(source, cartoon by Eric Allie)
In a previous post in this series, I already mentioned the temptation to see things in data that just aren’t there, or to make data say things they don’t really say. I focused on the correlation-causation problem, a typical case of “jumping to conclusions”.
Elsewhere I gave the following example: there are data doing the rounds claiming that Republicans follow political news more closely than Democrats, which has some people saying that Republicans are more knowledgable and make better political choices. However, people don’t read more news because they are Republicans, but because they are relatively wealthy and older, and when they are they also tend to be more of the Republican type. So if you see data showing a correlation between political conservatism and attention to the news, don’t jump to conclusions and say that conservatives are inherently more attentive to the news, let alone that they make better political choices. A young and relatively poor conservative probably pays less attention than a wealthy and older liberal. Attention isn’t a function of political orientation. It has other causes.
However, as is evident from the cartoon above, data don’t have to be of the correlation type for people to see things in them that aren’t there. People have indeed interpreted popular rejection of healthcare reform or of the Obama administration in general as an expression of underlying racism, as if there can’t be any other reasons for rejection.*
Polling on the health-care bill is … complicated. Voters don’t know much about the plan. Most disapprove of it, but many disapprove because they want to see it go further. (source)
So there’s a “double jump” to conclusions in the cartoon:
- First, jumping from disapproval of healthcare reform to anti-Obama racism (blaming the former on the latter when this isn’t shown by the data), which is ridiculed, rightly to the extent that it is something real.
- Second, jumping from disapproval ratings on “something” to disapproval ratings on “healthcare reform”. The data only show that people disapprove of “something”: people may disapprove of only a part of healthcare reform, or may disapprove of the fact that it doesn’t go far enough rather than disapprove of reform as such; or they may disapprove of something that is not really proposed and hence misunderstand the whole thing and base their disapproval on lack of knowledge. Needless to say, this second jump in the cartoon is quite unconscious and probably not on purpose.
All this jumping is quite understandable. We always have to interpret data, and we can easily lose our way in the process. It’s also tempting to “find” explanations for data that fit with our pre-established opinions and biases.
* Personally, I’m in favor of reform.