It appears the Forbes author did not understand the underlying article, or the concepts well. His graph axes of the Power Law distribution and comments regarding it show a lack of understanding. The axes should be performance and one of probability or frequency. He has the performance axis, but has substituted 'total number of people' for frequency/probability. Thus making this a CDF graph, not a PDF graph. But the cincher is the author's comments "Roughly 10-15% of the population are above the average (often far above the average), a large population are slightly below average, and a small group are far below average" The original research shows the largest group is the lowest performers, and a proper interpretation of the Power Law distribution applied here also indicates the same. The author states the largest group is "slightly below average". The graph comments reflect this misinterpretation also. His Power Law interpretation better reflects an interpretation of the Normal distribution, not a Power Law distribution.

Agreed. It wasn't at all what I was expecting him to say as I read the first part of the article. What I thought he was going to say was this: the normal distribution is only the prior -- what you would get if you hired people randomly. Hiring is difficult, of course, and there is inevitably a certain amount of randomness in the process, but once you have culled the low performers once -- or succeeded in not letting them in in the first place -- you no longer have a normal distribution, and you shouldn't act as if you did.