Representativeness shows that even if the odds of something are small if the person or situation matches a stereotype that matches we’re far more likely to give this more weight than we should.
Example, Mark is a detail-oriented, driven person who values logic and straightforward thinking. Is Mark more likely to be a Regional Manager for a bank or work at the checkout for a large retailer, such as ASDA or Walmart?
There are far more checkout workers than there are Regional Bank Managers so statistically, Mark is far more likely to work in a supermarket. However, we’re blinded to likelihood and probability and focus on the fact that his personality is representative of the type of person that we believe would succeed as a Regional Manager for a bank.
This also applied when we are considering events and not just people. Consider the popular example given by Amos Tversky and Daniel Kahneman:
Linda is 29 years old, single, outspoken, and intelligent. She studied Gender Studies and as a student, she was deeply concerned with issues of discrimination and social justice, and also participated in anti-war demonstrations.
Which is more probable?
- Linda is a bank teller.
- Linda is a bank teller and is active in the feminist movement.
This is the conjunction fallacy in play and is another great example at why humans are bad at probability – even statisticians. We incorrectly judge the likelihood of multiple events judged as more likely than a single event when directly compared.
The chances of Lucy being a bank teller AND a feminist are much lower than the chances of her just being a bank teller.
These are just a few of the MANY biases that our brains are susceptible to, but they’re the ones you’re most likely to come up against when you’re trying to judge a person or a situation.