/cst01/demo10a-eng.htm http://www.statcan.gc.ca/tables-tableaux/sum-som/l01/cst01/d...

smallnamespace 10y ago
Is there any natural limit to this? If we take this to its logical conclusion, should every group of humans should form a stratified sample[1] of the population? What should the variables be -- age, gender orientation, sex, religion, disability status, race, color, national origin, pregnancy, marital status, number of pets, ... ? [1] https://en.wikipedia.org/wiki/Stratified_sampling https://en.wikipedia.org/wiki/Stratified_sampling Do you see any downsides to making every group 'representative'?
- deleted 10y ago
  [deleted]
- RangerScience 10y ago
  Dear god yes there are downsides. Here's my thinking: First, consider that the sample size vs population size of CEOs is goddamn tremendous, and the size of both pools is pretty substantial. It's one thing to try to select a stratified sample of 10 out of 20; it's another to select a stratified sample 500 out of millions. I would expect the transition point to be around when people stop being people, and start being statistics. Second - It's like dark matter, and then it gets more complicated. Notionally, men and women are equal in capability; given just that, I'd expect to see a sample representative of the population demographics. This is not what is observed, so we can posit a) inequality in capability and/or b) other forces acting on the system. A isn't born out by other experiments, leaving B. So then it becomes a question of: What are those other forces AND are they ones we want to keep? Contrast with age, which clearly has a correlation with inequality in capability, so less of the disparity with the theoretical stratified sample must be explained by B. Of course, shit gets complicated fast; the correlation of skill with age is not only non-linear, but is more like a non-linear probability function of skill given an age; self-selection might at first seem to be a reasonable B force, but then you have to consider what's causing the self-selection, and what effect that should have on the "equally capable" population sizes... At 55, it looks like CEOs should be equally split down the middle. That this is not the case means there's more forces in play than explained by the "theory".