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Do Decision Biases Predict Bad Decisions? Omission Bias, Naturalness Bias, and Influenza Vaccination

Memorial Sloan-Kettering Cancer Center, New York, New York, dibonavm{at}mskcc.org

Gretchen B. Chapman, PhD

Rutgers University, Camden, New Jersey

Purpose. Numerous studies using hypothetical vignettes have demonstrated decision biases or deviations from utility theory. Do people who commit biases in questionnaire studies make worse real-world decisions than do less biased people? Methods. Two hundred seventy university faculty and staff participated in a questionnaire study in which they reported whether they accepted a free influenza vaccine offered at their work place. Influenza vaccine acceptance was the measure of real-world decision making. Participants responded to 3 hypothetical scenarios. Two scenarios measured the omission bias and described a vaccine (scenario 1) and a medication (scenario 2) that prevented a negative health outcome but that itself could cause the negative health outcome. The omission bias is a preference for not vaccinating or medicating even when the vaccine/medication lowers the total risk of the negative outcome. A 3rd scenario measured the naturalness bias by presenting a choice between 2 chemically identical medications, one extracted from a natural herb and the other synthesized in a laboratory. Preference for the natural medication indicated the naturalness bias. Results. The results indicated that a substantial proportion of participants exhibited these biases and that participants who exhibited these biases were less likely to accept the flu vaccine. Conclusions. To the extent that declining a free flu vaccine is a worse real-world decision, people who demonstrate the naturalness and omission biases in hypothetical scenarios make worse real-world decisions. Key words: omission bias; naturalness bias; influenza; vaccination. (Med Decis Making 2008;28:532—539)

This version was published on July 1, 2008

Medical Decision Making, Vol. 28, No. 4, 532-539 (2008)
DOI: 10.1177/0272989X07312723


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