Dunning-Kruger Effect

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A Dunning-Kruger Effect is a cognitive bias where unskilled humans overestimate their skill level (suffer from illusory superiority) and (though less-predominantly) highly skilled humans underestimate their skill level.



References

2015

  • (Wikipedia, 2015) ⇒ http://en.wikipedia.org/wiki/Dunning–Kruger_effect Retrieved:2015-3-11.
    • The Dunning–Kruger effect is a cognitive bias wherein unskilled individuals suffer from illusory superiority, mistakenly assessing their ability to be much higher than is accurate. This bias is attributed to a metacognitive inability of the unskilled to recognize their ineptitude. Conversely, highly skilled individuals tend to underestimate their relative competence, erroneously assuming that tasks which are easy for them are also easy for others.

      As David Dunning and Justin Kruger of Cornell University conclude: "The miscalibration of the incompetent stems from an error about the self, whereas the miscalibration of the highly competent stems from an error about others."


2013

  • (Schlösseraet al., 2013) ⇒ Thomas Schlössera, David Dunning, Kerri L. Johnson, and Justin Kruger. (2013). “How Unaware Are the Unskilled? Empirical Tests of the “Signal Extraction” Counterexplanation for the Dunning–Kruger Effect in Self-evaluation of Performance. " Journal of Economic Psychology, 39
    • ABSTRACT: Previous work on the Dunning–Kruger effect has shown that poor performers often show little insight into the shortcomings in their performance, presumably because they suffer a double curse. Deficits in their knowledge prevent them from both producing correct responses and recognizing that the responses they produce are inferior to those produced by others. Krajč and Ortmann (2008) offered a different account, suggesting instead that poor performers make performance estimates with no more error than top performers. Floor effects, coupled with the assumption of a backwards-J performance distribution, force their self-evaluations errors to be frequently positive in nature. Krajč and Ortmann, however, offered no empirical data to test their “signal extraction” account. In three studies, we assessed their theoretical model by examining whether (1) the data producing the Dunning–Kruger effect fit the statistical assumptions considered by Krajč and Ortmann necessary to produce it, and (2) to see if their framework reproduced Dunning–Kruger errors in a data set that fit their statistical assumptions. We found that the Krajč–Ortmann framework failed to anticipate self-evaluative misperceptions on the part of poor performers, but that it does much better at accounting for misperceptions among top performers. Paradoxically, the model suggests that Kruger and Dunning (1999) may have underestimated the accuracy of top performers, even though their account asserts such accuracy.

1999