Which bias occurs when individuals do not accurately factor in the actual base rates in their assessments?

The correct answer relates to representativeness bias, which refers to the tendency of individuals to judge the probability of an event based on how similar it is to a prototype or stereotype they already have in mind rather than considering the actual statistical base rates of that event. When people exhibit this bias, they might overlook important information about the general frequency of events—what statisticians refer to as "base rates." This can lead to misjudgments in assessing the likelihood of occurrences because the individual’s mental representation does not align with the actual statistical reality.

For example, if someone is assessing the likelihood that a person is a librarian, they may focus on that person's characteristics that match their stereotype of a librarian rather than reference how many individuals actually work as librarians compared to other professions in the population. This disregard for base rates can distort decision-making and lead to significant errors in reasoning.

The other options suggest different kinds of biases. Sampling bias relates to errors that arise when a sample is not representative of the broader population, while ignorance bias and recency bias involve different forms of cognitive errors or misjudgments. Ignorance bias refers to not accounting for information that may challenge one’s beliefs, and recency bias emphasizes the influence of recent events more heavily than those occurring