I wrote some shitty code based on the official data of cs graduates in 2015-2014, every candidate was assigned a random competence score based on a Gaussian distribution with a mean of 100.
these are the results:
"there are 48840 males, we will pick only 25000(51.187551187551186%)"
"we will pick all females to represent the company reaching out to them"
"let's say the company is going to hire 5000"
"hiring based on competence and taking females when equal"
"results:"
"male: number: 3505 percentage: 70.1% average score: 123.81256204767604"
"female: number: 1495 percentage: 29.9% average score: 123.75346343448992"
"if we force the 50% ratio"
"the average male score: 126.26036797470225"
"the average female score: 119.60577230318559"
so forcing a 50% ratio does indeed lower the bar. data for
males and females were generated using the same function so arguments about biological factors are not even needed.
these are the results:
"there are 48840 males, we will pick only 25000(51.187551187551186%)"
"we will pick all females to represent the company reaching out to them"
"let's say the company is going to hire 5000"
"hiring based on competence and taking females when equal"
"results:"
"male: number: 3505 percentage: 70.1% average score: 123.81256204767604"
"female: number: 1495 percentage: 29.9% average score: 123.75346343448992"
"if we force the 50% ratio"
"the average male score: 126.26036797470225"
"the average female score: 119.60577230318559"
so forcing a 50% ratio does indeed lower the bar. data for males and females were generated using the same function so arguments about biological factors are not even needed.
the code:https://jsbin.com/nogujuqewe/1/edit?html,console,output