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> But add them back in using the numbers on your first link. So we could expect 1 Black applicant, 1 Latino applicant, and 2 women (optimistically!). That's a t

> But add them back in using the numbers on your first link. So we could expect 1 Black applicant, 1 Latino applicant, and 2 women (optimistically!). That's a third of your set of 12, using your data sources, for a programming role. Let's please keep this conversation mathematically honest. I don't know if you're intentionally trying to misrepresent statistics, or if you don't understand them, but: - for three non-mutually-exclusive groups (black, latino, female) - given participation in a set (1, 1, 2) - you cannot sum their independent participation (4) to yield "a third" Beyond that, you appear to intentionally miss the key terms "ceiling" and "optimistically." Given a sample size of one pipeline (the OP), if you optimistically might get a single applicant that meets a given DI quota, it's very likely that you'll get zero. The original article explicitly stated zero, and the numbers make it clear that zero is a very likely result. That's the trouble with small sample sizes. I'm not sure what point you're trying to make by arguing that, in some other circumstance, maybe it could have been one... or even two!

runako 4y ago
> given participation in a set (1, 1, 2) When the set is of 12, that's 4 of 12, which is exactly one third, assuming independent participation (which obviously is what makes the number optimistic/ a ceiling). > Given a sample size of one pipeline Fair! If the OP only ever hires one person, you are correct. At Microsoft's scale, they are more like "the house" at a casino, and so their ratios should more closely approximate the population. I am making the larger point that yes, in one instance, this hiring manager could have experienced a challenge building a diverse pipeline, but that this experience is not generalizable to Microsoft or the industry as a whole. And the related takeaway that if your pipeline is routinely not presenting you with the significant plurality represented by "diverse" candidates, then you have a solvable process problem.
- strix_varius 4y ago
  > When the set is of 12, that's 4 of 12, which is exactly one third, assuming independent participation (which obviously is what makes the number optimistic/ a ceiling). I think this misunderstanding of statistics may be at the core of our disagreement. Yes, 4 is one-third of 12. No, you cannot expect overlapping, non-mutually-exclusive sub-populations of 9% and 18% to equal 27% of a total population. If you're interested in learning why, read (1), but the TL;DR is: given three people, A: black female, B: white male, C: white male, if you sum sub-populations as you're trying to do, you'll get (1 black + 1 female = 2) from a population of 3, and you'll extrapolate (incorrectly) that 2/3rds of this population meets your criteria. I hope that helps explain why your expectation of the gender & racial diversity of CS graduates is inaccurate. 1. https://stats.libretexts.org/Bookshelves/Introductory_Statistics/Book%3A_Introductory_Statistics_(OpenStax)/03%3A_Probability_Topics/3.03%3A_Independent_and_Mutually_Exclusive_Events https://stats.libretexts.org/Bookshelves/Introductory_Statis...
  - runako 4y ago
    > No, you cannot expect overlapping, non-mutually-exclusive sub-populations of 9% and 18% to equal 27% of a total population. Obviously, this is true. I don't know why you continue to exclude the Latino population, but again Latino CS grads (to focus on that narrow slice of tech jobs) are also ~7%. > I hope that helps explain why your expectation of the gender & racial diversity of CS graduates is inaccurate. But (as you have pointed out) we do know more about the sub-populations that let us infer that the populations are less overlapping than we might prefer. If you're interested, there are published demographic data that go into more detail than I have time to do here. But yes, I understand the concept that a Black Latina fits into three categories. > you'll extrapolate (incorrectly) that 2/3rds of this population meets your criteria. The basic idea here is simple: at Microsoft scale, their pipelines broadly should look like the collegiate exit pipeline. If you think that < 30% of programmers are women and/or Black/Latino, I think that assumption is the problem. No matter what assumptions you make around the numbers, my core point still holds. Which is that if your pipeline is routinely missing X% of qualified applicants, your sourcing is not good enough to know whether you're even seeing the best candidates. That is as true if X% is 15% or 30%. Using an analogy: if your company isn't seeing any applicants from states representing 15% of the population (say: Texas and New York), that says more about your sourcing than the talent available.

runako 4y ago

> given participation in a set (1, 1, 2) When the set is of 12, that's 4 of 12, which is exactly one third, assuming independent participation (which obviously is what makes the number optimistic/ a ceiling). > Given a sample size of one pipeline Fair! If the OP only ever hires one person, you are correct. At Microsoft's scale, they are more like "the house" at a casino, and so their ratios should more closely approximate the population. I am making the larger point that yes, in one instance, this hiring manager could have experienced a challenge building a diverse pipeline, but that this experience is not generalizable to Microsoft or the industry as a whole. And the related takeaway that if your pipeline is routinely not presenting you with the significant plurality represented by "diverse" candidates, then you have a solvable process problem.

strix_varius 4y ago
> When the set is of 12, that's 4 of 12, which is exactly one third, assuming independent participation (which obviously is what makes the number optimistic/ a ceiling). I think this misunderstanding of statistics may be at the core of our disagreement. Yes, 4 is one-third of 12. No, you cannot expect overlapping, non-mutually-exclusive sub-populations of 9% and 18% to equal 27% of a total population. If you're interested in learning why, read (1), but the TL;DR is: given three people, A: black female, B: white male, C: white male, if you sum sub-populations as you're trying to do, you'll get (1 black + 1 female = 2) from a population of 3, and you'll extrapolate (incorrectly) that 2/3rds of this population meets your criteria. I hope that helps explain why your expectation of the gender & racial diversity of CS graduates is inaccurate. 1. https://stats.libretexts.org/Bookshelves/Introductory_Statistics/Book%3A_Introductory_Statistics_(OpenStax)/03%3A_Probability_Topics/3.03%3A_Independent_and_Mutually_Exclusive_Events https://stats.libretexts.org/Bookshelves/Introductory_Statis...
- runako 4y ago
  > No, you cannot expect overlapping, non-mutually-exclusive sub-populations of 9% and 18% to equal 27% of a total population. Obviously, this is true. I don't know why you continue to exclude the Latino population, but again Latino CS grads (to focus on that narrow slice of tech jobs) are also ~7%. > I hope that helps explain why your expectation of the gender & racial diversity of CS graduates is inaccurate. But (as you have pointed out) we do know more about the sub-populations that let us infer that the populations are less overlapping than we might prefer. If you're interested, there are published demographic data that go into more detail than I have time to do here. But yes, I understand the concept that a Black Latina fits into three categories. > you'll extrapolate (incorrectly) that 2/3rds of this population meets your criteria. The basic idea here is simple: at Microsoft scale, their pipelines broadly should look like the collegiate exit pipeline. If you think that < 30% of programmers are women and/or Black/Latino, I think that assumption is the problem. No matter what assumptions you make around the numbers, my core point still holds. Which is that if your pipeline is routinely missing X% of qualified applicants, your sourcing is not good enough to know whether you're even seeing the best candidates. That is as true if X% is 15% or 30%. Using an analogy: if your company isn't seeing any applicants from states representing 15% of the population (say: Texas and New York), that says more about your sourcing than the talent available.