It's also just embarrassing that this is supposed to be a data science blog about employee performance and the only non-simulated data directly presented or discussed is the US wage distribution, where the author has just cavalierly marked the x-axis as "Performance". There's all this spew, and the author makes claims about what good data scientists do ... and there's no data in this discussion that's directly relevant to their rambling claims.
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.
