We Need to Redefine Normal

//We Need to Redefine Normal

We Need to Redefine Normal

Much of what we do in organisations in terms of performance assessment, training and salary grading is based on the idea that human performance follows a ‘Normal’ (or Gaussian) distribution. This is usually referred to as the bell curve. Practically all forced ranking systems are based on this assumption. Trouble is, it’s not true.

If you have ever been subjected to forced ranking, or even worse had to rank your team in such a system, you will know how it is next to impossible to have the vast majority of people rated as average and the same numbers allocated to over- and underperforming. The result is that good performers often get offended and underperformers may end up in the average group to make up numbers.

Thanks to some ground-breaking research done in the last couple of years we now finally know why the whole forced ranking thing never felt quite right – it is based on a flawed assumption. Human performance does not follow a bell curve at all, it instead follows a Pareto distribution (famous for its associated 80/20 rule), which is also known as a power law distribution.

The study, “The Best and the Rest: Revisiting the Norm of Normality of Individual Performance” by Ernest O’Boyle Jr. and Herman Aguinis, included 633,263 researchers, entertainers, politicians, and amateur and professional athletes. They found that results are remarkably consistent across industries, types of jobs, types of performance measures, and time frames and that individual performance follows a power law distribution.

To give you an idea how this is a rather big deal, compare the Normal and Pareto distributions in the image below:

bell-pareto-curves

The big difference in the distributions is quite obvious, even though both have the same average value! A Paretian distribution resembles a ski slope, with 80% performing below average, 10% around the middle, and 10% exceeding; meaning that the assumption that the typical performer is average is a myth of the bell curve assumption. The reason is that power law distributions have a ‘fat tail’ of overperformers and hyperperformers, this is the gray area on the right side of the graph. The results mirror the famous 80/20 rule, which assumes at 80% of the work is done by 20% of the people.

What do these findings mean in practice? Well, we need a new way of ranking performance for a start. But beyond the obvious, many leadership and training practices are also implicitly grounded in the bell curve assumption. They focus on shifting the performance of the majority of workers (the average) or the team, rather than on the 20% of people who do the majority of the work as displayed in a Paretian distribution. Any training and development that fails to specifically target those top performers who generate the majority of performance outcomes is likely to result in minimal impacts for the organisation.

Undoubtedly, such an approach would deeply challenge our notion of fairness. Industries that already take such a ‘winner takes all’ approach – politics, entertainment and sport – are seen as elitist and unfair. Schools have invested heavily in recent decades in creating more or less a mirror image of the Paretian distribution, where only a tiny minority of pupils is told they are below average and everyone else gets a price.

Nevertheless, we should start a discussion on how to better rank performance in line with these research findings and how to best invest in the top performers without alienating the rest of the workforce.

By |2014-05-31T08:45:03+00:00February 24th, 2014|Blog|Comments Off on We Need to Redefine Normal