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My god, you are the one who wrote this and you don't understand why your sample is so biased? No wonder it is so horrible. How many teachers are in your sample

My god, you are the one who wrote this and you don't understand why your sample is so biased? No wonder it is so horrible. How many teachers are in your sample? How many nurses? (extremely few - how would the tool capture that?) Or more insiduously compare the number of engineers to the number of people in marketing. Both are "knowledge workers", but they have a very different gender breakdown. And they also have a very different day. Someone in engineering is on their computer all day, someone in marketing or sales probably not. If you are making sales calls all day instead of looking at an IDE, you can be vastly more productive than an engineer staring at a blank screen, but that doesn't show up in RescueTime at all. It's one thing one you are on a team and can account for that, it is another when you make a generalization about the population. All of your data can be explained with other rational besides "men are more productive than women". Your company approaches the world from the view of engineers to begin with, and then slaps that bias on top of a faulty set of data. You should be careful with this or you are going to have NOW breathing down your neck (I guess all press is good press, but do you really want to make your product piss of women to that extent?).

webwright 17y ago
I didn't write it... I thought I made that clear. Again, it's SUPER obvious to us (and to you, apparently) that our dataset is NOT representative. Do you think it'd help if I added that to the bottom of the post? We just assumed that's obvious once we described the value proposition.
Retric 17y ago
Read what he wrote not what you think he wrote. He said picking an even sample of male and female removes the male/female bias, but only that bias. Edit: Granted, that's not strictly sufficient because the human population is not 50:50 M/F, but that's a side issue.
- whyenot 17y ago
  I think you are missing krschultz's point. There is probably an interaction effect between occupation and gender. Sampling equally from each gender doesn't necessarily remove gender bias.
  - Retric 17y ago
    I think your both (+the people that up voted you) are confusing occupation bias with gender bias. AKA, if the gender bias was caused by an occupation bias fixing the gender bias does not fix the occupation bias.
    - whyenot 17y ago
      No offense, but you are repeating exactly what I said. It's likely that gender and occupation are not independent variables.
houseabsolute 17y ago
WTF, read the comment you are responding to before you respond to it.

webwright 17y ago

I didn't write it... I thought I made that clear. Again, it's SUPER obvious to us (and to you, apparently) that our dataset is NOT representative. Do you think it'd help if I added that to the bottom of the post? We just assumed that's obvious once we described the value proposition.

Retric 17y ago

Read what he wrote not what you think he wrote. He said picking an even sample of male and female removes the male/female bias, but only that bias. Edit: Granted, that's not strictly sufficient because the human population is not 50:50 M/F, but that's a side issue.

whyenot 17y ago
I think you are missing krschultz's point. There is probably an interaction effect between occupation and gender. Sampling equally from each gender doesn't necessarily remove gender bias.
- Retric 17y ago
  I think your both (+the people that up voted you) are confusing occupation bias with gender bias. AKA, if the gender bias was caused by an occupation bias fixing the gender bias does not fix the occupation bias.
  - whyenot 17y ago
    No offense, but you are repeating exactly what I said. It's likely that gender and occupation are not independent variables.

houseabsolute 17y ago

WTF, read the comment you are responding to before you respond to it.