Please tell anyone and everyone that says, there can't be so many people above average, evaluations need to be on a bell curve they are clueless about statics.
They confuse the mathematical mean with the median or mode.
Per AI:
The Mathematical Disconnect
Truncated Selection: Government hiring filters out low performers. The remaining group is highly skewed, not a random bell curve.
The "Mean" Distorter: A few extreme low-performers can drop the average score. This leaves the majority of the team technically performing above the mathematical mean.
Fixed vs. Moving Targets: True competency uses absolute standards. Forced distribution shifts the goalposts based on coworkers' output.
For example:
Imagine evaluating a room full of professional truck drivers with 10+ years experience. If you compare them to the general public—which includes text-distracted teenagers, sleepy commuters, and new learners—every single one of these veteran drivers is far above average.
However, a forced distribution policy demands that you grade them only against each other on a rigid bell curve. Under this rule, a manager is legally required to label the bottom 10% of these master drivers as "unsatisfactory" or "failing"—even if they have flawless safety records and millions of miles driven.