Heuristics Representativeness

What are Heuristics?

People rely on heuristics because they facilitate the task of assessing probabilities and predicting values; they allow us to make decisions quickly and instinctually. Although heuristics like schemas are often inaccurate, people look for evidence of that the heuristic or schema is true and ignore failures of judgment (Tversky and Kahneman, 1974). Heuristic errors are known as systematic errors, and they occur because the heuristic cannot cope with the complexity of the task. Heuristics simply lack the validity.


Representativeness is when probabilities of B are evaluated by how much it resembles A, taking for granted the degree to which A and B are related (Tversky and Kahneman, 1974). Usually, representativeness heuristics are quite accurate because if A resembles B, there is a likelihood that they are somehow related. Unfortunately, similarities can be misleading as they are influenced by factors, which need to be taken in considering when judging probability.  Factors that influence similarity include: prior probability outcomes, sample size and chance. 

– Insensitivity to Prior Probability Outcomes

A major effect on probability is base-rate frequency. For example, even though Steve has the characteristics of a librarian compared to a farmer, the fact that there are many more farmers than librarians in his population needs to be taken into account when assessing the likelihood of him having one occupation over the other. If in Steve’s population there are a lot of farmers because of the rich soil in his area, the base-rate frequency suggests that Steve is more likely to be a farmer than a librarian.

In 1973, Kahneman and Tversky conducted an experiment to show how often people overlook base-rate frequency when assessing probability outcomes. Participants were shown short personality descriptions of several individuals sampled from 100 people. The 100 people consisted of lawyers and engineers, and the experiment task was to assess which people were likely to be lawyers or engineers. In condition A, participants were given the following base rate, 70 engineers and 30 lawyers. In condition B, the base rate was 30 engineers and 70 lawyers. The two conditions produced virtually the same probability judgements despite the significantly different base rate probabilities clearly given to the participants. Participants only used the base rate probabilities when they were given without personality descriptions.

Goodie and Fantino (1996) also studied base-rate frequency. Participants were asked to determine the probability that a taxi seen by a witness was blue or green. Even though participants were give the base-rate of taxi colours in the city, participants still determined probability by the reliability of the witnesses.

– Insensitivity to Sample Size

Another major effect on probability is sample size. The similarity of sample statistic to a population parameter does not depend on size; therefore, if probabilities are assessed by representativeness, the probability is independent of sample size. Tversky and Kahneman (1974) conducted a participant to show evidence of insensitivity to sample size. Participants were given the following information:

–       There are two hospitals in a town, one small and one larger

–       About 45 babies are born per day at the large hospital

–       About 15 babies are born per day at the small one

–       50% of the babies born are boys, but this figure differs slightly everyday

Participants were then asked which hospital is more likely to report a day with 60 male births. Most of the participants answered that the hospitals are equally likely to have 60 births. However, sample theory warrants that the small hospital would be more likely because the larger hospital is less likely to stray from the mean.


Unfortunately, the general public are not the only ones to fall victim to sample size. In 1971, Tversky and Kahneman conducted a meta-analysis on experienced research psychology, the majority stood by the validity of small group sizes. The researches simply put too much faith in their results from small sample sizes, underestimating the high chance of representativeness. It is likely that the reason the benefits of a large sample size are drilled into psychology students from day one is to avoid errors like this.

– Misconceptions of Chance

People expect that a randomly generated sequence of events will represent the essential characteristics of that process even when the sequence is short. In other words, people thing that the likelihood of getting H-T-H-T-T-H is more likely than H-H-H-H-H-H even though they are equally likely. This because every T or H has to be assessed as an individual probability event. In other words, in trail one, you have a 50% chance of getting a T or an H. In the second trail, the results of the first have no impact; therefore, you again have a 50% chance to get either letter. Probability matching is another word for this misconception of chance. Andrade and May (2004) describe another scenario based on real life misconceptions of chance.

First, participants are given a jar of 100 balls and told that 80% are white and 20% are white. The most commonly observed strategy when asked what ball colour will become next is one that imitates the pattern of 20% white and 20% red. In reality, the most efficient strategy is to say red for every draw because the probability event, as stated above, needs to be assessed for each individual draw not for the task as a whole. The implications of probability in gambling are huge, so it is not surprising that the gambler’s fallacy is another name for probability matching. People simply believe in the “law of averages,” that if an event has occurred less often that probability suggests, it is more likely to occur in the future.

– Insensitivity to Predictability

Another issue with probability is insensitivity to predictability, which is when people ignore the reliability of a description and instead pay attention to in related factors. For example, a person will pay more attention to a particular review and given greater reliability if the person’s name is the same as yours. Another example would be ignoring negative reviews and only paying attention to positive ones because they confirm your own belief. Obviously, doing so means disregarding the reliability of the evidence.


Tversky and Kahneman conducted an experiment in 1973 in which participants were given several descriptions of the performance of student teacher during a particular lesson. Some participants were asked to evaluate the quality of lesson described into percentile scores, and other participants were asked to predict the standing of the student teacher five years after the practice lessons. The judgments of the second group were based on the other participants’ evaluations. Even though the participants were aware of the limited predictability of judging a person’s performance five years into the future, they expressed high confidence in judging the student teacher’s performance to be identical to now. Sadly, high confidence in the face of poor judgment of probability is common and known as the illusion of validity. Confidence displayed by people in their predictions usually depends on representativeness and regard for other factor is usually ignored; the illusion persists even when a person is aware of the limited accuracy of prediction (ibid).

–  Misconceptions of Regression

People simply do expect regression to occur even in contexts where it is common (Tversky and Kahneman, 1974). A good example of regression towards the mean is with height; two above average height parents are more likely to have a child of average height than above average height. Despite this fact, people tend to dismiss regression because it is inconsistent with their beliefs. Failure to accept the truth; however, leads to overestimation of the effectiveness of punishment and the underestimation of the effectiveness of reward.

– Implicature

Implicature refers to what a sentence suggests rather than what is literally said (Blackburn, 1996). For example, the sentence “I showered and went to bed” implies that first I showered and then I went to bed; however, if you take the sentence literally, I could mean I went to bed and then I showered in the morning. Both possibilities are true, but given the context it would be strange for me not mean that I showered before going to sleep. Sometimes a qualification is added, which adds new information to the context. Even if the sentence is not altered itself, a qualification clarifies our implication. An example of a qualification would, in that exact order: “I showered and went to bed, in that exact order.”

– The Conjunction Fallacy

The conjunction fallacy, first outlined by Tversky and Kahneman in 1983, refers to tendency to believe that two events are more likely to occur together rather than independently. The example provided by Tversky and Kahneman is as follows:

Linda is a bank teller question is a good example:

“Linda is 31 years old, single, outspoken, and very bright. She studied philosophy at University. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-war demonstrations.

Which is more likely?

A) Linda is a Bank Teller

B) Linda is a Bank Teller and is active in the feminist movement.”

The results showed that people chose B more often than A because they see the information above as indicative of Linda’s personality, and B seems like a better fit for her as a person. The truth is than A and B do not have the same likelihood because there is no way of knowing if she is a feminist or not. Linda is a bank teller is obviously a fact. Regardless of that, the general public as well as statistical experts still rely on the representative heuristic, ignoring the probability a play.


People tend be overly confident in their estimates even when they are pointed out the irrationality of their thinking (Fischhoff et al. 1977). Baron (1994) found evidence that one reason for our inappropriately high confidence our tendency not to search for reasons why we might be wrong. As ridiculous as this may seem, studies consistently confirm that people ignore new information to hold on to their original belief. Weinstein (1989) reported a study where racetrack handicappers were slowly given more and more information about the outcomes of the race. Despite becoming more informed, the participants held onto their original belief with more confidence. DeBondt and Thaler (1986) propose that when new information arrives, investors revise their beliefs by overweighting the new information and underweighting the earlier information.