Data and Models

Abstract

Data should be taken from an appropriate probabilistic sampling protocol or from a valid experimental design, which also involves a probabilistic component. These are important steps leading to a degree of scientific rigor. Such data often arise from probabilistic sampling of some kind and are said to be “representative.“ Outside of this desirable framework lie populations where such ideal sampling is largely unfeasible. For example, human populations are often composed of members that are heterogeneous to sampling. Thus, by definition, it is impossible to draw a random sample and such heterogeneity can lead to negative biases in estimators of population size. Estimators that are robust to such heterogeneity have been developed and these approaches have proven to be useful, but the standard error is often large. In general, care must be exercised to either achieve reasonably representative samples or derive models and estimators that can provide useful inferences from (the sometimes unavoidable) nonrandom sampling.