<story><title>The Dunning-Kruger effect is autocorrelation</title><url>https://economicsfromthetopdown.com/2022/04/08/the-dunning-kruger-effect-is-autocorrelation/</url></story><parent_chain><item><author>bitshiftfaced</author><text>The authors did &amp;quot;X - Y vs X,&amp;quot; but that&amp;#x27;s not even the biggest problem. The authors subtracted two measures that had been transformed and bounded from 0 to 1 (think percentiles). What happens at the extremes of those bounds? How much can your top performers overestimate their performance? They&amp;#x27;re almost at 1 already, so not much. If they were to overestimate and underestimate at the same rate and by the same magnitude in terms of raw values, the ceiling effect on the transformed values means that the graph will make it look like they underestimate more often. The opposite problem happens for the worst performers.&lt;p&gt;See &amp;quot;Random Number Simulations Reveal How Random Noise Affects the Measurements and Graphical Portrayals of Self-Assessed Competency.&amp;quot; Numeracy 9, Iss. 1 (2016), particularly figures 7, 8, and 9.</text></item></parent_chain><comment><author>SamBam</author><text>Exactly, that was my thought. How would it be &lt;i&gt;possible&lt;/i&gt; to get anything other than the D-K effect, even if it wasn&amp;#x27;t just averaging to the mean?&lt;p&gt;The lowest quartile can&amp;#x27;t say they&amp;#x27;re below the lowest quartile, so any error at all will be counted as &amp;quot;overconfidence.&amp;quot; The top quartile can&amp;#x27;t say they&amp;#x27;re above the top quartile, so any error at all will be counted as &amp;quot;underconfidance.&amp;quot;</text></comment>
<story><title>The Dunning-Kruger effect is autocorrelation</title><url>https://economicsfromthetopdown.com/2022/04/08/the-dunning-kruger-effect-is-autocorrelation/</url></story><parent_chain><item><author>bitshiftfaced</author><text>The authors did &amp;quot;X - Y vs X,&amp;quot; but that&amp;#x27;s not even the biggest problem. The authors subtracted two measures that had been transformed and bounded from 0 to 1 (think percentiles). What happens at the extremes of those bounds? How much can your top performers overestimate their performance? They&amp;#x27;re almost at 1 already, so not much. If they were to overestimate and underestimate at the same rate and by the same magnitude in terms of raw values, the ceiling effect on the transformed values means that the graph will make it look like they underestimate more often. The opposite problem happens for the worst performers.&lt;p&gt;See &amp;quot;Random Number Simulations Reveal How Random Noise Affects the Measurements and Graphical Portrayals of Self-Assessed Competency.&amp;quot; Numeracy 9, Iss. 1 (2016), particularly figures 7, 8, and 9.</text></item></parent_chain><comment><author>dclowd9901</author><text>I think if people at all levels of skill were reasonably good at measuring their own ability, we would see two curves that roughly overlap. Instead we see the graph given.&lt;p&gt;The fact that random noise can generate a mean curve on the Y axis doesn’t mean DK doesn’t exist. It just means DK’s mean self analysis resembles a middling random mean, which if you think about it, makes sense. Most people will probably self evaluate as average, regardless of their actual skill. This means DK is right as rain.</text></comment>