154 Teaching Minds look like a genius. But what actually makes us feel that a scientist’s accurate predictions make him smart is the reasoning behind those predictions, the causal explanation. We can see how intelligence, or the lack of it, is perceived by people and we must begin to reconsider how intelligence should be measured by those trying to put numbers to mental abilities. And, we can see why those Palin supporters seem so dumb. Let’s look at one of them again: Interviewer: What do you think she would bring in terms of policy to office? Young woman: Good judgment. Interviewer: Any specifics? Young woman: I think she would control the out-of-control spending. This is a prediction. The question is what this prediction is based on. It is a good guess that the young woman cannot cite examples of Palin’s good judgment and has no idea whether Palin was able to control spending in Alaska. If she were able to cite examples, that is, if her predictions were supported by evidence that she clearly articulated, we would, in fact, think that the young woman was smart. Perhaps she is smart and perhaps the interviewer deliberately cut out those responses. It seems unlikely, given the weird “czar” remark that fol- lowed this, but the point is that we seek such evidence when we make a judgment about someone’s intelligence. What about planning? Those who make bad plans are usually laughed at. Criminals who get caught by doing something dumb are always made fun of by the press. Bad planning makes a person look stupid. Bad judgment, on the other hand, is more easily forgiven. When you make a mistake, you can always claim to have used bad judgment. Make the same mistake again and you begin to look stupid. So, if we are interested in making people more intelligent, as op- posed to more knowledgeable, it is clear that we need to redefine what we mean by intelligence. Intelligence is the ability to diagnose well, to plan well, and to be able to understand what causes wh