Document ID: chunk:federal_register_of_legislation:F2013C00288:reg:1850:p106
Version: federal_register_of_legislation:F2013C00288
Segment Type: reg
Provision Reference: reg 1850 (pt 106/117)
Character Range: 663019–665850

type I error (false positive decision error) — rejecting the hypothesis as false when it is really true
    * type II error (false negative decision error) — accepting the hypothesis as true when it is really false.
In order for decision rules (or tests of hypotheses) to be sound, they must be designed to minimise decision errors. This is not always simple, as for any given sample size, an attempt to decrease one type of error is generally accompanied by an increase in the other type of error. The only way to reduce both types of error is to increase the sample size, which may or may not be always possible.
In testing a given hypothesis, the maximum probability with which we would be willing to accept a type I error is referred to as the 'level of significance' or significance level of the test. A significance level of 0.05 or 0.01 is commonly adopted, although other values are used.

If for example the 0.05 (or 5%) significance level is selected for a decision rule, then we are accepting that there is a 1 in 20 (that is, 5 chances in 100) chance that we would reject the hypothesis when it should be accepted; that is, we are about 95% confident that we have made the right decision. In this case we say that the hypothesis has been rejected at the 0.05 significance level, which means that the hypothesis has a 0.05 probability of being wrong.

    19              Appendix C: Assessment of data quality

19.1          Assessment of reliability of field procedures and laboratory results
Source: NSW DEC, 2006.
Contaminated site practitioners should undertake an assessment of the reliability of field procedures and analytical results using the data quality indicators (DQI) of precision, accuracy, representativeness, completeness and comparability. DQI should be used to document and quantify compliance or otherwise with the requirements of the project SAQP.

19.2          QA/QC analytical methods
The DQI for chemical data will differ depending on which analytical methods have been used in a site assessment. These fall into three main categories:
    * field methods
    * laboratory screening methods
    * methods specific for contaminants that are known or expected to be present at a site.

19.3          Field methods
The following issues should be documented and discussed in assessment reports:
    * the applicability and limitations of field methodologies where used
    * instrument calibration and validation of field measurements, and comparison with laboratory results
    * the significance of the results of field screening methods compared with the results of laboratory analyses, for example, that the results reported for field screening using a photo-ionisation detector are compatible with the results reported by the laboratory for volatile organic compounds. Where not compatible, an