Confirmation bias is a type of mistake that occurs in thinking when information that confirms a pre-existing belief is given priority over information that does not support a preexisting belief. Informally, confirmation bias is sometimes referred to as wishful thinking.
Confirmation bias occurs when people filter out facts and opinions that don’t coincide with their preconceived notions. When a decision is made before all the data is examined, there is a danger of falling prey to confirmation bias, even when someone is trying to be objective.
In predictive modeling and big data analytics, confirmation bias can steer an analyst towards seeking evidence that favors an initial hypothesis. For example, the analyst might frame survey questions in such a way that all answers support a particular point of view. Interpretation of information can also hold a bias. Two analysts can review the same data, but select different aspects of the data to support each of their individual preferred outcomes. Because people tend to remember information that reinforces the way they already think, memory also plays a part in confirmation bias. This effect is called confirmatory memory or selective recall.
Confirmation bias can lead to data confabulation, the selective and possibly misleading use of data to support a decision that has already been made. To deal with and combat confirmation bias, it is important to be aware of its existence and the danger it poses. It is also important to actively seek out information that disagrees with a preexisting point of view, ask opposing or challenging questions and keep information channels open to consistently update current beliefs.
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