### U.S. Bureau of Labor Statistics

##
Job Openings and Labor Turnover Survey

## Reliability of JOLTS Estimates

### Confidence Intervals and Comparing JOLTS Estimates

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.

#### Sampling and Nonsampling Error

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