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A fallacy is an error in reasoning, usually based on mistaken assumptions. Researchers are very familiar with all the ways they could go wrong, with the fallacies they are susceptible to. Here, I discuss two of the most important.

The ecological fallacy occurs when you make conclusions about individuals based only on analyses of group data. For instance, assume that you measured the math scores of a particular classroom and found that they had the highest average score in the district. Later (probably at the mall) you run into one of the kids from that class and you think to yourself "she must be a math whiz." Aha! Fallacy! Just because she comes from the class with the highest average doesn't mean that she is automatically a high-scorer in math. She could be the lowest math scorer in a class that otherwise consists of math geniuses!

An exception fallacy is sort of the reverse of the ecological fallacy. It occurs when you reach a group conclusion on the basis of exceptional cases. This is the kind of fallacious reasoning that is at the core of a lot of sexism and racism. The stereotype is of the guy who sees a woman make a driving error and concludes that "women are terrible drivers." Wrong! Fallacy!

Both of these fallacies point to some of the traps that exist in both research and everyday reasoning. They also point out how important it is that we do research. We need to determine empirically how individuals perform (not just rely on group averages). Similarly, we need to look at whether there are correlations between certain behaviors and certain groups (you might look at the whole controversy around the book The Bell Curve as an attempt to examine whether the supposed relationship between race and IQ is real or a fallacy.

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