7 ms·

> Sadly "equal pay" means whatever you want it to mean. Exactly this. At a fundamental level every individual has an infinite number of "dimensions" (in ML par

> Sadly "equal pay" means whatever you want it to mean. Exactly this. At a fundamental level every individual has an infinite number of "dimensions" (in ML parlance) associated with him, and "equal pay" people or groups will try and convince you that expected pay should be the same over a certain subset of dimensions while ignoring all the others. Invariably they will pick the most beneficial to that particular person or influence group. They will give you arguments from moral, while forgetting to mention that like in any midly complex problem, data bias and confounding variables are of paramount importance. So "equal pay" means whatever you want it to mean because try and you may, you will never have a model with the full infinite set of dimensions that the real world has. Having to pick you pick the ones you want, a choice that others will attack.

stdbrouw 9y ago
A heuristic in which you refuse to undertake any action without complete information of perfect reliability is always biased towards the status quo. Heck, it's straight from the CIA Simple Sabotage Field Manual. So in the guise of "first needing to understand the complexities of the problem", you are rationalizing away the preponderance of evidence which shows that, yep, any way you cut it, there's a gender wage gap.
- fwdpropaganda 9y ago
  > A heuristic in which you refuse to undertake any action without complete information of perfect reliability is always biased towards the status quo. I agree with that phrase, and I acknowledge that it's a problem, but you're jumping into conclusions about what I was trying to say. I wasn't trying to say "we don't have complete information, so we should do nothing." Read on: > you are rationalizing away the preponderance of evidence which shows that, yep, any way you cut it, there's a gender wage gap. No, that's is precisely my point. It is NOT true that any way you cut it there's a gender gap. If you let me cut it how I want it I can have the gap be anything I want by carefully (as an example) picking which of the omitted variables I adjust for sampling bias and which ones I don't[0]. That is what I was trying to say. [0] And lets not talk about confounding variables, that problem is at least an order of magnitud harder even than sampling/population bias.
- ada1981 9y ago
  In case anyone needs a PDF of the CIA Simple Sabotage Field Manual https://www.cia.gov/news-information/featured-story-archive/2012-featured-story-archive/CleanedUOSSSimpleSabotage_sm.pdf https://www.cia.gov/news-information/featured-story-archive/...
kristianc 9y ago
> Exactly this. At a fundamental level every individual has an infinite number of "dimensions" (in ML parlance) associated with him, and "equal pay" people or groups will try and convince you that expected pay should be the same over a certain subset of dimensions while ignoring all the others. Surely the right response to a study which challenges your existing worldview would be "Hmm, that's interesting - I wonder what is driving that?" rather than "The equal pay people or groups will always try to convince you..."
- LanceH 9y ago
  But the bar -- as established by the 77% number -- is that a difference in pay is sexism.
- mseebach 9y ago
  It seems that GP did that, and learned that these studies ignore inconvenient factors.
  - mcny 9y ago
    What we should mandate is transparency. We all think we are expert negotiators but we are all idiots. We will all be better off if all salary and all compensation information is public and easily accessible. Sadly, a lot of people think they have something to lose and will never support it.
    - dingo_bat 9y ago
      I don't want my salary to be transparent. There is something called privacy. My salary is a private matter. I really don't believe I'm any kind of expert in negotiation.
      - oblio 9y ago
        His point is that your need for privacy is preventing group wins. You want to avoid a bit of shame or envy but by making this decision we, as employees, lose a lot of our leverage. You wouldn’t have to negotiate a better salary if it would be obvious that you are underpaid.
jwfxpr 9y ago
You cannot dismiss rigorous statistical analysis by arguing it can never encompass the full dimensions of the data. Of course it can't. The map is not the territory; it is a useful way to find our way around it. Ignoring the map is perilous, if not arrogant, even though it is merely a flawed representation of the real truth. You might argue that a specific study or meta-analysis contains a bias or misinterpretation, but only if you've actually examined their methodology, data, and reasoning. You cannot argue that all studies of complex topics are invalid simply because their topics are complex.
- tomp 9y ago
  > You cannot dismiss rigorous statistical analysis by arguing it can never encompass the full dimensions of the data. This simply means it's not rigorous. See Omitted-variable bias - from [1]: The bias results in the model attributing the effect of the missing variables to the estimated effects of the included variables. For example, including gender but not education or hours worked will result in attributing pay differences to gender, but including all relevant variables shows that's gender is irrelevant. https://en.wikipedia.org/wiki/Omitted-variable_bias https://en.wikipedia.org/wiki/Omitted-variable_bias
  - dwaltrip 9y ago
    You entirely missed the point. We can never include every single relevant variable to perfectly explain the observation. It's impossible. This doesn't mean statistics is useless. This is the meaning of the phrase "the map is not the territory". All models are flawed, but some are useful.
    - fwdpropaganda 9y ago
      I think there's some middle ground between saying that the analysis is rigorous and saying it's useless, no?
      - dwaltrip 9y ago
        Indeed. But that's where all the hard work is -- trying to determine how rigorous something is, knowing it certainly isn't perfectly rigorous.
fouc 9y ago
We can use bayesian inference here to update the probability of equal pay as more dimensions are added.
mistermann 9y ago
> Exactly this. At a fundamental level every individual has an infinite number of "dimensions" (in ML parlance) associated with him And of course, this applies not just to people but to most complex entities or ideas. The problem is, when dealing with humans, even intelligent ones, good luck getting them accept this approach when it interferes with their political/emotional/fiscal beliefs or desires. For reference, see recent discussions here on topics like trade tariffs.

stdbrouw 9y ago

A heuristic in which you refuse to undertake any action without complete information of perfect reliability is always biased towards the status quo. Heck, it's straight from the CIA Simple Sabotage Field Manual. So in the guise of "first needing to understand the complexities of the problem", you are rationalizing away the preponderance of evidence which shows that, yep, any way you cut it, there's a gender wage gap.

fwdpropaganda 9y ago
> A heuristic in which you refuse to undertake any action without complete information of perfect reliability is always biased towards the status quo. I agree with that phrase, and I acknowledge that it's a problem, but you're jumping into conclusions about what I was trying to say. I wasn't trying to say "we don't have complete information, so we should do nothing." Read on: > you are rationalizing away the preponderance of evidence which shows that, yep, any way you cut it, there's a gender wage gap. No, that's is precisely my point. It is NOT true that any way you cut it there's a gender gap. If you let me cut it how I want it I can have the gap be anything I want by carefully (as an example) picking which of the omitted variables I adjust for sampling bias and which ones I don't[0]. That is what I was trying to say. [0] And lets not talk about confounding variables, that problem is at least an order of magnitud harder even than sampling/population bias.
ada1981 9y ago
In case anyone needs a PDF of the CIA Simple Sabotage Field Manual https://www.cia.gov/news-information/featured-story-archive/2012-featured-story-archive/CleanedUOSSSimpleSabotage_sm.pdf https://www.cia.gov/news-information/featured-story-archive/...

kristianc 9y ago

> Exactly this. At a fundamental level every individual has an infinite number of "dimensions" (in ML parlance) associated with him, and "equal pay" people or groups will try and convince you that expected pay should be the same over a certain subset of dimensions while ignoring all the others. Surely the right response to a study which challenges your existing worldview would be "Hmm, that's interesting - I wonder what is driving that?" rather than "The equal pay people or groups will always try to convince you..."

LanceH 9y ago
But the bar -- as established by the 77% number -- is that a difference in pay is sexism.
mseebach 9y ago
It seems that GP did that, and learned that these studies ignore inconvenient factors.
- mcny 9y ago
  What we should mandate is transparency. We all think we are expert negotiators but we are all idiots. We will all be better off if all salary and all compensation information is public and easily accessible. Sadly, a lot of people think they have something to lose and will never support it.
  - dingo_bat 9y ago
    I don't want my salary to be transparent. There is something called privacy. My salary is a private matter. I really don't believe I'm any kind of expert in negotiation.
    - oblio 9y ago
      His point is that your need for privacy is preventing group wins. You want to avoid a bit of shame or envy but by making this decision we, as employees, lose a lot of our leverage. You wouldn’t have to negotiate a better salary if it would be obvious that you are underpaid.

jwfxpr 9y ago

You cannot dismiss rigorous statistical analysis by arguing it can never encompass the full dimensions of the data. Of course it can't. The map is not the territory; it is a useful way to find our way around it. Ignoring the map is perilous, if not arrogant, even though it is merely a flawed representation of the real truth. You might argue that a specific study or meta-analysis contains a bias or misinterpretation, but only if you've actually examined their methodology, data, and reasoning. You cannot argue that all studies of complex topics are invalid simply because their topics are complex.

tomp 9y ago
> You cannot dismiss rigorous statistical analysis by arguing it can never encompass the full dimensions of the data. This simply means it's not rigorous. See Omitted-variable bias - from [1]: The bias results in the model attributing the effect of the missing variables to the estimated effects of the included variables. For example, including gender but not education or hours worked will result in attributing pay differences to gender, but including all relevant variables shows that's gender is irrelevant. https://en.wikipedia.org/wiki/Omitted-variable_bias https://en.wikipedia.org/wiki/Omitted-variable_bias
- dwaltrip 9y ago
  You entirely missed the point. We can never include every single relevant variable to perfectly explain the observation. It's impossible. This doesn't mean statistics is useless. This is the meaning of the phrase "the map is not the territory". All models are flawed, but some are useful.
  - fwdpropaganda 9y ago
    I think there's some middle ground between saying that the analysis is rigorous and saying it's useless, no?
    - dwaltrip 9y ago
      Indeed. But that's where all the hard work is -- trying to determine how rigorous something is, knowing it certainly isn't perfectly rigorous.

fouc 9y ago

We can use bayesian inference here to update the probability of equal pay as more dimensions are added.

mistermann 9y ago

> Exactly this. At a fundamental level every individual has an infinite number of "dimensions" (in ML parlance) associated with him And of course, this applies not just to people but to most complex entities or ideas. The problem is, when dealing with humans, even intelligent ones, good luck getting them accept this approach when it interferes with their political/emotional/fiscal beliefs or desires. For reference, see recent discussions here on topics like trade tariffs.