Topic: Performance, Performance Appraisal
Publication: Personnel Psychology
Article: The best and the rest: Revisiting the norm of normality of individual
Authors: O’Boyle Jr., E., & Aguinis, H.
Reviewer: Neil Morelli
The gloves are off because O’Boyle and Aguinis have just challenged a perennial assumption of the performance literature. What kind of challenge you say? The authors advocate 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 performance, and the methods and tools used to assess it, are conceptualized and valued.
We are all too familiar with the inverted U-shaped normal distribution and its inferences that most performers hang out around the mean, while any extreme scores or deviation from this shape indicate bias or error. Instead, O’Boyle and Aguinis embrace extreme scores by arguing that the underlying distribution of performance more closely follows the ski jump-shaped Paretian distribution. In this distribution the tails are fatter and extend farther than the normal distribution, and extreme events are more accurately predicted. A helpful way to think about this distribution is the 80/20 rule common to economics—20% of performers are responsible for 80% of the results.
O’Boyle and Aguinis tested this assumption by collecting performance outcomes from 198 samples that spanned an eclectic mix of researchers, entertainers, politicians, and athletes. They compared chi-square values between models that forced the data to fit to a normal, Gaussian distribution and a power, Paretian distribution. They found that 93% of their samples fit to a Paretian distribution better than a Gaussian distribution; in other words, most of the performance outcomes were generated by a small group of superstar performers.
What does this mean for researchers? The generally accepted practice of removing outliers and defaulting to statistical tests that assume a normal distribution when studying performance outcomes may need to be rethought. Practitioners? Utility analysis, which shows the ROI of performance measurement, can be more accurate by working under this new assumption. Also, measures that track performance or are intended to select high performers may need to be readjusted 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.