Because estimates are based on a sample rather than the entire population, the published estimates may differ from the actual, but unknown, population values. In principle, many random samples could be drawn and each would give a different result. This is because each sample would be made up of different businesses who would give different answers to the questions asked. The spread of these results is the sampling variability.
Common measures of the variability among these estimates are the sampling variance, the standard error, and the coefficient of variation (CV). The sampling variance is defined as the squared difference, averaged over all possible samples of the same size and design, between the estimator and its average value. The standard error is the square root of the sampling variance. The CV expresses the standard error as a percentage of the estimate to which it refers. For example, an estimate of 200 units that has an estimated standard error of 10 units has an estimated CV of 5 percent. The CV has the advantage of being a relative, rather than an absolute, measure and can be used to compare the reliability of one estimate to another.