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Foolish Knowledge: The Dunning-Kruger Effect

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Foolish Knowledge: The Dunning-Kruger Effect

Aug 04, 2016   |  By
Rustin Diehl, JD, CKM

"Ignorance more frequently begets confidence than does knowledge." – Charles Darwin

When presented with a question or challenge, some humans are diffident about their knowledge and timid to take action.  Others bullishly push forward with confidence in what they think they know.  The underlying issue in both cases is the same:  many people suffer from false illusions of inferiority or superiority and are unable to evaluate themselves.

Cornell University Researchers David Dunning and Justin Kruger have studied this phenomenon, now called the “Dunning–Kruger effect.”  The Dunning-Kruger effect results from the metacognitive bias of unskilled individuals who mistakenly assess their ability to be much higher than is accurate. Put differently, the unskilled individual does not know what they don’t know and is unable to recognize their own ineptitude or effectively evaluate their own ability.

Most organizations recognize this issue and rely on experienced individuals for knowledge and action.  However, in some instances, experts may not serve an organization very well at all.  While the Dunning-Kruger applies to the inexperienced, this metacognitive problem effect also extends to experienced individuals. Dunning and Kruger found that some experienced individuals underestimate their relative competence, and may even erroneously assume that what is easy for them is also easy for others.”  In other words, even seasoned individuals can make assumptive errors due to their inability to effectively evaluate the abilities of others.

In the project planning process, the cognitive biases of both experts and the novices becomes particularly evident.  Jeff Sutherland, author of Scrum: The Art of Doing Twice the Work in Half the Time, points out the fact that first estimates of work can range from 400 percent beyond the time actually taken to 25 percent of the time taken. In other words, human time estimates can be off by a factor of 16.

Even worse, the research shows that neither novices nor experts are any better at estimating time requirements.  This inability to gauge time required for a project is consistent with the Dunning-Kruger effect and the inability of experts and novices alike to understand and assess their own abilities and the abilities of others to complete a given task as part of the project.

As a solution to the issue of cognitive bias in time estimates, Sutherland has found greater success by using both experts and novices in an anonymous time-estimation voting process.  Sutherland recommends that rather than asking the novices and experts who are voting to give precise time estimates for the various tasks in a project, they instead use a more approximating, “relative sizing” approach.  In the relative sizing of a task, Sutherland suggests that the individuals estimating time assign a number to each task from the Fibonacci sequence of numbers:  1, 2, 3, 5, 8, 13, 21…

The side-by-side use of both experts and novices in estimating time has proved to be an effective measure to eliminate some of the cognitive bias in the time and resource planning process.  Sutherland’s recommended technique relies on the efficacy of crowds and distributed decision-making as an effective method for overcoming the Dunning-Kruger effect.

The Dunning-Kruger effect is caused by expert and novice cognitive biases regarding knowledge and skill.  This bias can be overcome by reliance on crowds including both novices and experts because a mixed crowd holds more potentially diverse knowledge and abilities to contribute to a given task. In his 2005 book, The Wisdom of Crowds, James Surowiecki points out that “experts simply lack much of the knowledge held by novices because it is not in the ‘world they live in.’” By adding both novices and experts into a system or project, the overall group is made more diverse than it would otherwise be – and better able to overcome the knowledge and abilities biases pointed out by the Dunning-Kruger effect.

About the author:  Rustin Diehl, JD, CKM is an innovation advisor and trainer, focused on business modeling and training with businesses, private clients, and non-profits.  Rustin emphasizes models and tools that mobilize and connect knowledge resources in support of strategic innovation objectives.  He is a member of Manifest Advisors, a training and certification firm based in Salt Lake City, with a core focus on innovation, knowledge management, and strategy development.

Rustin is the lead instructor for KMI's "Innovation and KM" program - contact: for more info.

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