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Making inferences for any probability or non-probability survey requires some reliance on modeling assumptions.Those assumptions should be made clear to the user and evidence of the effect that departures from those assumptions might have on the accuracy of the estimates should be identified to the extent possible.RDS is increasingly used for sampling rare and hard to interview groups where probability sampling methods are often not feasible.The last of these three sections discusses a set of post hoc adjustments that have been suggested as ways to reduce the bias in estimates from non-probability samples; these adjustments use auxiliary data in an effort to deal with selection and other biases.This probably is the greatest need if non-probability methods are to be used more broadly in survey research.The concept of fitness for use also is explored and seems to have great relevance for non-probability samples, as well as for probability samples. Conclusions and Recommendations Unlike probability sampling, there is no single framework that adequately encompasses all of non-probability sampling. Over about the last 60 years most have used a probability-sampling framework.

A second approach is network sampling, including respondent driven sampling.Surveys at the lower and upper ends of the continuum are relatively easy to recognize by the effort associated with controlling the sample and post hoc adjustments.The difficulty arises in placing methods between these two extremes and assessing the risks associated with inferences from these surveys.These models typically attempt to use important auxiliary variables to improve fit and usability.Once the model is formulated, standard statistical estimation procedures such as likelihood-based or Bayesian techniques are then used to make inferences about the parameters being estimated.

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