Reliability of the estimates
Statistics based on the household and establishment surveys 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, and this variability is measured by the standard error of the
estimate. There is about a 90-percent chance, or level of confidence, that
an estimate based on a sample will differ by no more than 1.6 standard
errors from the "true" population value because of sampling error. BLS
analyses are generally conducted at the 90-percent level of confidence.
For example, the confidence interval for the monthly change in total
employment from the household survey is on the order of plus or minus
290,000. Suppose the estimate of total employment increases by 100,000
from one month to the next. The 90-percent confidence interval on the
monthly change would range from -190,000 to 390,000 (100,000 +/- 290,000).
These figures do not mean that the sample results are off by these
magnitudes, but rather that there is about a 90-percent chance that the
"true" over-the-month change lies within this interval. Since this range
includes values of less than zero, we could not say with confidence that
employment had, in fact, increased. If, however, the reported employment
rise was half a million, then all of the values within the 90-percent
confidence interval would be greater than zero. In this case, it is likely
(at least a 90-percent chance) that an employment rise had, in fact,
occurred. At an unemployment rate of around 4 percent, the 90-percent con-
fidence interval for the monthly change in unemployment is about +/- 270,000,
and for the monthly change in the unemployment rate it is about +/- .19
percentage point.