Researchers (O’Boyle & Aguinis, 2012) have just challenged a perennial assumption of the job performance literature. They say that the distribution of individual performance does not follow a “normal” (or Gaussian) distribution, but rather a “power” (or Paretian) distribution. On the surface this challenge may seem academic, but if true, this conclusion could have serious implications for how job performance, and the methods and tools used to assess it, are conceptualized and put into practice.
NORMAL DISTRIBUTION VERSUS POWER LAW
We are all too familiar with the inverted U-shaped distribution that is called a normal distribution. If data occurs in this form, it means that most people being measured cluster toward the average, while any extreme scores or deviations from this shape indicate bias or error. Instead, the authors of this study embrace extreme scores by arguing that the underlying distribution of job performance more closely follows the ski jump-shaped distribution that is called a power law, or Paretian distribution. In this distribution, the tails are fatter and extend farther than the normal distribution, and extreme events are more commonly expected. A helpful way to think about this distribution is the 80/20 rule often used in economics—20% of performers are responsible for 80% of the results.
The researchers tested this assumption by collecting performance outcomes from 198 samples that spanned an eclectic mix of researchers, entertainers, politicians, and athletes. Using careful statistical methods, they found that 93% of their samples fit a Paretian distribution better than a Gaussian distribution; in other words, most of the positive performance outcomes were generated by a small group of superstar performers.
PRACTICAL IMPLICATIONS
What does this mean for the workplace? Researchers may need to rethink their generally accepted practice of removing outliers and defaulting to statistical tests that assume a normal distribution when studying performance outcomes. Practitioners may need to alter their methods of utility analysis, which measures the ROI of performance measurement, to more accurately reflect this new assumption. Also, measures that track performance or are intended to select high performers may need to be adjusted to account for the “superstar effect.” Overall, the authors suggest that organizations would be well served by properly identifying, managing, compensating, and leveraging their elite performers.
O’Boyle Jr., E., & Aguinis, H. (2012). The best and the rest: Revisiting the norm of normality of individual performance. Personnel Psychology, 65, 79-119.
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