Document ID: chunk:federal_register_of_legislation:F2013C00288:reg:1850:p11
Version: federal_register_of_legislation:F2013C00288
Segment Type: reg
Provision Reference: reg 1850 (pt 11/117)
Character Range: 375780–379375

sampling programs. The advantages and disadvantages of judgemental and probability-based sampling are listed in Table 2.
Table 2. Advantages and disadvantages of probability-based and judgemental sampling
               Probability-based                                                              Judgemental
Advantages         * Designs are unbiased                                                         * Can be less expensive than probabilistic designs
                   * Provides ability to calculate uncertainty associated with estimates          * Can be very efficient with a reliable and full site history
                   * Provides reproducible results within uncertainty limits                      * Easy to implement
                   * Provides ability to make statistical inferences
                   * Can handle decision error criteria
Disadvantages      * Random locations may be difficult to locate and implement on the ground      * Depends on expert knowledge
                   * An optimal design depends on an accurate CSM                                 * Cannot reliably evaluate precision of estimates
                                                                                                  * Depends on subjective judgement to interpret data relative to study objectives
                                                                                                  * Designs are biased

Judgemental sampling designs involve selection of sampling locations based on expert knowledge or professional judgement. The value of judgemental sampling depends on the DQOs, the study size and scope, and the degree of professional judgement available to locate and interpret the data. When judgemental sampling is used in isolation, quantitative statements about the level of confidence in the results cannot be made.

Probability-based designs (such as random, systematic, grid, stratified, transect and composite sampling) apply statistical sampling theory and may involve random selection of sampling locations. An essential feature of this type of sampling is that each member of the population from which the sample is selected has a known probability of selection. When a probability-based design is used, quantitative conclusions (or statistical inferences) may be made about the sampled population from the analytical results. For example, the assessor may calculate a 95% upper confidence level (UCL) of the arithmetic mean for the parameter of interest, say, lead concentrations in soil. If comparing this with the relevant investigation levels, the assessor can state whether the data indicates that the concentration exceeds or is below the investigation levels with a certain level of confidence (in this example 95%). Expert judgement is then used to draw conclusions about the study area based on the results of the sample data. Data analysis is discussed further in Section 13.

    6.2.1          Judgemental sampling
In judgemental sampling, the selection of samples (number, location, timing, etc.) is based on knowledge of the site and professional judgement. Sampling is localised to known or potentially contaminated areas identified from knowledge of the site either from the site history or an earlier phase of site investigation. Judgemental sampling is commonly used to investigate sub-surface contamination issues in site assessment.

Although judgemental sampling can invalidate some statistical methods, particularly where the sampling size is small, alternative methods using non-parametric approaches can be used. Further