Science Deep Dive Bio-Performance
The Dunning-Kruger effect is real but smaller and stranger than its pop-science reputation, and the original explanation for why it happens has been empirically refuted.
22 min read
Bio-Performance

The Dunning-Kruger Effect Examined: Why Incompetence Feels Like Competence

The Dunning-Kruger effect is real but smaller and stranger than its pop-science reputation, and the original explanation for why it happens has been empirically refuted.

Mechanism
Controlled Human Data
Interpretation
Peer-reviewed evidence · Editorial synthesis
— What the Research Actually Found —

Twenty-seven years of investigation have refined the Dunning-Kruger effect from a viral meme into a precise, and more nuanced, scientific phenomenon. These four findings capture what the evidence now supports.

The Confidence Gap 50 percentile points

Bottom-quartile performers in Kruger and Dunning's original study perceived themselves at the 62nd percentile while actually ranking at the 12th, a 50-point miscalibration that launched a research programme.

Foundational
Health Misinformation 36 %

In a nationally representative US sample, 36% of respondents believed they knew as much as or more than doctors about the causes of autism, with the highest overconfidence among those with the lowest actual knowledge.

National Survey
Reasoning Overestimation >3 × factor

Among the lowest-performing participants on the Cognitive Reflection Test, self-assessed performance exceeded actual performance by a factor of more than three.

Multi-Study
Surgical Miscalibration 5.1 vs 3.8 /10

In a blinded prospective study, the worst-performing surgery residents rated their laparoscopic skill at 5.1 out of 10 while external assessors scored them at 3.8, yet they rated themselves identically to the best performers.

Blinded Prospective
42 Peer-reviewed sources
Evidence Signal

Converging evidence from registered reports, computational models, national surveys, EEG, twin studies, and cross-cultural replications confirms a residual self-assessment asymmetry beyond statistical artefact.

Study Mix
Registered Report
3
Meta
3
Review
6
Editorial Judgment

The Dunning-Kruger effect is real but substantially smaller than pop-science suggests, and the mechanism that drives it is not what you were told.

In 1999, a man named McArthur Wheeler robbed two Pittsburgh banks in broad daylight. He had no mask, no disguise, nothing but lemon juice smeared across his face. Wheeler believed, sincerely, that lemon juice made him invisible to security cameras. He was arrested that evening. When police showed him the footage, he stared at the screen and muttered, "But I wore the juice."[1] That story, apocryphal in the telling, verified in the arrest record, became the opening anecdote of what would turn into one of the most cited psychology papers of the twenty-first century: Justin Kruger and David Dunning's 1999 study on metacognitive miscalibration.[1]

The paper's central finding was arresting. Cornell undergraduates who scored in the bottom quartile on tests of grammar, logic, and humour estimated their performance at the 62nd percentile, while actually ranking at the 12th.[1] They were not merely wrong. They were wrong about being wrong, in a direction that flattered them. The top-quartile performers, by contrast, slightly underestimated their standing. The pattern was consistent across every domain tested, and the numbers were large enough to make the phenomenon feel universal. The Dunning-Kruger effect, as it was quickly named, appeared to show that incompetence itself was invisible to the incompetent.

The idea spread faster than any academic paper should. It became a meme, an insult, a diagnostic framework. "You're suffering from Dunning-Kruger" joined the vernacular alongside "confirmation bias" and "cognitive dissonance" as shorthand for someone who doesn't know what they don't know. By the time you encounter this article, you have almost certainly used the concept, or had it used on you.

Editorial pause
The Dunning-Kruger effect became famous for the wrong reason: it was too satisfying to check.

1999, The Paper That Launched a Meme. Kruger & Dunning's JPSP paper has been cited over 10,000 times. It remains among the top 50 most-accessed papers in the journal's history.

The trouble is that the phenomenon, as popularly understood, is not what the science actually shows. Over the past decade, a succession of registered reports, large-sample replications, and computational models have reshaped the evidence in ways that the internet has not caught up with. The famous graph, that elegant mountain of ignorance rising from the left, turns out to be partly a statistical artefact of how the data was plotted.[11][12][13] Nuhfer and colleagues demonstrated that 92% of purely random simulations, when graphed using the original method, produce the same "mountain" shape.[12] Magnus and Peresetsky showed that a statistical model with no psychology in it at all fits the data "almost perfectly."[11]

That does not mean the Dunning-Kruger effect is fictional. It means the effect is smaller, more specific, and mechanistically different from what two decades of pop-science coverage suggested. In representative samples using proper statistical methods, Dunkel and colleagues found the effect statistically significant but very small in magnitude.[14] A 2024 analysis using LOESS regression confined the meaningful overestimation to individuals with IQ scores between roughly 50 and 80.[15] The phenomenon is real. Its scope is narrower than advertised.

That framing also explains why the original mechanism is wrong. The dual-burden account, the claim that the same incompetence that impairs performance also impairs the ability to recognise that impairment, was tested directly in two pre-registered registered reports by McIntosh and colleagues.[5][6] The verdict was definitive: metacognitive efficiency, the quality of the metacognitive process itself, showed no relationship to task performance.[6] Low performers are not broken monitors. They are intact monitors with less information to work with.

Editorial pause
The effect is real. The graph is inflated. The mechanism as originally stated is empirically refuted.

This article examines what the Dunning-Kruger effect actually is, now that we know what it is not. The evidence points to a phenomenon that is less dramatic than a meme but more dangerous than a curiosity: a systematic asymmetry in how people integrate evidence about their own ability, with consequences that extend from medical training rooms to democratic decision-making.[23][24] The mechanism is not a deficit in self-knowledge. It is a deficit in evidence sensitivity, a measurable difference in how quickly people update their beliefs when presented with information that challenges their self-assessment.[4]

That distinction matters. If the Dunning-Kruger effect were about broken metacognition, you would expect it to be unfixable, a permanent feature of low ability. If it is about evidence processing, you would expect it to be addressable with the right kind of structured feedback. The research, as we will see, supports the second interpretation. Not easily. Not quickly. But genuinely.

Editorial pause (Section verdict)
The Dunning-Kruger effect is not about stupidity, it is about the rate at which people revise their self-model in light of new data.
The Mechanism

The Evidence Insensitivity Model: Why Low Performers Resist Correction

The original explanation for the Dunning-Kruger effect had a seductive elegance. Kruger and Dunning proposed what is now called the dual-burden account: the skills you need to perform well in a domain are the same skills you need to recognise that you are performing poorly.[1] Grammar requires understanding grammar to evaluate. Logic requires logic to assess. The unskilled, therefore, suffer a double curse, they fail, and they lack the very tools that would let them see the failure. "You need the skills to know you lack the skills" became one of psychology's most quotable lines.

McIntosh, Moore, Liu, and Della Sala dismantled that account in a 2022 registered report published in Royal Society Open Science.[6] Their design separated two components of metacognition that previous studies had conflated: metacognitive sensitivity, the amount of information available for self-assessment, and metacognitive efficiency, the quality of the processing applied to that information. If the dual-burden account were correct, both should decline with lower performance. What McIntosh found was that efficiency showed no relationship to performance whatsoever, the metacognitive process itself was intact. Only sensitivity tracked ability, because low performers simply had fewer correct cues available to evaluate.[6]

The implications are substantial. Low performers are not cognitively incapable of accurate self-assessment. They are informationally impoverished. Their internal monitor works. It just has less to work with.

Editorial pause
The metacognitive monitor is not broken in low performers, it is starved of signal.

If the dual-burden account explains what the effect is not, Jansen, Rafferty, and Griffiths provided the best current model of what it is. Their 2021 paper in Nature Human Behaviour combined a large-scale pre-registered replication (approximately 4,000 participants per study) with a formal Bayesian computational model that predicts the entire Dunning-Kruger pattern from first principles.[4]

The model works like this. Everyone begins with prior beliefs about their own ability, a rough internal estimate of "how good I probably am at this." When they encounter new evidence (a test result, a comparison with peers, a piece of feedback), they update that prior. But the rate of updating differs. The model estimates that low performers integrate new evidence at roughly half the rate of high performers.[4] This is not a claim about intelligence or metacognitive capacity. It is a claim about evidence weighting: low performers assign less influence to each new piece of information, which means their prior beliefs change more slowly.

The asymmetry is self-reinforcing. A person with strong priors about their own competence and a low update rate will persist in overestimation even as disconfirming evidence accumulates. A high performer with an equally strong prior and a higher update rate will converge toward accuracy faster. The model's predictions match the observed Dunning-Kruger data with high fidelity, including the asymmetric pattern where bottom-quartile performers overestimate by 50 percentile points while top-quartile performers underestimate by only about 12.[4][1]

Editorial pause
The Dunning-Kruger effect is not a failure of self-knowledge, it is a failure of belief revision.

"Low performers do not fail to receive feedback. They fail to update their beliefs when they receive it."

— Synthesised from Jansen, Rafferty & Griffiths (2021)
50percentile points

the gap between where bottom-quartile performers placed themselves (62nd percentile) and where they actually ranked (12th), the foundational Dunning-Kruger miscalibration

Kruger & Dunning (1999) · Grammar, logic, and humour tasks · Cornell undergraduates · n ≈ 167
The 5 Strongest Studies on the Dunning-Kruger Effect

These studies represent the most methodologically rigorous evidence available, spanning pre-registered replications, registered reports, EEG, multi-study experiments, and nationally representative surveys. Together, they establish that the effect is real, its original mechanism is wrong, and its consequences are measurable.

5

#1
88/100
/100
Jansen, Rafferty & Griffiths (2021), A rational model of the Dunning–Kruger effect supports insensitivity to evidence in low performers
~50 % reduced update rate

Pre-Registered Computational Model N ≈ 8,000
Design27/30 Sample18/20 Rigour13/15 Causality12/15 Replication9/10 Citations9/10
Supporting evidence · Rank 2–5
Most rigorous mechanism test, registered report, pre-committed analysis
82/100
/100
McIntosh, Moore, Liu & Della Sala (2022), Skill and self-knowledge: Empirical refutation of the dual-burden account of the Dunning–Kruger effect
McIntosh, Moore, Liu & Della Sala
β ≈ 0 **Stat unit:** (null)
Metacognitive efficiency, the quality of the metacognitive process itself, was unrelated to task performance. Only metacognitive sensitivity (the amount of information available) tracked performance. The dual-burden mechanism is empirically refuted.
The popular notion that "incompetence impairs the ability to recognise incompetence" is wrong as a mechanistic claim, the monitor works, it simply has less to monitor.
Only biological substrate study, preliminary EEG evidence
63/100
/100
Muller, Sirianni & Addante (2021), Neural correlates of the Dunning-Kruger effect
Muller, Sirianni & Addante
FN400 vs. LPC **Stat unit:** EEG components
In a preliminary EEG study of N = 54 participants, over-estimators showed familiarity-based heuristic processing (FN400); under-estimators showed analytic recollection processing (late parietal component), distinct neural signatures mapping onto dual-process theory.
The Dunning-Kruger effect has a measurable biological correlate in memory and attention processing strategy, it is not merely a statistical phenomenon.
Reasoning domain leader, best magnitude estimate for high-stakes cognition
76/100
/100
Pennycook, Ross, Koehler & Fugelsang (2017), Dunning-Kruger effects in reasoning: Theoretical implications of the failure to recognize incompetence
Pennycook, Ross, Koehler & Fugelsang
>3× **Stat unit:** overestimation factor
The worst-performing participants on the Cognitive Reflection Test overestimated their performance by a factor of more than three. Independently measured analytic cognitive style predicted metacognitive accuracy, linking the dunning kruger effect to reflective reasoning capacity.[17] Pennycook and colleagues' broader research programme confirmed that analytic thinking predicts calibration across multiple domains.[37]
In the domain most relevant to real-world decision-making (reasoning under uncertainty), the magnitude of self-assessment error among the lowest performers is extreme and measurable.
Real-world stakes leader, strongest evidence of population-level consequences
71/100
/100
Motta, Callaghan & Sylvester (2018), Knowing less but presuming more: Dunning-Kruger effects and the endorsement of anti-vaccine policy attitudes
Motta, Callaghan & Sylvester
36 **Stat unit:** %
In a nationally representative US survey, 36% of respondents believed they knew as much as or more than doctors about autism causation. A Dunning-Kruger overconfidence score independently predicted opposition to mandatory vaccination policy after controlling for education, ideology, and demographics.
The Dunning-Kruger effect is not an academic curiosity, it shapes public health policy attitudes with measurable consequences at population scale.
Editorial judgment

The common thread across these domains is not that people are foolish. It is that the feedback systems they rely on are inadequate for the task of calibration. A surgical resident receives praise for completing a procedure without being told how their technique compared to peers performing the same operation. A voter forms opinions from media that confirms existing beliefs rather than testing them. An aviation student passes courses without encountering the specific failure modes that would reveal their knowledge gaps. Dunning, Heath, and Suls reviewed self-assessment failures across health, education, and workplace domains in a comprehensive 2004 synthesis.[39] Their conclusion was structural: the problem is not that people refuse to self-correct, but that the environments in which they operate rarely provide the kind of information that would make accurate self-correction possible. People overestimate because the world is not designed to tell them they are wrong in ways they can hear.

Editorial pause
The Dunning-Kruger effect persists not because people are resistant to truth, but because most environments are resistant to delivering it.
When Overconfidence Meets Consequence

What Breaks When Self-Assessment Fails

The Dunning-Kruger effect is not merely an academic curiosity. In domains where accuracy of self-assessment determines whether someone seeks help, defers to expertise, or acts on incomplete knowledge, miscalibration has measurable costs.

Clinical Decision-Making
Medical Overconfidence
In a blinded prospective study, the worst-performing surgery residents rated their laparoscopic skills at 5.1 out of 10 while external assessors scored them at 3.8, yet their self-ratings were statistically indistinguishable from the best performers.[26] Clinicians have proposed that this miscalibration may contribute to failure to seek supervision and reduced patient safety.[25][28] Emergency medicine residents showed a similar pattern: overall prediction accuracy was reasonable (r = 0.58), but the lowest performers systematically overestimated their relative rank.[27]
What it feels like · unsupervised clinical decisions made with false confidence, reluctance to seek senior review
Public Health & Policy
Dunning-Kruger at Population Scale
Motta's nationally representative survey found 36% of Americans believed they knew as much as or more than doctors about autism causation, and 34% believed they knew as much as scientists.[23] That overconfidence independently predicted opposition to mandatory vaccination after controlling for education, ideology, and demographics.[23] When overconfidence about medical knowledge reaches population scale, it shapes health policy.
36%
What it feels like · confident rejection of expert consensus, policy decisions driven by self-assessed rather than actual knowledge
Political Reasoning
Partisan Amplification
Anson's survey experiment showed that when partisan identity was made salient, low-knowledge partisans displayed significantly amplified Dunning-Kruger overconfidence about political knowledge.[24] The effect is not merely cognitive, it is social. Identity-protective cognition exacerbates the confidence gap by giving overestimation an emotional payoff. Tetlock's forecasting research demonstrates that even domain experts protect overconfident predictions through "I was almost right" counterfactual reasoning after failures.[40]
What it feels like · certainty about political matters inversely related to depth of engagement, resistance to disconfirming information
Professional & Educational
The Information Literacy Gap
Mahmood's systematic review of 53 empirical studies found Dunning-Kruger evidence in 92%, a near-universal finding across populations and educational contexts.[30] In aviation, lower-performing students grossly overestimated both grammar and pilot knowledge ability, with direct safety implications.[29] The gap between perceived and actual competence is widest in domains where the consequences of that gap are highest. In a cross-national study, the effect appeared in all six European countries tested, across grades 3 and 4.[31]
92%
What it feels like · declining additional training because you believe you already know enough, missing skill gaps until they produce visible failure
1 / 4

"The metacognitive monitor cannot generate accurate signals from inside the competence gap it occupies."

— Protocol operating principle
Translation Layer · What Changes Starting Now

A Calibration Protocol for Reducing Self-Assessment Error

The science supports a structured approach to narrowing the gap between perceived and actual competence, not through self-reflection, which the evidence suggests is insufficient, but through external reference systems that supply what the metacognitive monitor lacks.

01
Ongoing
Prediction Logging
Rule
Record a specific numerical prediction before every significant performance, then log the actual outcome. Track the gap over at least 10–20 prediction-outcome pairs across 4–8 weeks. Use 0–100 or 1–10 scales, not verbal labels.
Why
Jansen's model shows low performers update beliefs at a reduced rate.[4] Prediction logging forces confrontation with the gap between belief and reality at a frequency high enough to overcome dampened updating. Tetlock's superforecasting research confirms that tracking predictions against outcomes is the single strongest calibration intervention.[35]
Common mistake
Using the log once and expecting insight. Calibration requires repeated data, not a single moment of self-knowledge. Expecting the gap to close in days rather than weeks.
02
Quarterly
Reference-Point Comparison
Rule
Seek performance comparisons against calibrated benchmarks, not self-selected peers. Use objective rankings, structured 360-degree feedback, or external rubrics from people at measurably different competence levels.
Why
Ehrlinger's five-study replication showed poor performers overestimate even with financial incentives, self-reflection alone is insufficient.[3] Dunning's synthesis of meta-ignorance demonstrates that comparison against worse performers merely confirms overconfidence.[2] Effective comparison requires instruction on what to look for, not just access to comparison data.
Common mistake
Comparing against weaker performers, which confirms overconfidence, or against people with no meaningful performance data.
03
Before Assessing
Domain Vocabulary
Rule
Learn the expert evaluation criteria for a domain before self-assessing performance in it. Acquire the descriptive framework first, then apply it to your own output.
Why
McIntosh's registered report shows low performers have fewer metacognitive cues available.[6] Building domain-specific vocabulary literally creates the cues the metacognitive monitor needs to function accurately. This is the one lever that directly addresses the mechanism.
Common mistake
Self-assessing before understanding what "good" looks like in the domain. Interpreting fluency, the feeling that something is easy, as evidence of competence.
04
Pre-Decision
Adversarial Pre-Mortem
Rule
Before any high-confidence decision, assume your current judgment is wrong and generate at least three specific failure scenarios. Write them down in 10 minutes. Then decide.
Why
Muller's EEG data shows over-estimators default to familiarity-based heuristic processing.[7] Kahneman's dual-process framework identifies this as System 1 dominance.[33] The pre-mortem forces System 2 engagement by requiring analytical generation of counter-evidence.
Common mistake
Treating the pre-mortem as an argument to abandon the decision. The goal is calibration, not paralysis.
1 / 4

These four steps share a single operational logic: they make performance standards external and observable, supplying the metacognitive monitor with the signal it needs to produce accurate output.

The Verdict
01
Claim
Self-assessment asymmetry is real
The Dunning-Kruger effect survives methodological scrutiny as a genuine phenomenon: low performers overestimate and high performers underestimate, driven by differential evidence sensitivity rather than metacognitive failure. The effect is smaller than popularly believed and partly inflated by statistical artefact, but the residual asymmetry is real.
02
Consequence
Miscalibration has measurable costs
In medicine, aviation, public health, and political decision-making, the gap between perceived and actual competence produces downstream harm, from unsupervised clinical decisions to population-level resistance to expert consensus. The domains where the stakes are highest are the domains where the effect is most consequential.
03
Lever
External feedback systems close the gap
Calibration improves with structured prediction logging, reference-point comparison, domain vocabulary acquisition, and adversarial pre-mortems. The fix is environmental, not psychological, make performance standards external, observable, and repeated, and the metacognitive monitor self-corrects over time.
Moderate-High
Moderate-High Confidence
Strong converging evidence from pre-registered replications, registered reports, and cross-domain studies · original mechanism refuted by highest-quality evidence · effect size debate ongoing but residual phenomenon confirmed · intervention evidence small but positive (g = 0.25)

References

0 sources cited — peer-reviewed sources

  1. 1Kruger, J., & Dunning, D. (1999). Unskilled and unaware of it: How difficulties in recognizing one's own incompetence lead to inflated self-assessments. Journal of Personality and Social Psychology, 77(6), 1121–1134. DOI: 10.1037/0022-3514.77.6.1121
  2. 2Dunning, D. (2011). The Dunning–Kruger effect: On being ignorant of one's own ignorance. Advances in Experimental Social Psychology, 44, 247–296. DOI: 10.1016/B978-0-12-385522-0.00005-6
  3. 3Ehrlinger, J., Johnson, K., Banner, M., Dunning, D., & Kruger, J. (2008). Why the unskilled are unaware: Further explorations of (absent) self-insight among the incompetent. Organizational Behavior and Human Decision Processes, 105(1), 98–121. DOI: 10.1016/j.obhdp.2007.05.002
  4. 4Jansen, R. A., Rafferty, A. N., & Griffiths, T. L. (2021). A rational model of the Dunning–Kruger effect supports insensitivity to evidence in low performers. Nature Human Behaviour, 5(6), 756–763. DOI: 10.1038/s41562-021-01057-0
  5. 5McIntosh, R. D., Fowler, E. A., Lyu, T., & Della Sala, S. (2019). Wise up: Clarifying the role of metacognition in the Dunning-Kruger effect. Journal of Experimental Psychology: General, 148(11), 1882–1897. DOI: 10.1037/xge0000579
  6. 6McIntosh, R. D., Moore, A., Liu, Y., & Della Sala, S. (2022). Skill and self-knowledge: Empirical refutation of the dual-burden account of the Dunning–Kruger effect. Royal Society Open Science, 9(12), 191727. DOI: 10.1098/rsos.191727
  7. 7Muller, A., Sirianni, L. A., & Addante, R. J. (2021). Neural correlates of the Dunning-Kruger effect. European Journal of Neuroscience, 53(2), 460–484. DOI: 10.1111/ejn.14935
  8. 8Vaccaro, A. G., & Fleming, S. M. (2018). Thinking about thinking: A coordinate-based meta-analysis of neuroimaging studies of metacognitive judgements. Brain and Neuroscience Advances, 2. DOI: 10.1177/2398212818810591
  9. 9Krueger, J., & Mueller, R. A. (2002). Unskilled, unaware, or both? The better-than-average heuristic and statistical regression predict errors in estimates of own performance. Journal of Personality and Social Psychology, 82(2), 180–188. DOI: 10.1037/0022-3514.82.2.180
  10. 10Gignac, G. E., & Zajenkowski, M. (2020). The Dunning-Kruger effect is (mostly) a statistical artefact: Valid approaches to testing the hypothesis with individual differences data. Intelligence, 80, 101449. DOI: 10.1016/j.intell.2020.101449
  11. 11Magnus, J. R., & Peresetsky, A. A. (2022). A statistical explanation of the Dunning–Kruger effect. Frontiers in Psychology, 13, 840180. DOI: 10.3389/fpsyg.2022.840180
  12. 12Nuhfer, E., Fleisher, S., Cogan, C., Wirth, K., & Gaze, E. (2017). How random noise and a graphical convention subverted behavioral scientists' explanations of self-assessment data. Numeracy, 10(1), Article 4. DOI: 10.5038/1936-4660.10.1.4
  13. 13Nuhfer, E., et al. (2016). Random number simulations reveal how random noise affects the measurements and graphical portrayals of self-assessed competency. Numeracy, 9(1), Article 4. DOI: 10.5038/1936-4660.9.1.4
  14. 14Dunkel, C. S., Nedelec, J. L., & van der Linden, D. (2023). Reevaluating the Dunning-Kruger effect: A response to and replication of Gignac and Zajenkowski (2020). Intelligence, 96, 101717. DOI: 10.1016/j.intell.2022.101717
  15. 15Gignac, G. E. (2024). Rethinking the Dunning-Kruger effect: Negligible influence on a limited segment of the population. Intelligence, 104(C), 101830. DOI: 10.1016/j.intell.2024.101830
  16. 16Hiller, A. (2023). Comment on Gignac and Zajenkowski. Intelligence. DOI: 10.1016/j.intell.2023.101832
  17. 17Pennycook, G., Ross, R. M., Koehler, D. J., & Fugelsang, J. A. (2017). Dunning-Kruger effects in reasoning: Theoretical implications of the failure to recognize incompetence. Psychonomic Bulletin & Review, 24(6), 1774–1784. DOI: 10.3758/s13423-017-1242-7
  18. 18Burson, K. A., Larrick, R. P., & Klayman, J. (2006). Skilled or unskilled, but still unaware of it: How perceptions of difficulty drive miscalibration in relative comparisons. Journal of Personality and Social Psychology, 90(1), 60–77. DOI: 10.1037/0022-3514.90.1.60
  19. 19Schlösser, T., Dunning, D., Johnson, K. L., & Kruger, J. (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, 85–100. DOI: 10.1016/j.joep.2013.07.004
  20. 20Svenson, O. (1981). Are we all less risky and more skillful than our fellow drivers? Acta Psychologica, 47(2), 143–148. DOI: 10.1016/0001-6918(81)90005-6
  21. 21Moore, D. A., & Healy, P. J. (2008). The trouble with overconfidence. Psychological Review, 115(2), 502–517. DOI: 10.1037/0033-295X.115.2.502
  22. 22Alicke, M. D., & Govorun, O. (2005). The better-than-average effect. In M. D. Alicke, D. Dunning, & J. Krueger (Eds.), The Self in Social Judgment (pp. 85–106). Psychology Press.
  23. 23Motta, M., Callaghan, T., & Sylvester, S. (2018). Knowing less but presuming more: Dunning-Kruger effects and the endorsement of anti-vaccine policy attitudes. Social Science & Medicine, 211, 274–281. DOI: 10.1016/j.socscimed.2018.06.032
  24. 24Anson, I. G. (2018). Partisanship, political knowledge, and the Dunning-Kruger effect. Political Psychology, 39(5), 1173–1192. DOI: 10.1111/pops.12490
  25. 25Rahmani, M. (2020). Medical trainees and the Dunning-Kruger effect: When they don't know what they don't know. Journal of Graduate Medical Education, 12(5), 532–534. DOI: 10.4300/JGME-D-20-00134.1
  26. 26Correia, R., et al. (2024). Description of the Dunning-Kruger effect in general surgery residents during laparoscopic cholecystectomy: A blinded prospective study. Acta Cirúrgica Brasileira. DOI: 10.1590/s0102-8650.fs20240039
  27. 27Gaeta, T. J., et al. (2024). The Dunning-Kruger effect in resident predicted and actual performance on the American Board of Emergency Medicine in-training examination. JACEP Open. DOI: 10.1002/emp2.13305
  28. 28Moris, D., & Giorgakis, E. (2026). Confidence without wisdom: The Dunning-Kruger problem in modern surgery. Cureus, 18(1), e102174.
  29. 29Pavel, S. R., Robertson, M. F., & Harrison, B. T. (2012). The Dunning-Kruger effect and SIUC University's aviation students. Journal of Aviation Technology and Engineering, 2(1), 125–129. DOI: 10.5703/1288284314864
  30. 30Mahmood, K. (2016). Do people overestimate their information literacy skills? A systematic review of empirical evidence on the Dunning-Kruger effect. Communications in Information Literacy, 10(2), 199–213. DOI: 10.15760/comminfolit.2016.10.2.24
  31. 31Yang Hansen, K., Thorsen, C., Radišić, J., Peixoto, F., Laine, A., & Liu, X. (2024). When competence and confidence are at odds: A cross-country examination of the Dunning-Kruger effect. European Journal of Psychology of Education. DOI: 10.1007/s10212-024-00804-x
  32. 32Nedelec, J. L., Dunkel, C. S., & van der Linden, D. (2025). Heritability of metacognitive judgement of intelligence: A twin study on the Dunning-Kruger effect. Intelligence, 111(C). DOI: 10.1016/j.intell.2025.101849
  33. 33Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.
  34. 34Gigerenzer, G. (2007). Gut Feelings: The Intelligence of the Unconscious. Viking.
  35. 35Tetlock, P. E., & Gardner, D. (2015). Superforecasting: The Art and Science of Prediction. Crown Publishers.
  36. 36Osterhage, J. L. (2021). Persistent miscalibration for low and high achievers despite practice test feedback in an introductory biology course. CBE, Life Sciences Education. PMC8442020.
  37. 37Pennycook, G., Fugelsang, J. A., & Koehler, D. J. (2015). Everyday consequences of analytic thinking. Current Directions in Psychological Science, 24(6), 425–432. DOI: 10.1177/0963721415604610
  38. 38Alicke, M. D. (1985). Global self-evaluation as determined by the desirability and controllability of trait adjectives. Journal of Personality and Social Psychology, 49(6), 1621–1630. DOI: 10.1037/0022-3514.49.6.1621
  39. 39Dunning, D., Heath, C., & Suls, J. M. (2004). Flawed self-assessment: Implications for health, education, and the workplace. Psychological Science in the Public Interest, 5(3), 69–106. DOI: 10.1111/j.1529-1006.2004.00018.x
  40. 40Tetlock, P. E. (1998). Close-call counterfactuals and belief system defenses: I was not almost wrong but I was almost right. Journal of Personality and Social Psychology, 75(3), 639–652. DOI: 10.1037/0022-3514.75.3.639
  41. 41Ziegler, M., et al. (2022). Less-intelligent and unaware? Accuracy and Dunning–Kruger effects for self-estimates of different aspects of intelligence. Journal of Intelligence, 10(1), 10. DOI: 10.3390/jintelligence10010010
  42. 42Gutierrez de Blume, A. P. (2022). Calibrating calibration: A meta-analysis of learning strategy instruction interventions to improve metacognitive monitoring accuracy. Journal of Educational Psychology, 114(4), 681–700. DOI: 10.1037/edu0000674
No references match your search.

DOI links resolve to the original publisher via doi.org. Open-access versions can often be found via Google Scholar.

High-Performance Insights

Leave a Reply

Your email address will not be published. Required fields are marked *