[cirenehc]

> "Accuracy remained high (69%) even when controlling for age, gender, and ethnicity."

So if I just assign the majority label to all of the population of a given demographics group, I would get the same result right? i.e., predicting "left" for all minorities under 30. You would also get ~70% accuracy.

[anigbrowl]

What do you think 'controlling for age [,etc]' means?

[cirenehc]

Please state what you want to say. No need to be passive aggressive.

[anigbrowl]

You quoted the part about controlling for age, then described the sort of mistake that comes from not controlling for age. So I would like to know what you think it means, in order to meet you where you are. I'm not expressing aggression toward you.

[cirenehc]

My interpretation of control is fixing all other variables (the ones they mentioned) except for the one being measured (political orientation). If that's not what they did I'm happy to learn.

[anigbrowl]

In that case I don't understand your original comment, as it describes the sort of mistake that arises when you don't control for other factors, but you appear to accept that they did.

[cirenehc]

My original comment said:

> So if I just assign the majority label to all of the population of a given demographics group, I would get the same result right? i.e., predicting "left" for all minorities under 30. You would also get ~70% accuracy.

I meant that even if you control age, gender, ethnicity, a very trivial predictor (i.e., always predicting the majority label) could yield similar performance. What I meant to say was that their model may not perform as well as they made it sound.