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Robin Hanson's avatar

JMG3Y, as Hal notes simple attempts to "debias" usually fail. But anytime someone uses statistical techniques to draw a conclusion, they are implicitly acknowledging that just eye-balling the data would be biased. I'd call that a typically successful attempt to overcome bias.

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Overcoming Bias Commenter's avatar

JMG3Y, there has been a great deal of research on "debiasing", attempts to reduce various perceptual and judgmental biases in different ways. I've looked at a few of these papers, and it seems that the consensus is that debiasing is extremely difficult and usually doesn't work. However, it is not usually done simply by explaining the reality of Bayesian inference or probability theory, then turning people lose on problems. Rather, various tricks are used, such as getting them to consider alternatives, or imagine themselves in certain scenarios, or rewording the problems to try to reduce biasing effects. And as I said, usually these don't help much.

Tetlock told an amusing story of his debiasing experiment that backfired, in his book I reviewed earlier. He attempted to get participants to explicitly consider a wide range of alternative scenarios in making a forecast, to try to overcome a common bias of focusing too soon in analysis. But his single-minded "hedgehogs" refused to take the scenarios seriously since they thought they already knew exactly what was going to happen; their scores didn't change. And his open-minded "foxes" wasted so much time delightedly exploring the intricacies of the new scenarios that they lost track of the bigger picture and ended up doing worse in the exercises.

In general there seems to be something of an unstated assumption that just teaching people Bayesian decision theory would be uselessly abstract; I don't know if this is due to earlier failed experiments, or perhaps reflects experimenters' judgment that the theory is too complex for average subjects to grasp.

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