Students in statistics service courses are frequently exposed to dogmatic approaches for evaluating the role of randomization in statistical designs, and inferential data analysis in experimental, observational and survey studies. In order to provide an overview for understanding the inference process, in this work some key statistical concepts in probabilistic and nonprobabilistic sampling are discussed. The statistical model constituting the basis of statistical inference is postulated and a brief review of the finite population descriptive inference and a quota sampling inferential theory are provided. Some comments on distinct approaches for conducting inferences in probabilistic and nonprobabilistic samples are adduced. (Contains 1 table.)