The Ecological Fallacy
blog.statwing.com
blog.statwing.com
While I suspect something similar holds true for the 2012 election, such granular breakdowns weren't reported in this cycle's exit poll summaries. The answer is probably hidden away in Edison Research's database, but the raw data hasn't been released yet. For now, you can get a good feel for the income breakdown by looking at Reuters polls[2], and doing the cross-tabs yourself, but there are quite a few undecideds and the sample-size is small.
And I agree that the 250k+ breakdown is also arbitrary, though slightly less so, since it's the lower bound for what many politicians define as "rich". But who knows who those with incomes of 1 million+ voted for? I suspect it was probably Romney. But how about billionaires? The point is that setting such broad and arbitrary breakpoints can be misleading. I'm sure this fallacy has a special name, I just don't know what it is.
>U.S. states with proportionally more immigrants have proportionally more households with income above $100k.[1] Ergo, immigrants are more likely than non-immigrants to have household incomes above $100k.
Whether or not that's a fallacy really depends on how you interpret that statement. If my only information about the world is what is stated above, then finding out that someone is an immigrant should increase my estimation of the likelihood that their household income is above $100k. Being an immigrant is evidence of living in a state where it's more common to have a household income over $100k. Living in a state where it's more common to have a household income over $100k is evidence of having a household income over $100k. When I learn more about the world, my model will change, and I'll stop being wrong about this particular thing.
The fallacy comes when you say that the group correlation implies a correlation at the individual level.
The next question that would inevitably come up is: how do you know? I'm guessing there isn't a way short of looking at the data for individuals. It would probably be safe to always assume group data does not imply individual data.
And, of course, this is another way that people can use statistics to lie to you. I would not be surprised at all to find people intentionally using this fallacy to their benefit.
Does anyone know of anything I'm missing here?
The best approach is multi-variate analysis. Which is to say for each correlation you find, identify another correlation that would be true (and is measurable) if the cause was what you hypothesize is the cause. Its a great way to write a paper too.
You start with "look at this correlation", we hypothesize that cause is "Q" and now we go look at the following correlations to prove or disprove our hypothesis, ... analysis and graphs ..., as you can see our hypothesis is thoroughly {proven | disproved} by these correlations.
If the data isn't available to do additional analysis then you are stuck trying to collect that data somehow. You can end up with inconclusive results in that case.
You wouldn't be able to say anything with confidence about individuals without the individual data but this image helps me think about what individual data would be needed to back up or refute an inference based on group data.
If you can find data within some groups and they show very little to no relationship for X and Y then it's a little more likely that you can use the group inference. But in any case watch out!
The fact that income moves very tightly with vote for Romney means it's a strong correlation. Since one variable goes up while the other goes down, it's a negative correlation.
You can have any combination of strong/weak and negative/positive correlation.
The image on wikipedia is good for wrapping your head around the distinction between the strength and the direction of the correlation (and the distinction between strength and the actual slope of the line of best fit). http://en.wikipedia.org/wiki/Correlation_and_dependence
Apologies if I misunderstood your question.
Cheers!
edit: Deleted the ad at the end, replaced with a link to the front page.
An ecological fallacy (or ecological inference fallacy) is a logical fallacy in the interpretation of statistical data where inferences about the nature of individuals are deduced from inference for the group to which those individuals belong.
Also you don't go into much detail explaining the smoking cancer example. Is it the case that the data turned out to be right 'in spite of' the fallacy or was it a case where the data was immune to the problems of other cases?
Wikipedia has more specific math for this at http://en.wikipedia.org/wiki/Ecological_fallacy#Simpson.27s_...