Base rate neglect

The best way to explain base rate neglect, is to start off with a (classical) example. Assume we present you with the following description of a person named Linda:

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

Next, we ask you which of the following statements you think is more likely:

  1. Linda is a bank teller
  2. Linda is a bank teller and is active in the feminist movement

So, which of the two statements describes Linda better? You might think it’s statement B. At least, that’s what most people tend to answer, when presented the above example. However, this is not possible, as statement B is a subset of statement A. Clearly, the probability that Linda is a bank teller cannot be smaller than the probability that Linda is a bank teller and a member of a feminist movement. The reason we think statement B is more likely, is because it our initial description sounds (more) representative of a feminist. And this is what causes us to wrongfully choose statement B over statement A.

Bayes law

One way of explaining this bias more formally, is to rely on Bayes Law:

 p(statement B| description) = \dfrac{p(description|statement B) \cdot p(statement B)}{p(description)}

What happens in the above example, is that we put too much weight on p(description|statement B), which captures representativeness, and too little weight on the base rate, p(statement B). This also explains why we refer to this bias as ‘base rate’ neglect.

Related Biases

Base rate neglect is strongly related to sample size neglect. This is another example where the representativeness heuristic is at play.


The mind tends to ignore general information (‘base rate information’) when specific information is also provided. Instead we focus on the lattter.