BS ISO 21285:2019 pdf free download – Soil quality一Inhibition of reproduction of the soil mite (Hypoaspis aculeifer) by soil contaminants.
10 Statistical analysis
10.1 General
Most of test methods with sub-lethal end points, e.g. reproduction, involve quantitative effects, e.g. counting juvenile mites. Quantal effects can also be measured in the same test, such as mortality after two weeks exposure.
Guidance given here for statistical evaluation of test results is intended to inform investigator about problems that can arise in consequence of a test design selected. Computer programs do not necessarily guard against violations of rules that can cause erroneous analyses. It is strongly recommended to look for more information in specific guidance documents (e.g. as provided by Reference [9]) or to contact a statistician.
10.2 Single-concentration tests
Quantitative single-concentration tests (e.g. effects on reproduction) have different statistical methods. For sampling at several locations with field replication, analysis of variance (ANOVA) can be a first step if results are suitable. If the null hypothesis of no difference was rejected, analysis can proceed to one of several multiple-comparison tests[9J.
An example of a single-concentration test for quantitative effects can be counting juvenile mites as end point of effects on reproduction after exposure to a sample of undiluted contaminated soil, compared to numbers of offspring exposed to a reference or standard soil. If there was only one mixture tested, and one control sample, without any replicates, results cannot be compared by any statistical test. In a quantitative test with replication for the soil to be tested (material) and for the control soil, a standard t-test is suitable for statistical analysis.
ANOVA involving multiple comparisons of end point data derived for undiluted soil to be tested including field replicates of field-collected soil from more than one sampling location is commonly used for statistical interpretation of the significance of quantitative findings from soil toxicity tests. This is a hypothesis-testing approach, and is subject to appreciable weaknesses[21. The parametric analyses (e.g. ANOVA and multiple comparisons) for such data assume that the data are normally distributed, that the treatments are independent, and that the variance is homogenous among the different treatments. These assumptions shall be tested. If the data satisfy these assumptions, analysis may proceed. If not, data may be transformed and tested again. As parametric tests are reasonably robust in the face of moderate deviations from normality and equality of variance, parametric analysis should proceed, even if moderate nonconformity continues after transformationL9i. If the original or transformed data do not satisfy either test for distribution of data, then analysis by nonparametric methods shall be carried out.
10.3 Multi-concentration tests
10.3.1 Range-finding test
If a clear dose-response is obvious, ER/EC-values can be estimated by using regression techniques like logistic regression function or probit analysis. In other cases, the effect range should be determined by expert knowledge.