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I don't understand how Simpson's paradox is different from missing an explanatory variable and confusing correlation vs. partial correlation. In Wikipedia's ar
by cbellet 12y ago
I don't understand how Simpson's paradox is different from missing an explanatory variable and confusing correlation vs. partial correlation.
In Wikipedia's article header chart, what I see is the projection on a plane of a 3D problem, where the 3rd dimension has been overlooked. http://en.wikipedia.org/wiki/Simpson's_paradox http://en.wikipedia.org/wiki/Simpson's_paradox
In Bob vs. Alice, I see also that the night/day flight dummy wasn't accounted for hence resulting in the so-called paradox.
- mendicantB 12y agoYou're correct. It isn't different. Simpsons paradox is actually a key indicator of a confounding variable.
- vbs_redlof 12y agoIt's just a special case of omitted variables with categorial variables. So instead of parameter estimates being biased up or down x amount (to the extent covariates are correlated with error terms), with Simpsons's paradox the mean effect is completely wrong due to improper grouping. This often leads to flipping signs on estimated parameters -- 'surprising' results that gets papers published. My favourite explanation: http://vudlab.com/simpsons/ http://vudlab.com/simpsons/
- deleted 12y ago[deleted]
- baddox 12y agoThe more complicated examples of Simpson's paradox tend to be important causes being ignored. But it's not always an issue of causality, like in the example of two Wikipedia contributors. That example doesn't really have a hidden cause, it's just the use of percentages where total articles is clearly the more useful metric.