Variance

You're seeing Variance when monthly revenue swings wildly even though the annual average looks fine. The average masks the risk — high variance means any single month could be a crisis.

You're seeing Variance when a team's output is brilliant one sprint and mediocre the next. The average output may be acceptable, but the spread signals process or talent inconsistency.

Track the variance of your key metrics, not just the mean. When variance is high, invest in systems that narrow the spread — better processes, smaller experiments, diversified channels — before optimising for a higher average.

Simons built Renaissance Technologies on the principle that understanding variance is more important than chasing returns. His Medallion Fund succeeded not by making the biggest bets but by making thousands of small, statistically validated bets where the variance of each was well understood and controlled. Simons treated unexplained variance as the enemy — every anomaly was investigated, every model stress-tested against distributional assumptions. Founders can adopt this by measuring the spread of their key outcomes, not just the average. When variance is high and unexplained, don't scale — investigate. Reduce the variance first, then compound. Simons showed that disciplined variance management turns modest edges into extraordinary, reliable performance over time.