Development of Default Distributions

Pesticide regulation makes use of measurements of specific fate and effects properties, as specified in laws such as the US Federal Insecticides Fungicides and Rodenticides Act (FIFRA). Studies are conducted according to relatively standardized designs. Particularly in this type of situation, it seems reasonable to develop default distributions for particular variables, as measured in particular, standardized studies. Default assumptions may relate to default distribution types, or default distribution parameters such as a coefficient of variation, skewness, or knrtosis. Default distributions may be evaluated in comparative studies that draw from multiple literature sources. Databases of pesticide fate and effects properties, such as those maintained by the USEPA Office of Pesticide Programs, may be useful for such comparative analyses. 

Default distributions can be evaluated at 2 levels 1) in comparative studies, one may compare how often alternative distributions better describe the data, e.g., based on goodness-of-fit statistics, and 2) before applying a default distribution in a particular situation, one should evaluate whether the distribution agrees with whatever data are available for that situation. 

It is good to keep in mind that there will be a certain rate of false positives, i.e., incorrect rejection of an appropriate distribution, and a certain rate of false negatives. It is sometimes suggested that one should routinely evaluate some set of distributions 

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