The base rate fallacy is a cognitive bias that occurs when people make judgments about the likelihood of an event based on limited information, rather than considering the full context and all relevant information. This bias can lead people to make incorrect conclusions and decisions, and it is often encountered in situations where probability and statistics are involved.

One common example of the base rate fallacy is when people estimate the probability of an event based on a small sample, rather than the larger population from which the sample is drawn. For example, if a person is told that a particular disease is rare, they may assume that an individual with that disease is unlikely to have it, even if the base rate of the disease in the population is actually quite high.

The base rate fallacy can also occur when people are presented with information that is disproportionately weighted or emphasized. For instance, if a person is shown a series of news stories about a particular crime, they may overestimate the frequency of that crime, even if it is actually quite rare. Similarly, if a person is given a list of characteristics that are associated with a particular group of people, they may overestimate the prevalence of those characteristics in the group, even if most members of the group do not possess them.

To avoid the base rate fallacy, it is important to consider all relevant information and to look at the big picture, rather than relying on limited or biased data. This may require seeking out additional information, such as looking at statistics or consulting with experts, to get a more accurate understanding of the situation. By being mindful of this bias, people can make more informed and accurate judgments and decisions.