Copy - May 23, 2021
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Confirmation bias occurs when people actively search for and favor information or evidence that confirms their preconceptions or hypotheses while ignoring or slighting adverse or mitigating evidence. It is a type of cognitive bias (pattern of deviation in judgment that occurs in particular situations - leading to perceptual distortion, inaccurate judgment, or illogical interpretation) and represents an error of inductive inference toward confirmation of the hypothesis under consideration. It can also be considered a type of selection bias in collecting evidence.
When scientists make predictions using computer models or other techniques they often become invested in those predictions. Creating and offering predictions to decision-makers and policy-makers can lead to bad judgment and illogical interpretation of real-world data. For example, during COVID19 many scientists ignored or undervalued strong evidence from data that contradicted their predictions. This impeded timely self-correcting mechanisms to recognize and pivot from policy mistakes.
Recent evidence with COVID19 suggests confirmation bias is rampant and out of control and has created a loss of confidence and credibility for science by the public and policy-makers that has serious consequences for our future. The danger for professional data science practitioners is providing flawed data science results leading to bad business and policy decisions. We must learn from the failures of academic and research scientists and proactively avoid confirmation bias by actively seeking both confirmatory and contradictory evidence and using scientific methods to weigh the evidence fairly.
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