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Because Silicon Valley can't even agree there is a problem. Just look at this thread of all the people whining for more data on this supposed "problem". Some o

Because Silicon Valley can't even agree there is a problem. Just look at this thread of all the people whining for more data on this supposed "problem". Some of the folks won't be happy until there are secret Amazon Echo's tabulating all the shitty comments and passive aggression in every office or emails being scanned and categorized for domineering and condescension. and even then you'll get a bunch of "correlation doesn't equal causation, dummy!"

et-al 10y ago
This. When a minority group is consistently saying there is a problem, one should probably question their existing beliefs and wonder if they don't see it due to some sort of bias. And it's fine to be wrong, to not have see it. However, take an honest chance to listen. Not debate. Listen. For a bunch of smarter-than-average folks here, we're still susceptible to groupthink.
saosebastiao 10y ago
It's called death by a thousand control variables. If only the data controlled for {education, hours worked, experience, attractiveness, nail color, booger density} then you'll see no effect... Except by then, the whole analysis becomes a charade. When you try all the possible combinations of variables and then stop the moment you find one that rejects the null hypothesis, your analysis isn't objective... it is predetermined.
- alexssung 10y ago
  I've never heard of death by a thousand control variables. What's exactly wrong with data controlled by relevant variables such as education, hours worked, and experience? If you find a combination of variables that reject the hypothesis, maybe you should look further into it, no? Dismissing it because you think it's not objective for some absurd reason is rather unscientific. "Trying all possible combinations of variables" does not disqualify an analysis from being objective--not sure where you got that idea from.
  - saosebastiao 10y ago
    There is nothing wrong with controls when they're relevant and have a convincing motive behind their inclusion. What's not okay is sequentially trying all of the possible analyses and then stopping the moment you find the exact combination of variables that tells you what you had already assumed to be true. Especially so when simple analyses point to A, and you keep adding new variables until you get to B, which is exactly the case here. That is a very well known abuse of statistics. There is a reason all the well known and popular Information Criterions (which measure model quality) are parameterized by the number of parameters in the model. And while adding control variables isn't per se bad, there are proper precautions to take when doing so, which become exponentially more costly the more you add. Such as segmented sampling, non-linearity transformations, and even controlled experiments. Because these fraudsters have a motive, the model only needs to be as rigorous as necessary to secure their predetermined conclusion. The "keep adding variables" model almost always ends up as a way to lie with statistics.
    - foobar1967 10y ago
      Wow, I've seen so many comments by climate change deniers using arguments so similar to yours that I literally had deja vu. The horseshoe nature of politics will never cease to amuse me, though; thanks for the example. Also, if a hypothesis is supported by simple studies but falls apart under more complex ones, it might be too simplistic a hypothesis. Almost as if sexism (and sexism guilt-slinging) wasn't an entirely black-and-white problem. Who'd have thought?
      - dang 10y ago
        Please don't add snark into inflammatory comments about already divisive topics. That's destructive of the kind of discussion we're hoping for here. https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newswelcome.html https://news.ycombinator.com/newswelcome.html
      - saosebastiao 10y ago
        Climate Change Deniers are some of the biggest users of the flawed analyses that I'm talking about. For example, several climate change deniers argue that if you control for urban heat island effects, the magnitude of the human component decreases substantially. Such willy-nilly expansions of model complexity fall apart when modeled more rigorously [0][1]. You'll notice that I've never claimed that new parameters universally make the model worse. All of the common Information Criterions [2][3][4] are numerically capable of improvement of model quality with increases in parameter space...it's just unlikely. Such p-value hunting might give you the p-value you're looking for, but it is very unlikely to improve the model. Sexism is a hard problem. Throwing variables wantonly at models until sexism disappears isn't doing anything for the problem...it's nothing more than a pseudoscientific way of pretending it doesn't exist. [0] https://www.skepticalscience.com/urban-heat-island-effect.htm https://www.skepticalscience.com/urban-heat-island-effect.ht... [1] http://news.stanford.edu/news/2011/october/urban-heat-islands-101911.html http://news.stanford.edu/news/2011/october/urban-heat-island... [2] https://en.wikipedia.org/wiki/Bayesian_information_criterion https://en.wikipedia.org/wiki/Bayesian_information_criterion [3] https://en.wikipedia.org/wiki/Deviance_information_criterion https://en.wikipedia.org/wiki/Deviance_information_criterion [4] https://en.wikipedia.org/wiki/Akaike_information_criterion https://en.wikipedia.org/wiki/Akaike_information_criterion
jdietrich 10y ago
I will not apologise for being driven by evidence. If we accept claims we agree with on face value, we lose the moral high ground to reject claims we disagree with on the basis of evidence. Without good evidence, all is lost. We do not blithely accept the claims made by Trump, nor should we blithely accept the claims made by our allies. If sexism is a major problem in the technology industry, it should be easy to produce data demonstrating that fact. Until we understand the magnitude and nature of the problem we face, any attempts to find a solution are, in a very literal sense, completely misguided.
- joshuamorton 10y ago
  So what counts as evidence? Differences in hiring rates, different percentages of the workforce, pay gaps, lack of advancement, mountains of anecdata? Because it seems like when confronted with data, people are quick to shout "correlation" and "cultural factors".
  - flukus 10y ago
    > So what counts as evidence? Solid comparative studies that account for confounding factors. Something obviously not taught in gender studies.
    - deleted 10y ago
      [deleted]
deleted 10y ago
[deleted]

et-al 10y ago

This. When a minority group is consistently saying there is a problem, one should probably question their existing beliefs and wonder if they don't see it due to some sort of bias. And it's fine to be wrong, to not have see it. However, take an honest chance to listen. Not debate. Listen. For a bunch of smarter-than-average folks here, we're still susceptible to groupthink.

saosebastiao 10y ago

It's called death by a thousand control variables. If only the data controlled for {education, hours worked, experience, attractiveness, nail color, booger density} then you'll see no effect... Except by then, the whole analysis becomes a charade. When you try all the possible combinations of variables and then stop the moment you find one that rejects the null hypothesis, your analysis isn't objective... it is predetermined.

alexssung 10y ago
I've never heard of death by a thousand control variables. What's exactly wrong with data controlled by relevant variables such as education, hours worked, and experience? If you find a combination of variables that reject the hypothesis, maybe you should look further into it, no? Dismissing it because you think it's not objective for some absurd reason is rather unscientific. "Trying all possible combinations of variables" does not disqualify an analysis from being objective--not sure where you got that idea from.
- saosebastiao 10y ago
  There is nothing wrong with controls when they're relevant and have a convincing motive behind their inclusion. What's not okay is sequentially trying all of the possible analyses and then stopping the moment you find the exact combination of variables that tells you what you had already assumed to be true. Especially so when simple analyses point to A, and you keep adding new variables until you get to B, which is exactly the case here. That is a very well known abuse of statistics. There is a reason all the well known and popular Information Criterions (which measure model quality) are parameterized by the number of parameters in the model. And while adding control variables isn't per se bad, there are proper precautions to take when doing so, which become exponentially more costly the more you add. Such as segmented sampling, non-linearity transformations, and even controlled experiments. Because these fraudsters have a motive, the model only needs to be as rigorous as necessary to secure their predetermined conclusion. The "keep adding variables" model almost always ends up as a way to lie with statistics.
  - foobar1967 10y ago
    Wow, I've seen so many comments by climate change deniers using arguments so similar to yours that I literally had deja vu. The horseshoe nature of politics will never cease to amuse me, though; thanks for the example. Also, if a hypothesis is supported by simple studies but falls apart under more complex ones, it might be too simplistic a hypothesis. Almost as if sexism (and sexism guilt-slinging) wasn't an entirely black-and-white problem. Who'd have thought?
    - dang 10y ago
      Please don't add snark into inflammatory comments about already divisive topics. That's destructive of the kind of discussion we're hoping for here. https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newswelcome.html https://news.ycombinator.com/newswelcome.html
    - saosebastiao 10y ago
      Climate Change Deniers are some of the biggest users of the flawed analyses that I'm talking about. For example, several climate change deniers argue that if you control for urban heat island effects, the magnitude of the human component decreases substantially. Such willy-nilly expansions of model complexity fall apart when modeled more rigorously [0][1]. You'll notice that I've never claimed that new parameters universally make the model worse. All of the common Information Criterions [2][3][4] are numerically capable of improvement of model quality with increases in parameter space...it's just unlikely. Such p-value hunting might give you the p-value you're looking for, but it is very unlikely to improve the model. Sexism is a hard problem. Throwing variables wantonly at models until sexism disappears isn't doing anything for the problem...it's nothing more than a pseudoscientific way of pretending it doesn't exist. [0] https://www.skepticalscience.com/urban-heat-island-effect.htm https://www.skepticalscience.com/urban-heat-island-effect.ht... [1] http://news.stanford.edu/news/2011/october/urban-heat-islands-101911.html http://news.stanford.edu/news/2011/october/urban-heat-island... [2] https://en.wikipedia.org/wiki/Bayesian_information_criterion https://en.wikipedia.org/wiki/Bayesian_information_criterion [3] https://en.wikipedia.org/wiki/Deviance_information_criterion https://en.wikipedia.org/wiki/Deviance_information_criterion [4] https://en.wikipedia.org/wiki/Akaike_information_criterion https://en.wikipedia.org/wiki/Akaike_information_criterion

jdietrich 10y ago

I will not apologise for being driven by evidence. If we accept claims we agree with on face value, we lose the moral high ground to reject claims we disagree with on the basis of evidence. Without good evidence, all is lost. We do not blithely accept the claims made by Trump, nor should we blithely accept the claims made by our allies. If sexism is a major problem in the technology industry, it should be easy to produce data demonstrating that fact. Until we understand the magnitude and nature of the problem we face, any attempts to find a solution are, in a very literal sense, completely misguided.

joshuamorton 10y ago
So what counts as evidence? Differences in hiring rates, different percentages of the workforce, pay gaps, lack of advancement, mountains of anecdata? Because it seems like when confronted with data, people are quick to shout "correlation" and "cultural factors".
- flukus 10y ago
  > So what counts as evidence? Solid comparative studies that account for confounding factors. Something obviously not taught in gender studies.
  - deleted 10y ago
    [deleted]

deleted 10y ago

[deleted]