Thinking, Fast and Slow

by Daniel Kahneman

System 1 can deal with stories in which the elements are causally linked, but it is weak in statistical reasoning.

The classic experiment I describe next shows that people will not draw from base-rate information an inference that conflicts with other beliefs. It also supports the uncomfortable conclusion that teaching psychology is mostly a waste of time.

The experiment shows that individuals feel relieved of responsibility when they know that others have heard the same request for help.

Most of us think of ourselves as decent people who would rush to help in such a situation, and we expect other decent people to do the same. The point of the experiment, of course, was to show that this expectation is wrong.

Changing one’s mind about human nature is hard work, and changing one’s mind for the worse about oneself is even harder.

Students who do not develop a new appreciation for the power of social setting have learned nothing of value from the experiment.

In the words of Nisbett and Borgida, students “quietly exempt themselves” (and their friends and acquaintances) from the conclusions of experiments that surprise them.

To teach students any psychology they did not know before, you must surprise them.

Nisbett and Borgida summarize the results in a memorable sentence: Subjects’ unwillingness to deduce the particular from the general was matched only by their willingness to infer the general from the particular.

The test of learning psychology is whether your understanding of situations you encounter has changed, not whether you have learned a new fact.

Statistical results with a causal interpretation have a stronger effect on our thinking than noncausal information. But even compelling causal statistics will not change long-held beliefs or beliefs rooted in personal experience.

You are more likely to learn something by finding surprises in your own behavior than by hearing surprising facts about people in general.

an important principle of skill training: rewards for improved performance work better than punishment of mistakes.

What he had observed is known as regression to the mean, which in that case was due to random fluctuations in the quality of performance.

The instructor had attached a causal interpretation to the inevitable fluctuations of a random process.

I had stumbled onto a significant fact of the human condition: the feedback to which life exposes us is perverse.

regression does not have a causal explanation.

The point to remember is that the change from the first to the second jump does not need a causal explanation. It is a mathematically inevitable consequence of the fact that luck played a role in the outcome of the first jump.

the phenomenon of regression is strange to the human mind. So strange, indeed, that it was first identified and understood two hundred years after the theory of gravitation and differential calculus.

Regression effects can be found wherever we look, but we do not recognize them for what they are.

The correlation coefficient between two measures, which varies between 0 and 1, is a measure of the relative weight of the factors they share.

The correlation between income and education level in the United States is approximately .40.

It took Francis Galton several years to figure out that correlation and regression are not two concepts—they are different perspectives on the same concept. The general rule is straightforward but has surprising consequences: whenever the correlation between two scores is imperfect, there will be regression to the mean.

The observed regression to the mean cannot be more interesting or more explainable than the imperfect correlation.

David Freedman used to say that if the topic of regression comes up in a criminal or civil trial, the side that must explain regression to the jury will lose the case.

our mind is strongly biased toward causal explanations and does not deal well with “mere statistics.”

Our difficulties with the concept of regression originate with both System 1 and System 2. Without special instruction, and in quite a few cases even after some statistical instruction, the relationship between correlation and regression remains obscure. System 2 finds it difficult to understand and learn. This is due in part to the insistent demand for causal interpretations, which is a feature of System 1.

The control group is expected to improve by regression alone, and the aim of the experiment is to determine whether the treated patients improve more than regression can explain.

Regression effects are a common source of trouble in research, and experienced scientists develop a healthy fear of the trap of unwarranted causal inference.

Some intuitions draw primarily on skill and expertise acquired by repeated experience. The rapid and automatic judgments and choices of chess masters, fireground commanders, and physicians that Gary Klein has described in Sources of Power and elsewhere illustrate these skilled intuitions, in which a solution to the current problem comes to mind quickly because familiar cues are recognized.

Other intuitions, which are sometimes subjectively indistinguishable from the first, arise from the operation of heuristics that often substitute an easy question for the harder one that was asked.

Of course, many judgments, especially in the professional domain, are influenced by a combination of analysis and intuition.

We are capable of rejecting information as irrelevant or false, but adjusting for smaller weaknesses in the evidence is not something that System 1 can do.

intuitive predictions are almost completely insensitive to the actual predictive quality of the evidence.

your associative memory quickly and automatically constructs the best possible story from...

People are asked for a prediction but they substitute an evaluation of the evidence, without noticing that the question they answer is not the one they were asked. This process is guaranteed to generate predictions that are systematically biased; they completely ignore regression to the mean.

we have all we need to produce an unbiased prediction. Here are the directions for how to get there in four simple steps: Start with an estimate of average GPA. Determine the GPA that matches your impression of the evidence. Estimate the correlation between your evidence and GPA. If the correlation is .30, move 30% of the distance from the average to the matching GPA.

Step 3 moves you from the baseline toward your intuition, but the distance you are allowed to move depends on your estimate of the correlation.

The approach builds on your intuition, but it moderates it, regresses it toward the mean.

Intuitive predictions need to be corrected because they are not regressive and therefore are biased.

When they are eventually compared to actual outcomes, nonregressive predictions will be found to be biased.

You still make errors when your predictions are unbiased, but the errors are smaller and do not favor either high or low outcomes.

common biases of discrete prediction: neglect of base rates and insensitivity to the quality of information.

Correcting your intuitive predictions is a task for System 2. Significant effort is required to find the relevant reference category, estimate the baseline prediction, and evaluate the quality of the evidence. The effort is justified only when the stakes are high and when you are particularly keen not to make mistakes.

A characteristic of unbiased predictions is that they permit the prediction of rare or extreme events only when the information is very good.

A preference for unbiased predictions is justified if all errors of prediction are treated alike, regardless of their direction. But there are situations in which one type of error is much worse than another.

For a rational person, predictions that are unbiased and moderate should not present a problem.

we are not all rational, and some of us may need the security of distorted estimates to avoid paralysis.

Following our intuitions is more natural, and somehow more pleasant, than acting against them.

We will not learn to understand regression from experience. Even when a regression is identified, as we saw in the story of the flight instructors, it will be given a causal interpretation that is almost always wrong.