The 'average' meaning does not depends on context but:
- on the distribution
- on whether by 'average' you mean 'mean' (in statistics, arithmetic mean) or 'median' (what value cuts the population in half)
The GP gives an example of people being above average by having two legs. I will take a similar example but in a classroom.
Say students in a classroom take a test scored from 0 (worst) to 20 (best). The result of the test is as follows: forty students get a 11 and ten gets a 1. That makes a mean of (11x40+10)/50 => 9. Hence all forty students are above average. If we plot the number of people per mark this gives a very special (and artificial) distribution since we made it up to make the point that it is possible for most people to be above average. In more realistic scenarios and unless skewed by external factors (bad test, wrong population, cheating) the distribution is often close to being normal, i.e mean and median are approximately the same (hence it's an impossibility to have 80% of people above mean).
Yet what's really interesting in Dunning-Kruger tests is not the actual results of the tests, it is the comparison between one's own assessment and real results, which actually abstracts from distribution problems and 'average' discussions, since each one person is compared to oneself. In the above whacky scenario we could have expected people getting a 1 to evaluate themselves scoring a 8.
If you don't want to read the whole paper, just look at the quartile/percentile graphs in the paper. They speak for themselves.
When the vast majority of people fall into the 60-100 range, but it's still possible to get zero's (ex: cheating) you don't end up with a normal distribution. This is one of the reasons most standardized tests give percentile results.
IMO, the Dunning-Kruger effect relates to this. Compared to people that regularly ride horses I am probably at the bottom 5 percentile. However, I am better than most people because most people have never ridden a horse. As people improve the list of people they compare themselves to shrinks faster than their skills improve. Being the worst player in the MBA makes you better than 99.99999% of people, but that does not let you keep your job.
- on the distribution
- on whether by 'average' you mean 'mean' (in statistics, arithmetic mean) or 'median' (what value cuts the population in half)
The GP gives an example of people being above average by having two legs. I will take a similar example but in a classroom.
Say students in a classroom take a test scored from 0 (worst) to 20 (best). The result of the test is as follows: forty students get a 11 and ten gets a 1. That makes a mean of (11x40+10)/50 => 9. Hence all forty students are above average. If we plot the number of people per mark this gives a very special (and artificial) distribution since we made it up to make the point that it is possible for most people to be above average. In more realistic scenarios and unless skewed by external factors (bad test, wrong population, cheating) the distribution is often close to being normal, i.e mean and median are approximately the same (hence it's an impossibility to have 80% of people above mean).
Yet what's really interesting in Dunning-Kruger tests is not the actual results of the tests, it is the comparison between one's own assessment and real results, which actually abstracts from distribution problems and 'average' discussions, since each one person is compared to oneself. In the above whacky scenario we could have expected people getting a 1 to evaluate themselves scoring a 8.
If you don't want to read the whole paper, just look at the quartile/percentile graphs in the paper. They speak for themselves.