> the statistical truth that the worst 10% probably are negative value employees.
Why is this a statistical truth? In a large, Gaussian distributed population MAYBE this is true (but hiring isn’t random, is it?). But the thresholds are not applied to large populations anywhere I’ve worked. It’s pressure applied to management at all levels.
It assumes that you have perfect measurement. Anyone who knows corporate politics, middle management machiavellianism, and inability to properly instrument development, requirements, scheduling knows this is a fantasy.
That means people will devote SUBSTANTIAL amounts of their day-to-day labor in jockeying and positioning.
People will be hired (wasted money and time) explicitly to be fired/PIP'd.
The jockeying will result in disruption of team development.
People will sabotage their coworkers.
Trust disappears.
Paranoia grows.
Blame deflected.
Difficult problems won't be tackled because why risk it.
Bandaids rather than solutions will result.
YOUR BEST PEOPLE WILL LEAVE. Let me emphasize this. You are selecting for people that aren't good, because good people will have OPTIONS and THEY WILL LEAVE.
What are you left with? A management hierarchy of sociopaths. A subpar set of employees that furthermore will not cooperate in good faith (in the best case) or actively engage in institutional sociopathic behavior. You will have a cesspool, and cesspools repulse good people.
And the signal is right in front of people that apply: the hiring process is abusive hazing with the "raise the bar" bullshit.
Staff turnover means abandoned systems. Abandoned systems will be avoided as bad career risk, or reimplemented before the org has gotten good ROI from them.
THAT IS NOT GOOD MANAGEMENT.
Remember: AWS had a CRAP Christmas. Crap. This is starting to boil up.
It's even worse than that. The Office at least had people do their jobs and keep their heads down, cooperate to get things done, ride out the management bullshit.
Amazon management practices will breed sociopathy at the root worker level.
I agree that is kind of the average-stage evolution of an enterprisey company. Amazon is worse.
The Office was utopian compared to real-life Corporate America.
The kind of mean-spirited pettiness that drives actual post-2000 capitalism, and it gets worse the higher you go, is so depressing and repugnant that it can't be put on TV.
Also, characters with no redeeming qualities are considered bad writing. But most coprorate executives are people with no redeeming qualities. You can't put them in fiction; unlike a well-written villain, they'll suck everything into a hole. Even Bill Lumbergh on Office Space had his charms.
Amazon's hiring criteria is that a candidate must be better then 50% of current employee in their role (the bar); this is where the "bar raiser" concept comes from. So from their perspective statistically, half of their current employees wouldn't make it through the interview process if they went through it again.
This is magical numbers thinking by shape rotators.
How does one qualify the new candidate being better than 50% of current team.
Better than 50% at what?
Every role has many dimensions, and most members of the team have a mixed set of strengths. You form a cohesive package if you hire complementary people and get 2+2=5 type of output.
The idea that everyone can be treated as an interchangeable cog and objectively, quantitatively ranked is pure silliness.
The central limit theorem doesn't give us one of those. Not even the Lindeberg-Feller central limit theorem. Neither does the strong law of large numbers. Nor the weak law of large numbers. Nor the martingale convergence theorem.
There is some old material, from about 100 years ago, in schools of education that given a performance measure and a large population the measure will be Gaussian distributed on that population, and the larger the population the closer to a Gaussian distribution. An example is supposed to be the size of the largest eigenvalue in a principle components decomposition. Uh, that largest eigenvalue was long called IQ (intelligence quotient). Nothing intelligent about it. Nonsense. 100% total crackpot nonsense. Brain-dead, cult nonsense.
"Low performers" in need of a "performance improvement program" (PIP)? Start with the managers who believe in a Gaussian distribution. Then move to the managers who believe that the "bottom 20%" are always low performers.
A lot is known about how to be a good manager, and the Gaussian probability density distribution is not part of that.
I've seen a lot of bad managers. A pattern is that they are tyrants, have lots of rules and measures, are big on formality over reality, dot i's and cross t's with great reliability, and have everyone with their nose to the grindstone, ear to the ground, shoulder to the wheel and trying to work in that position. But the organization is stagnant, is not changing or keeping up with the market or technology. So, after a few years like that, it becomes obvious to everyone, customers, BoD, stockholders, employees, even nearly all of the managers that the organization is about ready to die, and often that's what happens, e.g., by firing 50% of the people and going downhill from there.
Once I saw a failing organization where nearly all the managers except at the lowest level were part of a tight clique, cult enforcing failure. Finally the BoD installed a new CEO and fairly soon 80+% of the clique, cult were given "PIPs" and demoted out of management or retired.
But there is also good evidence that even such a sick organization does not have to die and, instead, just needs some good management, starting at the CEO level. In particular, with a good CEO, suddenly, wonder of wonders, 90+% of the employees can be seen as great performers.
So, net, as bad as the situation can be, it is fair to say that Darwin is on the case with improvement on the way!
At one time Amazon was a small mail-order book and record shop run by Bezos from a small office. At least looking from 10,000 feet up, Bezos was a good manager, and, thus, I doubt that he was playing with the Gaussian distribution, firing his bottom 20%, stack ranking, etc. Instead he knew all the employees and the work of each. Sooooo, it sounds like now Bezos should leave outer space, return to Amazon, and fix the destructive nonsense that is ruining the company.
> "Low performers" in need of a "performance improvement program" (PIP)? Start with the managers who believe in a Gaussian distribution. Then move to the managers who believe that the "bottom 20%" are always low performers.
Maybe I missed your point, but qualitative phenotypes caused by interactions of many genes that are measureable, like height, are gaussian distributed.
Height cannot be Gaussian if only because height is never negative but every Gaussian density is positive for all real numbers, including negative real numbers.
"interactions of many genes that are measureable"
The interactions of many is commonly used to justify a Gaussian assumption, but there is no such theorem. The main theorem to get a Gaussian density is the central limit theorem which usually assumes an infinite sequence of independent identically distributed random variables (plus some), i.e., i.i.d. The i.i.d. is asking a LOT!!! Asking so much that in practice it is essentially unrealistic.
What people often mean by Gaussian is just a central peak, some symmetry, and long tails, but an actual Gaussian distribution has a lot more, e.g., sufficient statistics (as in a classic paper by Halmos and Savage, with a derivation in M. Loeve's two volumes, from Springer, Probability) which, as I recall, E. Dynkin showed are quite sensitive to deviations from actual Gaussian.
Gaussian is important in a lot of derivations -- a LOT is known. But in practice Gaussian has some utility but only as an approximation where don't need to be very careful about accuracy.
I am not really disagreeing with you, but for a first order approximation, treating human height as a bunch of iids is the simplest approach. https://www.nature.com/articles/s41431-021-00836-7.
"At the same time, it is well-understood that additivity of effects and normal distribution of residuals is only an approximation to the distribution of height in human populations. .... However, up until now, no evidence for non-additive genetic interactions was found for height [6, 18]. Moreover, if non-additive effects were to exist for polygenic traits, very large sample sizes would be required to detect them [9]."
> treating human height as a bunch of iids is the simplest approach.
Sounds fine.
If you treat heights as some i.i.d.s, then can use the central limit theorem to argue that in the limit for large samples (we get convergence in distribution) the probability density distribution of the sum of heights expressed as a z-score (mean 0, standard deviation 1) has Gaussian probability density distribution.
But that does not mean that the probability density distribution of heights is Gaussian. Indeed, if include both males and females, then for the density likely get two peaks instead of just one. If also include children, then get a left tail longer than the right one.
So, net, we cannot expect that heights are Gaussian. And we will have a tough time finding a large population that is Gaussian. z-scores in the limit for large samples Gaussian -- sure, can get that. A large population Gaussian, not much chance.
Why is this a statistical truth? In a large, Gaussian distributed population MAYBE this is true (but hiring isn’t random, is it?). But the thresholds are not applied to large populations anywhere I’ve worked. It’s pressure applied to management at all levels.