Coincidentally, I also experienced a similar effect to the somewhat famous "two humped camel" programming aptitude test [1]. This paper argued that you can split candidates into two normal distributions based on aptitude. In my own tests, what I experienced was more of an extremely long tail on the right side, which is actually somewhat similar to the linked paper.
In our own tests, had a bunch of people fall into the 40-60% range, but there was a small bunch of people clustered around 100%, which are just people who could have scored 100%+ being binned down to the max score, and not a second normal distribution.
[1] http://www.eis.mdx.ac.uk/research/PhDArea/saeed/paper1.pdf
[0] http://retractionwatch.com/2014/07/18/the-camel-doesnt-have-...
If you have 100 people, 95 of them are competent and put out 6 resumes every 3 years (on average 2/year), and 5 of them are incompetent and put out 100 resumes every year, then in a typical year the average hiring manager will see 95x2 = 190 resumes from competent applicants and 5x100 = 500 resumes from incompetent applicants. Over 70% of your pile of resumes consists of incompetents even though they're only 5% of the population you're sampling from. (Numbers are all made-up.)
Article in a math journal here: http://www.maa.org/sites/default/files/pdf/upload_library/22...