Bias in inferences from samples constitutes one of the main sources of error in macrosociological studies. The problem is typically understood as one of "selection bias." What constitutes the population available for selection, by contrast, is rarely thought of as being problematic. This article works to remedy this oversight and to demonstrate that this factor is not to be overlooked—suggesting that the categorization of cases constitutes another important source of biased inferences. This article argues that, because the boundaries of most macrosociological concepts are fuzzy, samples formed according to the logic of crisp-set categorization might lead to "categorization bias." In order to avoid such a categorization bias, we argue that cases should be weighted depending on how strongly they resemble the prototypes of the categories for which one wants to arrive at generalizations. This article further argues that, by weighting for prototypicality, the logic of fuzzy boundaries of concepts and comparative and statistical methods can be combined. In the process, we provide an example of how weighting by prototypicality can be applied in the area of ethnic studies.