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.