Cognitive bias

Cognitive bias occurs where our analysis of a situation is compromised by mental shortcuts or patterns of thinking, that place undue emphasis on a particular perspective.

For example, cold bias affects our thinking without our awareness, whereas hot bias affects our thinking in a way we are aware of, and engage in knowingly.

Working with colleagues helps us to identify and address our cognitive biases. We can prompt each other to reflect on our thought processes as we explain our thinking.

The list below identifies some of the key biases to guard against when it comes to evaluation:

Confirmation biasLooking for, remembering, noticing or giving more weight to data that supports an existing view. This includes rejecting new evidence that contradicts our current thinking.
The bandwagon effectGroup thinking, going with the flow of the crowd or not thinking independently, and is often based on a conscious or unconscious desire to fit in.
Optimism biasOver-emphasising pleasing outcomes, whilst failing to identify limitations and weaknesses. This might come about through evaluating ambiguous information in a favourable light. It might also take the form of requiring a higher standard of evidence for a negative conclusion, whilst accepting a lower standard of evidence for a positive conclusion.
Pessimism biasThe opposite of optimism bias, where we over-emphasise negative events or outcomes. This can include ‘negativity bias’, where we perceive criticism or bad news as being more important, profound or trustworthy than praise or good news.
Status quo bias and the appeal to novelty fallacyWhere we prefer to do what we have always done simply because we have always done it that way (‘status quo bias’), or alternatively where we prematurely approve of a proposal simply because it is new and modern (the ‘appeal to novelty’ fallacy).
Self-serving biasClaiming more personal responsibility for successes than for failures.
The clustering illusionThe tendency to overestimate the importance of small runs, streaks or clusters in large samples of random data. This is also known as seeing ‘phantom patterns’.
Observational selection biasWhen we become aware of something, we start to see it everywhere and believe that it’s becoming more common or prevalent (when in fact it’s just that we are noticing it more).
Social desirability biasThe natural human desire not to ‘say the wrong thing’. This is particularly important to be aware of when collecting self-report data, using tools such as surveys, focus groups or interviews.
Return to top of page