 Explanatory Notes, January 2004
 Explanatory Notes, February 2004
 Explanatory Notes, March 2004
 Explanatory Notes, April 2004
 Explanatory Notes, May 2004
 Explanatory Notes, June 2004
 Explanatory Notes, July 2004
 Explanatory Notes, August 2004
 Changes in this issue, August 2004
 Explanatory Notes, September 2004
 Explanatory Notes, October 2004
 Explanatory Notes, November 2004
 Explanatory Notes, December 2004
 Glossary
 Abbreviations
 Data Quality Issues (Appendix), January 2004
 Data Quality Issues (Appendix), February 2004
 Data Quality Issues (Appendix), March 2004
 Data Quality Issues (Appendix), April 2004
 Data Quality Issues (Appendix), May 2004
 Data Quality Issues (Appendix), June 2004
 Data Quality Issues (Appendix), July 2004
 Data Quality Issues (Appendix), August 2004
 Data Quality Issues (Appendix), September 2004
 Data Quality Issues (Appendix), October 2004
 Data Quality Issues (Appendix), November 2004
 Data Quality Issues (Appendix), December 2004
 Trend Revisions (Technical Note), January 2004
 Standard Errors (Technical Note), January 2004
 Standard Errors (Technical Note), February 2004
 Trend Revisions (Technical Note), February 2004
 Trend Revisions (Technical Note), March 2004
 Standard Errors (Technical Note), March 2004
 Trend Revisions (Technical Note), April 2004
 Standard Errors (Technical Note), April 2004
 Trend Revisions (Technical Note), May 2004
 Standard Errors (Technical Note), May 2004
 Trend Revisions (Technical Note), June 2004
 Standard Errors (Technical Note), June 2004
 Trend Revisions (Technical Note), July 2004
 Standard Errors (Technical Note), July 2004
 Trend Revisions (Technical Note), August 2004
 Standard Errors (Technical Note), August 2004
 Trend Revisions (Technical Note), September 2004
 Standard Errors (Technical Note), September 2004
 Trend Revisions (Technical Note), October 2004
 Standard Errors (Technical Note), October 2004
 Trend Revisions (Technical Note), November 2004
 Standard Errors (Technical Note), November 2004
 Trend Revisions (Technical Note), December 2004
 Standard Errors (Technical Note), December 2004
 Data Source

STANDARD ERRORS
RELIABILITY OF ESTIMATES
Estimates based on a sample are subject to sampling variability, that is, they may differ from those that would be obtained from full enumeration.
The sampling error associated with any estimate can be estimated from the sample results and one measure so derived is the standard error. Given an estimate and the standard error on that estimate, there are about two chances in three that the sample estimate will differ by less than one standard error from the figure that would have been obtained from full enumeration, and about nineteen chances in twenty that the difference will be less than two standard errors. The relative standard error is the standard error on the estimate expressed as a percentage of the estimate.
It would be impractical to publish estimates of standard errors for all figures in individual tables. However, the following table of standard errors and relative standard errors gives an indication of the magnitude of the sampling error associated with any estimate of a particular size for shortterm and total movement.
APPROXIMATE STANDARD ERROR ON ESTIMATES FOR STRATIFIED SAMPLE 
 
 Shortterm arrival or departure of Australian resident
 Shortterm arrival or departure of overseas visitor
 Total arrival or departure
 
 Standard error  Relative standard error  Standard error  Relative standard error  Standard error  Relative standard error  
Estimated number of movements  no.  %  no.  %  no.  %  
 
5,000,000  13,296  0.3  9,334  0.2  11,417  0.2  
4,000,000  12,052  0.3  8,436  0.2  10,349  0.3  
3,000,000  10,613  0.4  7,403  0.2  9,113  0.3  
2,000,000  8,866  0.4  6,156  0.3  7,612  0.4  
1,000,000  6,506  0.7  4,488  0.4  5,582  0.6  
500,000  4,761  1.0  3,268  0.7  4,082  0.8  
100,000  2,283  2.3  1,559  1.6  1,951  2.0  
50,000  1,656  3.3  1,131  2.3  1,413  2.8  
10,000  778  7.8  535  5.4  660  6.6  
5,000  560  11.2  387  7.7  473  9.5  
2,000  361  18.0  252  12.6  304  15.2  
1,000  258  25.8  182  18.2  216  21.6  
750  224  29.9  159  21.2  188  25.0  
500  184  36.8  131  26.2  153  30.7  
400  165  41.2  118  29.5  137  34.3  
300  143  47.7  103  34.3  119  39.7  
200  117  58.6  85  42.4  97  48.6  
100  83  83.0  61  61.0  69  68.5  
 
An example of the use of this table is as follows. If the estimate of the number of Australian resident departures for shortterm visits abroad is 1,000, then the standard error on this estimate is 258 i.e. there are two chances in three that the actual number of Australian resident departures for shortterm visits abroad will lie between 742 and 1,258 and nineteen chances in twenty that it will lie between 484 and 1,516.
The larger the size of an estimate the smaller the relative standard error. For any estimate of greater than 5,000,000 the relative standard error will be less than 0.3%.
The estimate of the difference between an estimate in two different periods or between different estimates from the same period is also subject to sampling error. The standard error on the difference between any two estimates which are subject to sampling error can be approximated by using the larger standard error of the estimates inflated by a factor of 1.4.
An example of the use of this procedure is as follows. Assume the estimates of the number of arrivals to Australia from Germany during February 2003 and February 2004 are 7,500 and 10,000 respectively. The difference between the 2003 and 2004 figure is 2,500 and the standard errors on these estimates are approximately 461 and 535. The standard error on the difference is approximately 749 (1.4 x 535), and there are nineteen chances in twenty that the estimate of the difference between the two years will lie between 1,002 and 3,998.

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