6537.0 - Government Benefits, Taxes and Household Income, Australia, 2003-04  
ARCHIVED ISSUE Released at 11:30 AM (CANBERRA TIME) 13/06/2007   
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The estimates in this publication are based on information obtained from the occupants of a sample of dwellings. The accuracy of the estimates refers to how close the estimates are to the true population value. The discrepancy between the value of sample estimates and the population value results from two types of errors, sampling and non-sampling errors. Paragraph 35 of the Explanatory Notes discusses non-sampling errors in the estimates while this Appendix provides more information about sampling errors (or sampling variability).

One measure of the likely difference due to sampling variability is given by the standard error (SE), which indicates the extent to which an estimate might have varied because only a sample of dwellings was included. There are about two chances in three that the sample estimate will differ by less than one SE from the figure that would have been obtained if all dwellings had been included, and about 19 chances in 20 that the difference will be less than two SEs. Another measure of the likely difference is the relative standard error (RSE), which is obtained by expressing the SE as a percentage of the estimate.

For estimates of population sizes, the size of the SE generally increases with the level of the estimate, so that the larger the estimate the larger the SE. However, the larger the sampling estimate the smaller the SE in percentage terms (RSE). Thus, larger sample estimates will be relatively more reliable than smaller estimates.

In the tables in this publication, estimates with RSEs of 25% or more are considered unreliable for most purposes. Estimates with RSEs greater than 25% but less than or equal to 50% are annotated by an asterisk to indicate they are subject to high SEs and should be used with caution. Estimates with RSEs of greater than 50%, annotated by a double asterisk, are considered too unreliable for general use and should only be used to aggregate with other estimates to provide derived estimates with RSEs of 25% or less.

Space does not allow for the separate indication of the SE of all the estimates in this publication. RSEs for all tables are provided on the ABS web site in an excel spreadsheet <https://www.abs.gov.au> (see Statistics: Access to all ABS products and statistics, by Catalogue Number 65. Consumer income and expenditure, Government Benefits, Taxes and Household Income, Australia, cat.no. 6537.0). The RSEs have been derived using the group jackknife method.


Proportions and percentages

Proportions and percentages, which are formed from the ratio of two estimates, are also subject to sampling errors. The size of the error depends on the accuracy of both the numerator and the denominator. For proportions where the denominator is an estimate of the number of households in a grouping and the numerator is the number of households in a sub-group of the denominator group, the formula for the RSE is given by

Equation: RSE (x divided by y) = square root of ([RSE (x)] squared - [RSE (y)] squared)

Differences between estimates

The difference between survey estimates is also subject to sampling variability. An approximate SE of the difference between two estimates (x-y) may be calculated by the formula:

Equation: SE (x - y) = square root of ([SE(x)] squared - [SE(y)] squared)

This approximation can generally be used whenever the estimates come from different samples, such as two estimates from different years or two estimates for two non-intersecting subpopulations in the one year. If the estimates come from two populations, one of which is a subpopulation of the other, the SE is likely to be lower than that derived from this approximation, but there is no straightforward way of estimating how much lower.