Possible bias due to sampling problems or low response rates has been a troubling "nuisance" variable in empirical research since seminal and classical studies were done on these problems at the beginning of this century. Recent research suggests that: (1) earlier views of the alleged bias problem were misleading; (2) under a variety of fairly well-specified conditions, allegedly biased samples are in fact random; and (3) "de facto" biased samples can and will "jump" from being biased to being random samples. These conditions occur when the population is fuzzy and dynamic and when the number of factors structuring the population and those influencing responding or non-responding are large, interact with each other, and have a trivial effect, at best. Researchers and theorists may be rejecting and disregarding what might be reasonable and highly valid data. Actual data from a number of state-wide studies with response rates in the 15% to 30% range illustrate this view. A variety of simulation studies is needed to evaluate these views on bias. The Catastrophe Theory of non-linear functions, family-wise error rates, and fuzzy subset theory are also considered. (Author/SLD)