Base Rate Neglect

CAN PSYCHOLOGY BE TAUGHT?

The reckless cabdrivers and the impossibly difficult exam illustrate two inferences that people can draw from causal base rates: a stereotypical trait that is attributed to an individual, and a significant feature of the situation that affects an individual’s outcome. The participants in the experiments made the correct inferences and their judgments improved. Unfortunately, things do not always work out so well. 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 was conducted a long time ago by the social psychologist Richard Nisbett and his student Eugene Borgida, at the University of Michigan. They told students about the renowned “helping experiment” that had been conducted a few years earlier at New York University. Participants in that experiment were led to individual booths and invited to speak over the intercom about their personal lives and problems. They were to talk in turn for about two minutes. Only one microphone was active at any one time. There were six participants in each group, one of whom was a stooge. The stooge spoke first, following a script prepared by the experimenters. He described his problems adjusting to New York and admitted with obvious embarrassment that he was prone to seizures, especially when stressed. All the participants then had a turn. When the microphone was again turned over to the stooge, he became agitated and incoherent, said he felt a seizure coming on, and asked for someone to help him. The last words heard from him were, “C-could somebody-er-er-help-er-uh-uh-uh [choking sounds]. I…I’m gonna die-er-er-er I’m…gonna die-er-er-I seizure I-er [chokes, then quiet].” At this point the microphone of the next participant automatically became active, and nothing more was heard from the possibly dying individual.

What do you think the participants in the experiment did? So far as the participants knew, one of them was having a seizure and had asked for help. However, there were several other people who could possibly respond, so perhaps one could stay safely in one’s booth. These were the results: only four of the fifteen participants responded immediately to the appeal for help. Six never got out of their booth, and five others came out only well after the “seizure victim” apparently choked. The experiment shows that individuals feel relieved of responsibility when they know that others have heard the same request for help.

Did the results surprise you? Very probably. 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. Even normal, decent people do not rush to help when they expect others to take on the unpleasantness of dealing with a seizure. And that means you, too.

Are you willing to endorse the following statement? “When I read the procedure of the helping experiment I thought I would come to the stranger’s help immediately, as I probably would if I found myself alone with a seizure victim. I was probably wrong. If I find myself in a situation in which other people have an opportunity to help, I might not step forward. The presence of others would reduce my sense of personal responsibility more than I initially thought.” This is what a teacher of psychology would hope you would learn. Would you have made the same inferences by yourself?

The psychology professor who describes the helping experiment wants the students to view the low base rate as causal, just as in the case of the fictitious Yale exam. He wants them to infer, in both cases, that a surprisingly high rate of failure implies a very difficult test. The lesson students are meant to take away is that some potent feature of the situation, such as the diffusion of responsibility, induces normal and decent people such as them to behave in a surprisingly unhelpful way.

Changing one’s mind about human nature is hard work, and changing one’s mind for the worse about oneself is even harder. Nisbett and Borgida suspected that students would resist the work and the unpleasantness. Of course, the students would be able and willing to recite the details of the helping experiment on a test, and would even repeat the “official” interpretation in terms of diffusion of responsibility. But did their beliefs about human nature really change? To find out, Nisbett and Borgida showed them videos of brief interviews allegedly conducted with two people who had participated in the New York study. The interviews were short and bland. The interviewees appeared to be nice, normal, decent people. They described their hobbies, their spare-time activities, and their plans for the future, which were entirely conventional. After watching the video of an interview, the students guessed how quickly that particular person had come to the aid of the stricken stranger.

To apply Bayesian reasoning to the task the students were assigned, you should first ask yourself what you would have guessed about the two individuals if you had not seen their interviews. This question is answered by consulting the base rate. We have been told that only 4 of the 15 participants in the experiment rushed to help after the first request. The probability that an unidentified participant had been immediately helpful is therefore 27%. Thus your prior belief about any unspecified participant should be that he did not rush to help. Next, Bayesian logic requires you to adjust your judgment in light of any relevant information about the individual. However, the videos were carefully designed to be uninformative; they provided no reason to suspect that the individuals would be either more or less helpful than a randomly chosen student. In the absence of useful new information, the Bayesian solution is to stay with the base rates.

Nisbett and Borgida asked two groups of students to watch the videos and predict the behavior of the two individuals. The students in the first group were told only about the procedure of the helping experiment, not about its results. Their predictions reflected their views of human nature and their understanding of the situation. As you might expect, they predicted that both individuals would immediately rush to the victim’s aid. The second group of students knew both the procedure of the experiment and its results. The comparison of the predictions of the two groups provides an answer to a significant question: Did students learn from the results of the helping experiment anything that significantly changed their way of thinking? The answer is straightforward: they learned nothing at all. Their predictions about the two individuals were indistinguishable from the predictions made by students who had not been exposed to the statistical results of the experiment. They knew the base rate in the group from which the individuals had been drawn, but they remained convinced that the people they saw on the video had been quick to help the stricken stranger.

For teachers of psychology, the implications of this study are disheartening. When we teach our students about the behavior of people in the helping experiment, we expect them to learn something they had not known before; we wish to change how they think about people’s behavior in a particular situation. This goal was not accomplished in the Nisbett-Borgida study, and there is no reason to believe that the results would have been different if they had chosen another surprising psychological experiment. Indeed, Nisbett and Borgida reported similar findings in teaching another study, in which mild social pressure caused people to accept much more painful electric shocks than most of us (and them) would have expected. Students who do not develop a new appreciation for the power of social setting have learned nothing of value from the experiment. The predictions they make about random strangers, or about their own behavior, indicate that they have not changed their view of how they would have behaved. In the words of Nisbett and Borgida, students “quietly exempt themselves” (and their friends and acquaintances) from the conclusions of experiments that surprise them.

The Pervasiveness of Base-Rate Neglect

This failure to incorporate base rates—known as base rate neglect (or the base rate fallacy)—is not confined to the classroom; it appears repeatedly across many domains of professional judgment and everyday reasoning. The bias occurs when we disregard the overall prevalence of an event (the base rate) and instead give too much weight to specific, often vivid, case information. A classic illustration comes from a Star Trek exchange: Spock states the odds of the transporter working properly are 99.7 to 1 against, yet the reply is “The transporter works fine. They’re fine.” Despite the overwhelming base rate of failure, the anecdotal outcome (they turned out fine) feels more compelling, and the base rate is ignored. That same cognitive move—overweighting the individual instance at the expense of the background frequency—explains the students’ predictions about the helpful strangers.

Considerable research has demonstrated that people tend to neglect prior probabilities whenever specific case information is available, even if that information is uninformative. In one simple demonstration, students were asked to imagine that they had three different foreign coins, each with a known bias of 3:2 in favor of either heads or tails. The prior probability that the bias favored heads was varied across coins: for some students it was .50, .67, and .90; for others .50, .33, and .10. After seeing samples of 10 coin flips containing between 5 and 9 heads, students judged the probability that the coin was biased toward heads. Time and again they overweighted the specific sample evidence and underweighted the prior probability. Even when prior odds strongly favored one hypothesis, a modest run of heads shifted their confidence dramatically—the same insensitivity to base rates shown by the students watching bland videos of individuals from a group with a known low rate of helping.

Medical decision making provides a sobering real-world illustration. Since the early 1980s, research has consistently found that physicians’ prognostic and diagnostic judgments reflect marked neglect of base rates. For example, when predicting survival for patients with different underlying mortality rates, physicians show underprediction (overly pessimistic forecasts) when the base survival rate is very high, and overprediction (overly optimistic forecasts) when the base rate is low. This pattern cannot be attributed to poor discriminability, lack of feedback, or mere ignorance—physicians are generally accurate when asked directly about prevalence rates. In one classic study, Eddy (1982) documented how physicians systematically failed to use known base rates when interpreting test results. Further work has shown that even in settings with clear, unambiguous event‑based feedback, physicians’ calibration does not align with ecological learning theories. When base rates varied across locations—such as pneumonia diagnoses in Illinois (11% base rate) versus Nebraska (32% base rate)—the more extreme base rate led to greater overprediction, the opposite of what a simple learning-from-experience account would predict.

The direct support model helps explain these findings: judgments are anchored to the specific evidence in mind, with insufficient adjustment toward the base rate. This is consistent with observations that physicians’ forecasts correlate with the availability of recent cases rather than with overall prevalence, and that personal experience with a disease actually increases overprediction rather than improving calibration. Even patients show parallel biases: seriously ill patients with a low survival base rate tend to be overoptimistic relative to their physicians, while physicians themselves are well calibrated only in intermediate base-rate domains. Across all these studies, the pattern of over- and underprediction is strongly linked to the extremity of the base rate, exactly as the neglect model predicts.

Why It Happens

Several cognitive shortcuts underlie base rate neglect:

• Representativeness heuristic: We judge probability by how similar an event is to a stereotype rather than by statistical frequency. A nice, normal-looking person does not resemble someone who would ignore a seizure, so we assume the base rate does not apply to them.
• Availability heuristic: Vivid, memorable examples (the bland video, the person who did help, the last patient we saw) overshadow dry statistics. Case information feels tangible, while base rates feel abstract.
• Cognitive overload and anchoring: It is easier to focus on a single story than to integrate complex probabilities. Our judgment is anchored to the specific details in front of us, and we adjust only slightly toward the base rate, if at all.

These mental moves are automatic and often go unnoticed, which is why simply knowing a base rate is no guarantee it will be used.

Avoiding the Bias

Awareness alone is insufficient; deliberate strategies can help. The most direct remedy is to consistently ask yourself, “What is the chance of this happening in the general population?” before allowing any case-specific information to influence your judgment. Whenever possible, use Bayesian reasoning to formally combine the prior probability (the base rate) with the new evidence. In practice, this means starting from the base rate and only adjusting your estimate to the extent that the new information is both relevant and diagnostic. When the case details are uninformative—as in the video interviews—the correct adjustment is zero, and you should stay with the base rate.

Why Surprise Matters—and What Kind

Teachers of psychology should not despair, however, because Nisbett and Borgida report a way to make their students appreciate the point of the helping experiment. They took a new group of students and taught them the procedure of the experiment but did not tell them the group results. They showed the two videos and simply told their students that the two individuals they had just seen had not helped the stranger, then asked them to guess the global results. The outcome was dramatic: the students’ guesses were extremely accurate.

To teach students any psychology they did not know before, you must surprise them. But which surprise will do? Nisbett and Borgida found that when they presented their students with a surprising statistical fact, the students managed to learn nothing at all. But when the students were surprised by individual cases—two nice people who had not helped—they immediately made the generalization and inferred that helping is more difficult than they had thought. 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.

This is a profoundly important conclusion. People who are taught surprising statistical facts about human behavior may be impressed to the point of telling their friends about what they have heard, but this does not mean that their understanding of the world has really changed. The test of learning psychology is whether your understanding of situations you encounter has changed, not whether you have learned a new fact. There is a deep gap between our thinking about statistics and our thinking about individual cases. 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. On the other hand, surprising individual cases have a powerful impact and are a more effective tool for teaching psychology because the incongruity must be resolved and embedded in a causal story. That is why this book contains questions that are addressed personally to the reader. You are more likely to learn something by finding surprises in your own behavior than by hearing surprising facts about people in general.