Forcing a bell curve in performance reviews creates several significant problems:
Artificial scarcity of high performance – A bell curve assumes that only a fixed percentage of employees can be top performers, regardless of actual performance levels. If you have a strong team where most people are genuinely performing well, you’re forced to artificially label some as “average” or “below average.”
Destroys collaboration – When employees know they’re competing against each other for limited “high performer” slots, it incentivises hoarding information, avoiding knowledge sharing, and even sabotaging colleagues. This is particularly damaging in roles that require teamwork.
Punishes small teams disproportionately – In a team of 5 people, forcing one person into the bottom 20% means someone gets labelled as underperforming even if all five are actually doing brilliant work. The statistical assumption breaks down with small sample sizes.
Ignores actual performance data – Performance isn’t naturally distributed in a bell curve across teams. Some teams might genuinely have mostly strong performers (especially after good recruitment and development), whilst others might have more improvement needs. Forcing a predetermined distribution ignores reality.
Creates unfair outcomes – High performers might be rated as “average” simply because there are too many good people on their team, whilst average performers on weaker teams might be rated as “high performers.” This destroys the credibility of the entire review process.
Demotivates good employees – Nothing kills morale faster than being told you’re “bottom 10%” when you know you’re doing solid work, just because someone had to fill that slot.
The alternative is to evaluate people against clear performance standards and role expectations, not against each other. This allows for more accurate feedback and fairer treatment whilst still maintaining accountability.
