5 ms·

Actually, no. When you pick out an individual there's still a 60% chance than one is better than the other given no other information than their sex. That's how

Actually, no. When you pick out an individual there's still a 60% chance than one is better than the other given no other information than their sex. That's how statistics works. But I agree that it's better not to make the prejudgement at all. The problem is that there are many things that are true in the aggregate but we can't acknowledge them because we are scared of demotivating people. Is that the best we can do?

bryanlarsen 11y ago
Yup, "insignificant" is the wrong word, since it has a specific meaning in statistics, and for that meaning, my paragraph is definitely wrong. How about if I change that word: "But at an individual level, that difference becomes trivial."
- golemotron 11y ago
  I get what you mean, regardless of wording. People should be seen as individuals. I don't know how we can be blind to aggregate differences enough to not be perturbed when we have unequal outcomes but not so cognizant of them that we demotivate people. But as I write this, I'm arriving at an answer: we need to recognize that people are making choices based upon who they are and what they like.
  - metasean 11y ago
    As I have read this thread, I keep coming back to how we measure competence and ability. For example, at one point in history our IQ tests included questions based on baseball rules. So someone taking the test that didn't know the rules of baseball couldn't do well – regardless of their mathematical or logical ability, i.e. what the question was supposedly measuring. Along those lines, I would strongly: - recommend listening to the Hanselminutes episode on Women in Technology in the Muslim World (http://hanselminutes.com/203/women-in-technology-in-the-muslim-world http://hanselminutes.com/203/women-in-technology-in-the-musl...) - remind people that the first programmer was Ada Lovelace, and computers used to be actual people and mostly female (https://en.wikipedia.org/wiki/Human_computer https://en.wikipedia.org/wiki/Human_computer) & suggest reading some of the reasons this changed (http://www.smithsonianmag.com/smart-news/computer-programming-used-to-be-womens-work-718061/?no-ist http://www.smithsonianmag.com/smart-news/computer-programmin...) In other words, culture can strongly impact both perceptions of ability and measurements of ability, as well as impacting the equitable training and opportunity that people are afforded. I suspect that most differences in most fields are not actual sex differences (i.e. biological), but in fact are social differences based on gender (i.e. cultural based on a person's gender identity).
    - cgearhart 11y ago
      I recently took an artificial intelligence class that required writing an agent to answer non-verbal IQ test questions. One of the greatest challenges was that some of the questions had ambiguous answers. For example, an axial symmetry in the example frame could be reproduced with either a rotation or a mirror operation in the output frame, and both options were available as candidate solutions. Naturally, the question came up: how do we define the "right" answer when there are multiple valid choices? As it turns out, the folks who write the test determine the correct answer to these types of question based on the most common response from test takers who get the highest scores on non-ambiguous questions. The thinking goes that if folks who get most of the other questions right largely agree about a preference for one answer over the other, that must be the most correct answer. The most interesting part of that discussion to me was when a classmate observed that we use human intelligence as a baseline for measuring human intelligence, and then marvel at the extent of our own intelligence; if a machine used more sophisticated logic than a human was capable of to choose a different answer, we would look down on the machine for being less intelligent than man. It reminded me very much of Plato and the shadows in the cave.
Retric 11y ago
You’re trying to redefine words out of context. Significant is often used in science to mean large enough to be concerned with. So, a 1% chance of death tomorrow is a significant chance of death tomorrow.
logfromblammo 11y ago
That is not how statistics works. This is how you reverse a conditional probability (Bayes's Rule): P(B|A) = P(A|B) * P(B) / P(A) If A is "female" and ~A is "male", and B is "good engineer", then what we know , including ancestor post's assumption, is this: P(A|B) = 0.4 (the population of known-good engineers is 40% female) P(A) = 0.5 (the population as a whole is 50% female) We can further assume, arbitrarily, that P(B) = 0.02 (one in every fifty people make good engineers). So the likelihood that a person is a good engineer given that they are female is P(B|A). P(B|A) = 0.4 * 0.02 / 0.5 = 0.016 P(B|~A) = 0.6 * 0.02 / 0.5 = 0.024 What we learn from this is that sex is not a good screening criterion for finding good engineers. You realize an advantage of only 0.8%, not 60%.
- 6502nerdface 11y ago
  Thanks for presenting this so clearly. However, note that, as you show in this scenario, males are, on a relative basis, 50% more likely than females to be good engineers, knowing nothing else about them other than gender (P(B|~A) = 1.5 * P(B|A)). While the absolute probabilities (and difference in probabilities) are small, the relative improvement in classification error rate attributable to knowing the person's gender could very well mean that gender is still a useful feature to include (among many others! it's not so useful on its own, as you note) in a classifier for good engineers.
  - logfromblammo 11y ago
    If you were writing an automated Bayesian classifier to find good engineers, males would get one measly additional point towards the refer-to-a-human threshold. In contrast, there are many other hypothetical rules that would be better predictors. Liking Star Wars, 4 points. Star Trek, 9 points. Knowing how much mana a Lightning Bolt costs, 6 points. Owning a 3-wolf shirt, 2 points. Being left-handed, 15 points. Reading HN, 40 points. Anything you can glean statistically from a population of known-good engineers can be transformed into Bayesian classifier rules, in exactly the same way you can predict an e-mail is spam if it has certain strings in it. But even including the one point for knowing the sex could be considered sex discrimination, even though it would be totally supported by the math. But since you likely want your threshold value to be high enough to weed out false positives, while still low enough to avoid false negatives, that one point rule is practically a waste of time. The cases where that one point makes a difference will be just those people who barely meet the threshold. If you have two people who are only just barely good enough to be considered good engineers, and otherwise exactly the same, the statistical argument says to prefer the male. I have never met anyone that is content to hire engineers who are likely to be only barely adequate in preference to those who are likely much better. That hypothetical person is the only one who might care about male or female. Everyone else will be looking for the highest point totals, gained from criteria that are better predictors.

bryanlarsen 11y ago

Yup, "insignificant" is the wrong word, since it has a specific meaning in statistics, and for that meaning, my paragraph is definitely wrong. How about if I change that word: "But at an individual level, that difference becomes trivial."

golemotron 11y ago
I get what you mean, regardless of wording. People should be seen as individuals. I don't know how we can be blind to aggregate differences enough to not be perturbed when we have unequal outcomes but not so cognizant of them that we demotivate people. But as I write this, I'm arriving at an answer: we need to recognize that people are making choices based upon who they are and what they like.
- metasean 11y ago
  As I have read this thread, I keep coming back to how we measure competence and ability. For example, at one point in history our IQ tests included questions based on baseball rules. So someone taking the test that didn't know the rules of baseball couldn't do well – regardless of their mathematical or logical ability, i.e. what the question was supposedly measuring. Along those lines, I would strongly: - recommend listening to the Hanselminutes episode on Women in Technology in the Muslim World (http://hanselminutes.com/203/women-in-technology-in-the-muslim-world http://hanselminutes.com/203/women-in-technology-in-the-musl...) - remind people that the first programmer was Ada Lovelace, and computers used to be actual people and mostly female (https://en.wikipedia.org/wiki/Human_computer https://en.wikipedia.org/wiki/Human_computer) & suggest reading some of the reasons this changed (http://www.smithsonianmag.com/smart-news/computer-programming-used-to-be-womens-work-718061/?no-ist http://www.smithsonianmag.com/smart-news/computer-programmin...) In other words, culture can strongly impact both perceptions of ability and measurements of ability, as well as impacting the equitable training and opportunity that people are afforded. I suspect that most differences in most fields are not actual sex differences (i.e. biological), but in fact are social differences based on gender (i.e. cultural based on a person's gender identity).
  - cgearhart 11y ago
    I recently took an artificial intelligence class that required writing an agent to answer non-verbal IQ test questions. One of the greatest challenges was that some of the questions had ambiguous answers. For example, an axial symmetry in the example frame could be reproduced with either a rotation or a mirror operation in the output frame, and both options were available as candidate solutions. Naturally, the question came up: how do we define the "right" answer when there are multiple valid choices? As it turns out, the folks who write the test determine the correct answer to these types of question based on the most common response from test takers who get the highest scores on non-ambiguous questions. The thinking goes that if folks who get most of the other questions right largely agree about a preference for one answer over the other, that must be the most correct answer. The most interesting part of that discussion to me was when a classmate observed that we use human intelligence as a baseline for measuring human intelligence, and then marvel at the extent of our own intelligence; if a machine used more sophisticated logic than a human was capable of to choose a different answer, we would look down on the machine for being less intelligent than man. It reminded me very much of Plato and the shadows in the cave.

Retric 11y ago

You’re trying to redefine words out of context. Significant is often used in science to mean large enough to be concerned with. So, a 1% chance of death tomorrow is a significant chance of death tomorrow.

logfromblammo 11y ago

That is not how statistics works. This is how you reverse a conditional probability (Bayes's Rule): P(B|A) = P(A|B) * P(B) / P(A) If A is "female" and ~A is "male", and B is "good engineer", then what we know , including ancestor post's assumption, is this: P(A|B) = 0.4 (the population of known-good engineers is 40% female) P(A) = 0.5 (the population as a whole is 50% female) We can further assume, arbitrarily, that P(B) = 0.02 (one in every fifty people make good engineers). So the likelihood that a person is a good engineer given that they are female is P(B|A). P(B|A) = 0.4 * 0.02 / 0.5 = 0.016 P(B|~A) = 0.6 * 0.02 / 0.5 = 0.024 What we learn from this is that sex is not a good screening criterion for finding good engineers. You realize an advantage of only 0.8%, not 60%.

6502nerdface 11y ago
Thanks for presenting this so clearly. However, note that, as you show in this scenario, males are, on a relative basis, 50% more likely than females to be good engineers, knowing nothing else about them other than gender (P(B|~A) = 1.5 * P(B|A)). While the absolute probabilities (and difference in probabilities) are small, the relative improvement in classification error rate attributable to knowing the person's gender could very well mean that gender is still a useful feature to include (among many others! it's not so useful on its own, as you note) in a classifier for good engineers.
- logfromblammo 11y ago
  If you were writing an automated Bayesian classifier to find good engineers, males would get one measly additional point towards the refer-to-a-human threshold. In contrast, there are many other hypothetical rules that would be better predictors. Liking Star Wars, 4 points. Star Trek, 9 points. Knowing how much mana a Lightning Bolt costs, 6 points. Owning a 3-wolf shirt, 2 points. Being left-handed, 15 points. Reading HN, 40 points. Anything you can glean statistically from a population of known-good engineers can be transformed into Bayesian classifier rules, in exactly the same way you can predict an e-mail is spam if it has certain strings in it. But even including the one point for knowing the sex could be considered sex discrimination, even though it would be totally supported by the math. But since you likely want your threshold value to be high enough to weed out false positives, while still low enough to avoid false negatives, that one point rule is practically a waste of time. The cases where that one point makes a difference will be just those people who barely meet the threshold. If you have two people who are only just barely good enough to be considered good engineers, and otherwise exactly the same, the statistical argument says to prefer the male. I have never met anyone that is content to hire engineers who are likely to be only barely adequate in preference to those who are likely much better. That hypothetical person is the only one who might care about male or female. Everyone else will be looking for the highest point totals, gained from criteria that are better predictors.