This topic is written about better elsewhere.

Here's an opinion piece in the Harvard Business Review in 2022: https://hbr.org/2022/01/we-need-to-let-go-of-the-bell-curve

Here's another article on the same topic from 2014: https://www.forbes.com/sites/joshbersin/2014/02/19/the-myth-...

Here's more press on the same topic from 2012: https://www.npr.org/2012/05/03/151860154/put-away-the-bell-c...

From a data science point of view, if you want to compare the fitness of different distributions to data, go ahead and do some fitness tests, like AIC or BIC, to compare distributions. Ordinary Gaussian outperforms skew-normal and log-normal in many settings where the physics of the measurements would suggest otherwise.

However, it matters what you are measuring.

Here's a summary quote that explains what this Pareto versus Gaussian stuff is talking about:

> "We found that a small minority of superstar performers contribute a disproportionate amount of the output."

That is very different than saying that employee performance is Pareto instead of Gaussian distributed. "Output" and "employee performance" measures two different things. If there is any big picture flaw to all of this: it is quintessentially Individual Contributor to conflate output with employee performance.

Another POV is that people who get fired from IC jobs understandably lament a lot of the details of their circumstances. One detail that comes up is that other people take credit for their work, which should illuminate how output and employee performance measure different things in a way that interacts in the opposite of what the article is advocating for.