To Make Better Predictions, Don’t Stick With The Easy Stuff

NPR:

The presidential primaries are a great opportunity to test your skills in political prediction. Who will win which states, and by what margin? And if your predictions aren’t all that good, how can you do better?

Two weeks ago, I wrote about how to make better predictions in domains both big (world politics) and small (your breakfast), drawing on recent work in psychological science. Today, I’m going to revisit this question from a new angle.

Making good predictions isn’t just about your accuracy; it’s also about your calibration.

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The paper, by psychologists Joyce Ehrlinger, Ainsley Mitchum and Carol Dweck, reports three studies in which participants were asked to estimate their own performance on a task, either a multiple choice test with antonym problems or a multiple choice general knowledge quiz. The participants were asked to estimate their percentile relative to other students completing the task, from zero percent (worse than all other students) to 100 percent (better than all other students). If participants were perfectly calibrated, then the average percentile estimate should have been 50 percent. But that’s not what they found. In Study 1, for instance, the average was 66 percent. Like the children of Lake Wobegon, participants (on average) believed themselves to be better than average.

Read the whole story: NPR