TECHNICAL NOTE 2 DATA RELIABILITY
ABS SURVEY DATA
1 For 2005-06 the mining collection was a sample survey designed primarily to deliver national industry subdivision and selected class estimates. Industry division estimates (excluding Subdivision 15 Services to mining) for states and territories for key data variables are also produced, but the survey was not specifically designed for these purposes.
Sampling error
2 The majority of data in Chapters 1 to 3 of this publication have been obtained from a sample of mining businesses. As such, these data are subject to sampling variability; that is, they may differ from the figures that would have been produced if the data had been obtained from all mining businesses in the population. The measure of the likely difference as used by the ABS is given by the standard error, which indicates the extent to which an estimate might have varied by chance because the data were obtained from only a sample of units. There are about two chances in three that a sample estimate will differ by less than one standard error from the figure that would have been obtained if the data had been obtained from all units, and about 19 chances in 20 that the difference will be less than two standard errors.
3 The standard error can also be expressed as a percentage of the estimate, and this is known as the relative standard error (RSE).
4 Relative standard errors at the industry subdivision and selected class level for Australia for selected data items representing the full range of data contained in this publication are shown in the table below. The relative standard errors of the selected estimates for the states and territories are mainly 5% or less. Detailed relative standard errors can be made available on request.
5 To illustrate the above, the estimate of sales and service income for total mining in 2005-06 was $96,063m. The RSE of this estimate is shown as 1.1%, giving a standard error of approximately $1,057m. This implies that there are two chances in three that, if all units had been included in the survey, an estimate in the range of $95,006m to $97,120m would have been obtained. Similarly, it implies that there are 19 chances in 20 (i.e., a confidence interval of 95%) that the estimate would have been within the range of $93,949m to $98,177m.
RELATIVE STANDARD ERRORS |
|  |
| | Employment at
end of June | Wages and
salaries(a) | Sales and
service income(b) | Industry
value added | |
| Industry | % | % | % | % | |
| |
| 11 Coal mining | 3.0 | 3.8 | 1.5 | 1.0 | |
| 12 Oil and gas extraction | 1.7 | 1.0 | 0.5 | 0.2 | |
| 13 Metal ore mining | | | | | |
| 1311 Iron ore mining | - | - | - | - | |
| 1313 Copper ore mining | - | - | - | - | |
| 1314 Gold ore mining | 6.3 | 5.0 | 4.4 | 7.5 | |
| 1315 Mineral sand mining | 0.1 | 0.1 | 0.1 | 0.1 | |
| 1317 Silver-lead-zinc ore mining | 8.6 | 6.9 | 10.0 | 16.7 | |
| 1312, 1316 and 1319 Bauxite mining,
nickel ore mining and metal ore mining n.e.c. | 0.3 | 3.0 | 0.6 | 0.6 | |
| Total metal ore mining | 2.4 | 1.6 | 1.9 | 2.6 | |
11-13 Total coal mining, oil and gas extraction
and metal ore mining | 1.7 | 1.9 | 0.9 | 0.8 | |
| 14 Other mining | 9.4 | 5.0 | 11.4 | 6.9 | |
| 15 Services to mining | 6.4 | 5.9 | 6.0 | 10.7 | |
| 11-15 Total mining | 2.4 | 1.9 | 1.1 | 0.9 | |
| |
| - nil or rounded to zero (including null cells) |
| (a) Excludes the drawings of working proprietors. |
| (b) Includes rent, leasing and hiring income. |
6 The size of the RSE may be a misleading indicator of the reliability of some of the estimates for trading profit, OPBT, EBITDA and IVA. Estimates of these variables may legitimately include positive and negative values, reflecting the financial performance of individual businesses. In these cases the aggregated estimate can be small relative to the contribution of individual businesses, resulting in a standard error which is large relative to the estimate.
Non-sampling error
7 All data presented in this publication are subject to non-sampling error.
8 The imprecision due to sampling variability, which is measured by the standard error, should not be confused with inaccuracies that may occur because of inadequacies in available sources from which the population frame was compiled, imperfections in reporting by providers, errors made in collection such as in recording and coding data, and errors made in processing data. Inaccuracies of this kind are referred to collectively as non-sampling error and they may occur in any enumeration, whether a full census or a sample.
9 For the purpose of compiling the estimates in this publication, businesses in the ATO maintained population (see Technical Note 1) are coded to ANZSIC industry classes on the basis of the activity reported to the ATO when they registered for an ABN. There are a number of reasons why a business classified to any given ANZSIC industry on the ABS Business Register may not have been mainly engaged in activities associated with that industry during the 2005-06 reference year. This may be because of inaccurate or incomplete information at the time the business was registered or it may be because the business has changed activity, either temporarily or permanently.
10 Although it is not possible to quantify non-sampling error, every effort is made to reduce it to a minimum. Collection forms are designed to be easy to complete and assist businesses to report accurately. Efficient and effective operating procedures and systems are used to compile the statistics. The ABS compares data from different ABS (and non-ABS) sources relating to the one industry, to ensure consistency and coherence.
NON-ABS DATA
11 The mineral production data shown in Chapter 4 are mainly compiled from data produced by the state and Northern Territory departments responsible. For information about the comparability of these data, please see the introduction to that chapter.
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