All sample estimates have inherent variability. The true population value estimated from a sample may be lower or higher than the sample point estimate. Comparing sample estimates to determine if they are different in terms of statistical significance requires the use of a statistical measure called the standard error of the estimate. Using standard errors allows the data user to probabilistically determine if the difference between sample estimates is within the bound of natural variability or if the difference exceeds the bound of natural variability. In other words, the data user can determine if the difference between sample estimates is considered statistically significant.
All sample estimates, including the JOLTS estimates, are subject to both sampling and nonsampling error. When a sample rather than the entire population is surveyed, there is a chance that the sample estimates may differ from the "true" population values they represent. The exact difference, or sampling error, varies depending on the particular sample selected. Sampling error is a generic term for standard error.
Sample estimates also are affected by nonsampling error. Nonsampling error can occur for many reasons, including the failure to include a segment of the population, the inability to obtain data from all units in the sample, the inability or unwillingness of respondents to provide data on a timely basis, mistakes made by respondents, errors made in the collection or processing of the data, and sampling and nonsampling error in the independent population controls of employment (also known as benchmark employment) data used in JOLTS estimation. This nonsampling error may be partially reflected in the standard error of an estimate. For more information on nonsampling error and benchmarking, see the JOLTS Handbook of Methods.
Significance testing is a technique used to compare estimates using the estimates