TECHNICAL NOTE 2 DATA RELIABILITY


RELIABILITY

1 The estimates in this release are based on information obtained from a sample survey and from administrative data collected by the ATO. Any collection of data can be affected by factors that impact the reliability of the resulting statistics, regardless of the methodology used. These factors result in non-sampling error. In addition to non-sampling error, sample surveys are also subject to inaccuracies that arise from selecting a sample rather than conducting a census. This type of error is called sampling error.


Sampling error

2 The majority of data contained in 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. One measure of the likely difference 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 all units had been included in the collection, and about nineteen chances in twenty that the difference will be less than two standard errors.

3 Sampling variability can also be measured by the relative standard error (RSE), which is obtained by expressing the standard error as a percentage of the estimate to which it refers. The RSE is a useful measure in that it provides an immediate indication of the percentage errors likely to have occurred due to the effects of random sampling, and this avoids the need to refer also to the size of the estimate. Selected data item RSEs at the industry subdivision and selected class level for Australia are shown in the table overleaf. Detailed relative standard errors are available on request.

4 To illustrate, the estimate of sales and service income for Total mining in 2011-12 was $218,276m. The RSE of the estimate is shown as 0.3%, giving a standard error of approximately $655m. Therefore, there are two chances in three that, if all units had been included in the survey, a figure in the range of $217,621m to $218,931m would have been obtained, and nineteen chances in twenty (i.e., a confidence interval of 95%) that the figure would have been within the range of $216,966m to $219,586m.

5 The size of the RSE may be a misleading indicator of the reliability of the estimates for (a) operating profit before tax, (b) earnings before interest, tax, depreciation and amortisation and (c) industry value added. It is possible for an estimate legitimately to include positive and negative values, reflecting the financial performance of individual businesses. In this case, 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.

Relative Standard Errors
	Employment at end June	Wages and Salaries	Sales and service Income	Total Income	Total expenses	Industry value added
	%	%	%	%	%	%
06 Coal mining	1.7	1.6	0.3	0.5	1.3	1.5
07 Oil and gas extraction	0.9	0.4	0.2	0.2	0.6	0.4
0801 Iron ore mining	0.2	0.1	0.2	0.2	0.1	0.3
0803 Copper ore mining	0.3	0.2	0.3	0.3	0.1	0.5
0804 Gold ore mining	3.0	4.0	1.8	1.7	1.9	2.5
0805 Mineral sand mining	0.7	2.7	1.3	1.1	1.6	1.0
0807 Silver-lead-zinc ore mining	1.1	0.9	0.9	1.5	1.0	1.2
0802, 0806 and 0809 Bauxite mining ,nickel ore mining and other metal ore mining	11.9	1.0	1.0	1.1	1.1	5.1
08 Metal ore mining	1.8	0.9	0.3	0.3	0.4	0.4
06-08 Total coal mining, oil and gas extraction and metal ore mining	1.2	0.7	0.2	0.2	0.6	0.4
09 Non-metallic mineral mining and quarrying	5.1	3.6	2.7	6.1	2.9	5.5
10 Exploration and other mining support services	2.5	2.6	2.3	2.3	2.5	4.1
B Total mining	1.1	0.8	0.3	0.3	0.6	0.4

Non-sampling error

6 Error other than that due to sampling may occur in any type of collection, whether a full census or a sample, and is referred to as non-sampling error. All data presented in this publication are subject to non-sampling error. Non-sampling error can arise from 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. It also occurs when information cannot be obtained from all businesses selected. The imprecision due to non-sampling variability cannot be quantified and should not be confused with sampling variability, which is measured by the standard error.

7 Although it is not possible to quantify non-sampling error, every effort is made to minimise it. 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.

8 Differences in accounting policy and practices across businesses and industries can also lead to some inconsistencies in the data used to compile the estimates. Although much of the accounting process is subject to standards, there remains a great deal of flexibility available to individual businesses in the accounting policies and practices they adopt.

9 The above limitations are not meant to imply that analysis based on these data should be avoided, only that the limitations should be considered when interpreting the data presented in this publication. This publication presents a wide range of data that can be used to analyse business and industry performance. It is important that any analysis be based upon the range of data presented rather than focusing on one variable.


REFERENCE PERIOD

10 Where businesses were unable to supply data for the 12 months ended 30 June, an accounting period for which data can be provided is used for data other than those relating to employment.

11 Estimates of financial data in Mining are heavily impacted by fluctuating commodity prices. Thus the reporting by businesses for an accounting period that is not for the period ended 30 June, can result in different estimates compared with what they would have been, had the businesses reported for an accounting period ended 30 June.

12 In November 2011, the ABS released an information paper on the impact that businesses reporting for accounting periods other than those ended 30 June had on the estimates present in past versions of this release. In the second half of 2013, the ABS will release this paper updated to show the impact of Off-June reporters on estimates presented in this release.


QUALITY INDICATORS

13 In the 2011-12 survey of Australian Mining businesses, there was an 86.6% response rate from all businesses that were surveyed and found to be operating during the reference period. Data were imputed for the remaining 13.4% of operating businesses. This imputation contributed 2.9% to the estimate of sales and service income for Total mining.