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TECHNICAL NOTE DATA QUALITY
4 As the estimates in this publication are based on information relating to a sample of businesses, they are subject to sampling variability, that is, they may differ from the estimates that would have been produced if the information had been obtained from all businesses.
5 The difference between estimates obtained from a sample of businesses, and the estimates that would have been produced if the information had been obtained from all businesses, is called sampling error. This should not be confused with inaccuracy that may occur because of imperfections in reporting by respondents or in processing by the ABS. Please see the section on Non-Sampling Error for more detail regarding these types of errors. The expected magnitude of the sampling error associated with any estimate can be estimated from the sample results. One measure of sampling error is given by the standard error (SE), which indicates the degree to which an estimate may vary from the value that would have been obtained from a full enumeration (the 'true' figure). There are about two chances in three that a sample estimate differs from the true value by less than one standard error, and about nineteen chances in twenty that the difference will be less than two standard errors.
6 An example of the use of a standard error is as follows. From the publication, the estimated total expenditure on R&D was $18,321,322, with a standard error of $333,448. There would be about two chances in three that a full enumeration would have given an estimate in the range $17,987,874 to $18,654,770 and about nineteen chances in twenty that it would be in the range $17,654,426 to $18,988,218.
7 In this publication, indications of sampling variability are measured by relative standard errors (RSEs). The relative standard error is a useful measure in that it provides an immediate indication of the percentage errors likely to have occurred due to sampling, and thus avoids the need to refer to the size of the estimate. RSEs are obtained using the formula: RSE = SE/estimate x 100. RSEs are shown in the Relative Standard Error tables in this section. RSEs for all data included in this release (including data cube content) are available upon request.
8 Estimates with RSEs between 25% and 50% are annotated to indicate they are subject to high sample variability and should be used with caution. In addition, estimates with RSEs greater than 50% have been included and annotated to indicate they are considered too unreliable for general use. In the publication, the symbol '*' indicates an estimate has an RSE of between 25% and 50%, and estimates with the symbol '**' have an RSE greater than 50%. All cells in the data cubes 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.
COMPARABILITY OF ESTIMATES OVER TIME
9 The comparability of estimates over time may be affected by the following changes in classifications:
10 Revisions to previous cycle data occur on discovery of:
11 Revisions are applied up to two cycles prior to the current cycle, but only where the impact on:
12 In processing 2011-12 data, revisions were applied to 2009-10 and 2010-11 estimates. Revisions must be taken into consideration when interpreting results, particularly when comparing estimates over time.
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