Document ID: chunk:federal_register_of_legislation:F2013C00288:reg:1850:p105
Version: federal_register_of_legislation:F2013C00288
Segment Type: reg
Provision Reference: reg 1850 (pt 105/117)
Character Range: 660463–663247

DQOs
    * the optimum manner in which to collect the data required to meet the objectives for the assessment and which will meet the project DQOs
    * the SAQP.

18.3          Notes about decision errors and decision-making
Decision errors are incorrect decisions caused by using data that is not representative of site conditions due to sampling or analytical error. As a result, a decision may be made that site clean-up is not needed when really it is, or vice versa.

There are two types of decision error:
    * sampling errors occur when the sampling program does not adequately detect the variability of a contaminant from point to point across the site. That is, the samples collected are not representative of the site conditions (e.g. an appropriate number of representative samples have not been collected from each stratum to account for estimated variability)
    * measurement errors occur during sample collection, handling, preparation, analysis and data reduction.
The combination of the above errors is referred to as 'total study error'. This directly affects the probability of making decision errors. Study error is managed through the correct choice of sample design and measurement systems. Note that the attainment of a nominated probability generally requires use of a statistically based sampling plan.

The possibility of making a decision error, although small, is undesirable because of the adverse consequences arising from that incorrect decision. Decision error can be controlled through the use of hypothesis testing. This test can be used to show either that the baseline condition is false (and therefore the alternative condition is true) or that there is insufficient evidence to indicate that the baseline condition is false (and therefore the site assessor decides by default that the baseline condition is true).

The burden of proof is placed on rejecting the baseline condition, because the test hypothesis structure maintains the baseline condition as being true until overwhelming evidence is presented to indicate that the baseline condition is not true.

The null hypothesis is an assumption assumed to be true in the absence of contrary evidence, for example, that the site is contaminated unless proved to be clean.

If we reject a hypothesis when it should be accepted, we say that a type I error has been made. If, on the other hand, we accept a hypothesis when it should be rejected, we say that a type II error has been made. In either case, a wrong decision or error in judgment has occurred:
    * 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