Representativeness Bias
Representativeness bias is the tendency to look for patterns in situations, people, or objects to help us make decisions. Our brains try to eliminate uncertainty by finding observed or perceived similarities in our past experiences to inform the present. This mental shortcut is often helpful, but overreliance can lead to inaccuracies and errors in judgment.
In everyday life when faced with a new experience, representativeness bias often leads us to make assumptions about reality and possible outcomes based on only the past experiences while neglecting the mathematical probabilities. For example, if we see someone wearing business attire, carrying a briefcase, and texting on a cellphone and were asked to guess whether they work for a corporation or in a library, research has shown most people will say they work for a corporation. They simply match with a mental image we have of what people who work in corporations look like. However, the probability of what their profession actually is should not consider such subjective perceptions – and mathematically, both jobs are equally likely. Representativeness bias is a powerful mental shortcut when forced to make decisions with high levels of uncertainty, but overreliance on it can lead to problems.
Unsurprisingly, stereotyping is a key consequence of representativeness bias. It results from our expectations that someone will behave in a certain way, or a situation will unfold in a certain manner based on our own past experiences – when we don’t even know that specific person or situation. Not all stereotypes are born from direct experiences, for example we tend to have perceptions about societal norms, about age, gender or race, and even about organizations.
During incident investigations we need to be mindful not to stereotype. For example, if a person on site fits a particular mental image we have built from past experiences, anecdotal examples, or societal trends – we need to understand that this mental image is not objective evidence. This is not to say we would be wrong each and every time, but our knowledge can never be sufficient data to replace mathematical probabilities. We should not assume workers, managers, or even leaders share traits or characteristics because of their trade, background, age, or levels of experience in the industry. For example, not all older workers are ‘stuck in their ways’, and resist changing their work methods as a result of new safety protocols or interventions. We should try to treat everyone equally during the investigation process. It can be very hard to overcome stereotyping.
Many stereotypes are entrenched in society and constantly reinforced. Advertisements often enhance stereotypes, for example positioning women as homemakers and men as money earners in a family. They are often introduced when we’re young, making it harder to change our thinking when we become adults, although it’s not impossible. A good approach is to always be mindful you could be stereotyping and take that as your starting point. A robust technique to challenge stereotyping is to actively look for evidence that goes against your initial assumption or mental image. The investigation should reveal whether your initial assumption was accurate or not. This approach mirrors how any good scientific research is conducted, where you only consider a hypothesis correct if you cannot find any evidence to the contrary.
Although it is easier said than done, we must try to avoid generalizing – if you can generalize about someone, you are likely stereotyping them. Try to actively listen to the individual in front of you and what they are saying without also thinking: ‘oh, they are saying this because they’re X, Y or Z’. Investigation interviews are about getting good information only. Analysis of the how, what, and why should be done after all the interviews are completed to generate learning.
While our brains don’t like uncertainty and want to make sense of everything all at once, such tendencies can lead to overreliance on shortcuts like representativeness bias. Remind yourself that mathematical probability dictates that each incident is a new event, and although there can be patterns in the data from incidents across a company or industry, each incident is unique in reality and should be examined from a neutral starting point. You may end up with the same conclusions as your initial thoughts, but you will have arrived there honestly and not missed any contributory aspects or further information that could help the organization learn as much as it can from the incident.