Related Experiment Video
Updated: Apr 4, 2026

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
Published on: September 19, 2012
Public perceptions of expert disagreement: Bias and incompetence or a complex and random world?
Nathan F Dieckmann1, Branden B Johnson2, Robin Gregory2
1Oregon Health & Science University, USA; Decision Research, USA.
Abstract:
Expert disputes can present laypeople with several challenges including trying to understand why such disputes occur. In an online survey of the US public, we used a psychometric approach to elicit perceptions of expert disputes for 56 forecasts sampled from seven domains. People with low education, or with low self-reported topic knowledge, were most likely to attribute disputes to expert incompetence. People with higher self-reported knowledge tended to attribute disputes to expert bias due to financial or ideological reasons. The more highly educated and cognitively able were most likely to attribute disputes to natural factors, such as the irreducible complexity and randomness of the phenomenon. Our results show that laypeople tend to use coherent-albeit potentially overly narrow-attributions to make sense of expert disputes and that these explanations vary across different segments of the population. We highlight several important implications for scientists, risk managers, and decision makers.
More Related Videos
05:22Dissociation of the Confounding Influences of Expectancy and Integrative Difficulty Residing in Anomalous Sentences in Event-related Potential Studies
Published on: May 9, 2019
06:08Exploring the Role of Deontic Reasoning and World Knowledge in Wason´s Selection Task
Published on: July 22, 2025
Related Concept Videos
Fundamental Attribution Error
Stereotype Content Model
Bias
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
Motivational Bias
Random and Systematic Errors
Random and Systematic Errors