|
|
|
|
|
by xenocyon
1713 days ago
|
|
"The more dimensions you analyze, the close to 'truth' you can get." Data scientist here. The above is not the correct takeaway from Simpson's paradox. It is not generally correct that the trends seen in subdivided groups are closer to truth than overall groups; sometimes the opposite is the case. It depends entirely on what the divisions are and whether they make sense. With regard to gender-based pay disparity, there are a multiplicity of factors, from the most obvious ("equal pay for equal work") to other factors such as the fact that professions largely staffed by women tend to get paid less than professions largely staffed by men. For instance childcare is miserably compensated, despite being a position of high responsibility and impact. The consensus regarding women during the pandemic (not limited to tech workers) was that women have disproportionately sacrificed their careers to cover the needs of childcare and at-home schooling during the pandemic. |
|
What seems like a plausible interpretation to my un-credentialed, low-IQ self is: 1. Within dimension comparisons in this instance may suggest that “like” populations are similar across gender, suggesting that there is no bias in how tech compensates women and men. 2. The distribution of said dimensions differing between men and women may reflect exogenous effects that are not controllable by tech since they’re upstream of the tech hiring process.