Excellence Isn’t What You Think It Is

DSC08778 by Yasunobo Hiraoka, (=)
DSC08778, courtesy of Yasunobo Hiraoka.

In human resources you might spend a lot of time talking about employee performance and what it means to be excellent, average, and under-performing.  But your conversations about performance might be trapped in a decades-long mathematical error which skews your subjective judgements.  It’s worth exploring assumptions about the bell curve performance distribution, if only to be a little wiser when you “use your words.”

You will often hear that high-performing employees are three times as productive as an average performer.  I looked into it, and it’s not true.  The differences in performance are far greater, in some cases six-to-one or more.  I tracked down a good academic article by two Organizational Behaviour academics, tucked behind a paywall.  The paper is “The Best and the Rest: Revisiting the Norm of Normality of Individual Performance.”  Ernest O’Boyle Jr. and Herman Aguinis.  Personnel Psychology 2012, 65, 79-119.

The authors note there is a decades-long consensus that employee performance is distributed on a bell curve.  However, this consensus might be way off base.  Through a series of studies, they show that the distribution of performance more closely resembles a power-law distribution.  Here’s what the two distribution curves look like side-by-side.

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The blue diagram should be familiar to most people as “the bell curve.”  There are a bunch of proper names for it, but the features are well-known.  The largest number of people is really close to the peak in the middle, which in this case is the average performance score.  A bunch of people are a little to the left or little to the right of the average, and those are your below-average and above-average performers.  Then there are tails on either side of the curve; those are the rare low and high performers who are often about to get terminated or promoted.

There’s another way to look at it.  The rose-colored diagram is called a “power-law” distribution.  (Note that in this case the axes are reversed so that performance runs up-down and percentage runs left-right).  This diagram can reflect the likelihood that you will buy a rock album, with you and millions of others buying Beatles and Broken Bells on the far left of the curve, and dozen people buying albums from your own band on the far right.  The important thing to notice about the power-law distribution is that there’s a lot of activity on the far-left side of the diagram where the high performers are satisfying customers.

Researchers tested employee performance in a number of fields and found that performance more closely resembles the rose-colored diagram, the power-law distribution.

The research isn’t supposed to turn out that way, according to mainstream thinking.  The authors get into why we assume that performance fits this bell curve, and it’s not flattering to the legacy of social scientists.  Their zinger is that this is a “received doctrine” passed down from one decade to the next.  Yet if you trace the doctrine back to earlier sources, nobody can name that one study where they proved that the bell curve made sense in the first place.  Rather, there is lots of evidence of people fudging their data and throwing out the “outliers” to get their model to fit the doctrine.

The areas of performance that they studied were entertainers, university professors, politicians, and athletes.  There’s a small vulnerability (which they acknowledge) that these industries might not reflect all sectors.  In my opinion these are all star-system fields with a winner-take-all rewards system, and that system isn’t true of all types of work.  But on the upside, they have chosen fields with lots of data, and where our personal perceptions match the data.  We have heard of Sinatra, Einstein, Reagan, and Babe Ruth.  We understand that people just below the top ranking in these fields are barely known.

According to the math, the power-law distribution implies that top performers deliver the goods to a far greater extreme than originally thought.  People who perform at two standard deviations above the mean – a common measure for high performance – would be four times as productive under the bell curve.  Looking at actual performance, which more closely matches the power-law distribution, the correct multiple is seven times as productive.  At the top one-tenth of one percent of performance, the bell curve says they are six times as productive but power-law says they are twenty-five times as productive.

What about people who are below-average?  With the bell curve, the below-average people are one-half of the population because the average cuts the distribution in half.  It’s almost like a democracy.  But because superstars deliver so much more under the power-law distribution, it skews the average and creates a larger pool of people who are below average.  With a power-law distribution, below-average performers are 66% of academics, 83% of actors, 68% of politicians, and 71% of professional basketball players.

This research has many implications.  For example, those who have excelled might want to keep all of the gains for themselves, opening a controversy about the distribution of the spoils.  However the authors flag that excellence does not exist in a vacuum and all of these people are surrounded by support systems that cause them to be great.  Perhaps the gains from high performance need to be re-invested into these support systems to sustain excellence over the long-term.  Some superstars also engage in anti-social behaviours because of fawning admirers and their employer’s reluctance to terminate.  These behaviours make great gossipy television, but it doesn’t look good during lawsuits.  It is also unclear what traits cause these people to be superstars and whether these traits can be developed. Could we choose to create superstars then cast them aside every few years and start again?  Isn’t that what boy bands are all about?

I have worked in a couple of fields with a number of employers, and I have experienced diverse feedback about my own performance.  Let’s give others the benefit of the doubt and assume that the feedback is accurate.  Could it be that each of us is exceptional at one or two things, and mediocre at the rest?  Does it sound about right that we are more exceptional in some environments than others?  There is over a thousand professions on this earth and there are many workplaces.  How do you find that one skill and that one workplace where you rock the world?  …but enough about me and you.

If you work in human resources, how do you help other employees find that one thing?  If you’re in public policy, how do you organize a modern economy so that more people can find that perfect fit?  Would you ear-tag individual employees by type, place them into known jobs, prescribe what they ought to learn, and judge them against the average?  Or would you assess employees for their past moments of magic, foster intrinsic motivation, cultivate them to experience bursts of growth, build the work around their talents, and encourage them migrate into roles that are best for them?  As you can see, core mathematical assumptions have a big impact on how we talk to one another as humans.

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