Survey researchers routinely conduct studies that use different methods of data collection and inference. Over about the last 60 years most have used a probability-sampling framework. More recently, concerns about coverage and nonresponse coupled with rising costs, have led some to wonder whether non-probability sampling methods might be an acceptable alternative, at least under some conditions. There is a wide range of non-probability designs that include case-control studies, clinical trials, evaluation research designs, intercept surveys, and opt-in panels, to name a few. Generally speaking, these designs have not been explored in detail by survey researchers even though they are frequently used in other applied research fields. In the fall of 2011 the AAPOR Executive Council appointed a task force “to examine the conditions under which various survey designs that do not use probability samples have scientific merit, that is, the methods produce study estimates that have desirable statistical properties for making inferences to a larger population.” A key feature of statistical inference is that it requires some theoretical basis and explicit set of assumptions for making the estimates and for judging the accuracy of those estimates. We consider methods for collecting data and producing estimates without a theoretical basis as not being appropriate for making statistical inferences. In this report, we have examined the strengths and weaknesses of various non-probability methods, considering the theoretical and, to some extent, empirical evidence. We do not claim to have produced an exhaustive study of all possible methods or fully examined all of the literature on any one of them. However, we believe that we have at least identified the most prominent methods, and examined them in a balanced and objective way.