Freeman Dyson reviews Thinking, Fast and Slow by Daniel Kahneman, in the New York Review of Books:
In 1955, when Daniel Kahneman was twenty-one years old, he was a lieutenant in the Israeli Defense Forces. He was given the job of setting up a new interview system for the entire army. The purpose was to evaluate each freshly drafted recruit and put him or her into the appropriate slot in the war machine. The interviewers were supposed to predict who would do well in the infantry or the artillery or the tank corps or the various other branches of the army. The old interview system, before Kahneman arrived, was informal. The interviewers chatted with the recruit for fifteen minutes and then came to a decision based on the conversation. The system had failed miserably. When the actual performance of the recruit a few months later was compared with the performance predicted by the interviewers, the correlation between actual and predicted performance was zero.
Kahneman had a bachelor’s degree in psychology and had read a book, Clinical vs. Statistical Prediction: A Theoretical Analysis and a Review of the Evidence by Paul Meehl, published only a year earlier. Meehl was an American psychologist who studied the successes and failures of predictions in many different settings. He found overwhelming evidence for a disturbing conclusion. Predictions based on simple statistical scoring were generally more accurate than predictions based on expert judgment.
A famous example confirming Meehl’s conclusion is the “Apgar score,” invented by the anesthesiologist Virginia Apgar in 1953 to guide the treatment of newborn babies. The Apgar score is a simple formula based on five vital signs that can be measured quickly: heart rate, breathing, reflexes, muscle tone, and color. It does better than the average doctor in deciding whether the baby needs immediate help. It is now used everywhere and saves the lives of thousands of babies.