RELIABILITY OF THE ESTIMATES
1 As the estimates in this publication are based on information obtained from a sample of persons, they are subject to sampling variability. That is, the estimates may differ from those that would have been produced had all persons been included in the survey.
2 One measure of the likely difference is given by the standard error (SE), which indicates the extent to which an estimate might have varied by chance because only a sample of persons was included. There are about two chances in three (67%) that a sample estimate will differ by less than one SE from the number that would have been obtained if all persons had been surveyed, and about 19 chances in 20 (95%) that the difference will be less than two SEs.
3 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.
4 RSEs for all estimates have been calculated using the Jackknife method of variance estimation. This involves the calculation of 30 'replicate' estimates based on 30 different subsamples of the obtained sample. The variability of estimates obtained from these subsamples is used to estimate the sample variability surrounding the estimate.
5 The Excel spreadsheet (in Downloads) contains all the tables produced for this release and the calculated RSEs for each of the estimates.
6 Only estimates (numbers or percentages) with RSEs less than 25% are considered sufficiently reliable for most analytical purposes. However, estimates with larger RSEs have been included. Estimates with an RSE in the range 25% to 50% should be used with caution while estimates with RSEs greater than 50% are considered too unreliable for general use. All cells in the Excel spreadsheet with RSEs greater than 25% contain a comment indicating the size of the RSE. These cells can be identified by a red indicator in the corner of the cell. The comment appears when the mouse pointer hovers over the cell.
CALCULATION OF STANDARD ERROR
7 Standard errors can be calculated using the estimates (counts or percentages) and the corresponding RSEs. For example, Table 1 shows that the estimated number of persons aged 15–24 years who participated in a cultural activity in the 12 months before interview was 1,035,400. The RSE corresponding to this estimate is 2.8%. The SE is calculated by:
8 Therefore, there are about two chances in three that the value that would have been produced if all dwellings had been included in the survey will fall within the range 1,006,400 to 1,064,400 and about 19 chances in 20 that the value will fall within the range 977,400 to 1,093,400. This example is illustrated in the diagram below:
PROPORTIONS AND PERCENTAGES
9 Proportions and percentages 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. A formula to approximate the RSE of a proportion is given below. This formula is only valid when x is a subset of y:
|10 As an example, using estimates from Table 1, of the 1,035,400 people aged 15–24 years who participated in a cultural activity, 45.5% or 470,600 were men. The RSE of the estimated number of people who were aged 15–24 years and participated in a cultural activity is 2.8% and the RSE of the estimated number of men aged 15–24 years who participated in a cultural activity is 4.3%. Applying the above formula, the RSE of the proportion is: |
Therefore, the SE for men aged 15–24 years who participated in a cultural activity is 1.5 percentage points (= 45.5 × (3.3/100)). Hence, there are about two chances in three the proportion of men aged 15–24 years who participated in a cultural activity is between 44.0% and 47.0% and 19 chances in 20 the proportion is within the range 42.5% to 48.5%.
Published estimates may also be used to calculate the difference between two survey estimates (of counts or percentages). Such an estimate is subject to sampling error. The sampling error of the difference between two estimates depends on their SEs and the relationship (correlation) between them. An approximate SE of the difference between two estimates (x–y) may be calculated by the following formula:
While this formula will only be exact for differences between separate and uncorrelated characteristics or sub populations, it provides a good approximation for the differences likely to be of interest in this publication.
A statistical significance test for a comparison between estimates can be performed to determine whether it is likely that there is a difference between the corresponding population characteristics. The standard error of the difference between two corresponding estimates (x and y) can be calculated using the formula in the Differences section. This standard error is then used to calculate the following test statistic:
If the value of this test statistic is greater than 1.96 then there is evidence, with a 95% level of confidence, of a statistically significant difference in the two estimates with respect to that characteristic. Otherwise, it cannot be stated with confidence that there is a real difference between the populations with respect to that characteristic.
This page last updated 29 October 2013