
Error from finite sample size, which falls as the square root of the number of events.Statistical uncertainty is the inherent error in any measurement or estimate that arises because you have only a finite number of observations. If you flip a coin 10 times, you might get 7 heads, but that doesn't mean the coin is biased — the discrepancy is due to statistical uncertainty. The key property is that this uncertainty decreases as you collect more data, specifically as the square root of the number of events. So to halve the uncertainty, you need four times as many observations.
The square-root law was established by German physicist and mathematician Carl Friedrich Gauss in the early 19th century, building on earlier work by Abraham de Moivre on probability distributions.
Imagine counting random events, like radioactive decays or customer arrivals. The count you observe fluctuates around a true average. The size of that fluctuation is roughly the square root of the count. If you observe 100 events, the uncertainty is about 10; if you observe 10,000, it's about 100. This scaling is a consequence of the Poisson distribution, which governs independent random events. The relative uncertainty (uncertainty divided by the count) thus shrinks as 1/√N, making large samples more precise.
It is used everywhere from particle physics (estimating the significance of a new particle signal) to opinion polls (calculating the margin of error from a sample of voters).
If you want to double the precision of a measurement, you must quadruple the amount of data — a fact that frustrates experimentalists in every field.




