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cognitive bias

Contributor(s): Corinne Bernstein

Cognitive bias is a limitation in objective thinking that is caused by the tendency for the human brain to perceive information through a filter of personal experience and preferences. The filtering process is called heuristics; it’s a coping mechanism that allows the brain to prioritize and process the vast amount of input it receives each second. While the mechanism is very effective, its limitations can cause errors that can be exploited.         

It may not be totally possible to eliminate the brain’s predisposition to take shortcuts, but understanding that bias exists can be useful when making decisions.  A continually evolving list of cognitive biases has been identified over the last six decades of research on human judgment and decision-making in cognitive science, social psychology and behavioral economics. They include:

Anchoring effect – the tendency for the brain to rely too much on the first instance of information it received when making decisions later on.

Availability bias – the tendency for the brain to conclude that a known instance is more representative of the whole than is actually the case.

Bandwagon effect – the tendency for the brain to conclude that something must be desirable because other people desire it.

Bias blind spot – the tendency for the brain to recognize another’s bias but not its own.

Clustering illusion – the tendency for the brain to want to see a pattern in what is actually a random sequence of numbers or events.

Confirmation bias – the tendency for the brain to value new information that supports existing ideas.

Framing effect – the tendency of the brain to arrive at different conclusions when reviewing the same information depending upon how the information is presented.

Group think – the tendency for the brain to place value on consensus. 

Negativity bias – the tendency for the brain to subconsciously place more significance on negative events than positive ones. This bias probably evolved as a survival technique. Assuming the worst of a situation that turns out not to be dangerous is much safer than not expecting danger that turns out to be present.

Recency bias – the tendency for the brain to subconsciously place more value on the last information it received about a topic.

Sunk cost effect – the tendency for the brain to continue investing in something that clearly isn’t working in order to avoid failure.

Survivorship bias – the tendency for the brain to focus on positive outcomes in favor of negative ones. A related phenomenon is the ostrich effect, in which people metaphorically bury their heads in the sand to avoid bad news.

Cognitive bias and its impact on data analytics

Being aware of how human bias can cloud analytics analysis is an important first step toward preventing it from happening.  While data analytics tools can help business executives make data-driven decisions, it is still up to humans to select what data should be analyzed. This is why it is important for business managers to understand that cognitive biases that occur when selecting data can cause digital tools used in predictive analytics and prescriptive analytics to generate false results.

Throughout history, analysts have learned the hard way about the pitfalls of deploying and using predictive modeling without examining the data selected for analysis for cognitive bias. For example, pollsters and election forecasters predicted large margins of victory for Hillary Clinton in the 2016 United States presidential election. The culmination of many types of bias played a part in predictions that inaccurately forecast Hillary Clinton would be elected president and reliance on weak polling data and flawed predictive models resulted in an unpredicted outcome.

This was last updated in February 2017

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