Base rate fallacy

Base rate fallacy, which is also called base rate neglect, is a social science theory about why humans do poorly at predicting or understanding the probability of a certain event. When people engage in base rate neglect, they tend not to consider all the facts of a situation and end up believing in a fallacy. They often focus on particular facts and not on what is broadly true about a situation. For example, many people have a fear of flying on airplanes, but they have much less fear of driving in a car. In reality, many more people die in traffic accidents than in aviation accidents. People who fear airplane crashes often notice such accidents reported in the media, as these large-scale events tend to receive more coverage than the numerous traffic accidents that happen every day. People who are more afraid of flying than riding in cars ignore the basic information about the number of deaths attributed to each type of accident each year.

Background

Base rate fallacy is closely related to probability. Probability is the likelihood of an outcome or an event happening. For example, think of a coin flip. Each coin flip, if done correctly, is completely random. Since the coin has only two sides and the outcome is random, the probability that a person will flip heads is 50 percent, and the probability of tails is 50 percent. Understanding the probability of a coin flip is simple because the flip does not rely on any variables. However, many events in life are affected by variables. Conditional probability is the likelihood of an outcome or an event happening when certain variables are present. For example, a gardener wants to know the probability of having two bushes with purple flowers. The gardener knows that at least one bush has purple flowers. It might seem as though the gardener has a 50 percent chance of having two purple bushes. However, since the gardener knows one bush has purple flowers, the probability of having two purple bushes is actually 33 percent. People’s misunderstanding of probability and conditional probability makes base rate fallacy more likely.

Overview

Scientists have researched people’s base rate assumptions for decades. One early prominent study was conducted in the 1970s by two Israeli scientists. The study indicated that people were likely to ignore data given to them at the beginning of the experiment that would help them determine the probability of various outcomes. Other scientific studies have similarly found people ignore or misunderstand probability when they make assumptions about what will happen. For example, consider traffic accidents and aviation accidents. In 2018, about 36,560 people died in traffic accidents in the United States. In 2018, only one person died in a commercial aviation accident. Some people may have a base rate fallacy simply because they are unaware of the true statistics and will change their views once they know the probability. Nevertheless, people’s experiences and personal beliefs may also cause them to question the base rate, even if they know the statistical probability of an event. For example, a person who has flown a plane that has had to make an emergency landing might not trust aviation travel, even though that person understands the statistical improbability of a crash or other airplane safety issue.

Base rate fallacy is more likely to take place when people need to determine conditional probability. That is because conditional probably requires people to understand numerous variables. For example, think about Student A deciding whether to buy lunch in the cafeteria. Student A and Student B attend school together. Student A likes to buy lunch when the cafeteria serves oranges, not apples. The cafeteria serves apples 90 percent of the time and oranges 10 percent of the time during a 100-day school year. Student A cannot remember which type of fruit will be served on a particular day and asks Student B. Student B says that oranges will be served with lunch. Student A knows—from asking Student B the same question previously—that Student B is correct about the fruit being served 80 percent of the time. Student A feels confident about Student B’s answer since she answers correctly 80 percent of the time, so student A plans to buy lunch.

In this scenario, Student A has taken part in base rate fallacy. Using mathematical formulas to determine the probability may help clarify such situations. Student A assumed that Student B was correct because she is correct 80 percent of the time. Nevertheless, using math to determine the probability shows that Student A neglected the base rate. The cafeteria serves apples 90 percent of the time, or 90 days out of the 100-day school year. Student A also knows that the cafeteria will serve oranges 10 percent of the time, or 10 days of the year. If Student B is correct in guessing 80 percent of the time, she correctly identified 72 days and incorrectly identified 18 days when the cafeteria served apples. Also, Student B correctly identified eight days and incorrectly identified two days when the cafeteria served oranges. So, Student B incorrectly identified apple days 18 times and correctly identified orange days only 8 times. Based on the mathematical probability, Student A should not trust Student B’s guess. Based on the data, Student A should most likely assume the cafeteria will serve apples.

The persistence of base rate fallacy affects many different aspects of people’s lives, with stakes that are often higher than choosing whether to purchase lunch in a cafeteria. Two sectors concerned with base rate fallacy are finance and insurance. People involved in finance could lose money if they invest in a failing scenario. Similarly, insurance companies have to be aware of how likely events that require insurance payouts are to happen. Science and medicine are also fields that are commonly affected by base rate fallacy. For example, a doctor may believe a certain treatment works well based only on anecdotal information and one patient who saw positive results. Furthermore, doctors can fall into the base rate fallacy when they are presented data but do not fully understand the probability because of numerous variables.

Bibliography

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