Abstract: All scientific studies are based on samples. The representativeness of the sample is essential for generalization of research findings. The need for high representativeness of the study population depends largely on the nature of the research question. In studies of disease burdens, for example, the researcher's concern is some current specific facts about a specific population, which can be clearly defined and from which drawing a representative sample is both important and possible. In contrast, in studies of causes of disease and effectiveness of treatment, for example, the researcher is interested to find a law of nature in all relevant populations, which are an abstract entity and from which drawing a representative sample is impossible. The difficulty of obtaining representativeness is also inversely related to the variation (like interaction) of the studied phenomenon. Furthermore, overemphasis on representativeness may lead to inevitable compromises in quality control, induce biases, and eventually decrease internal validity, making the gained representativeness compromised. Therefore, research on disease causes and treatment effectiveness relies on repeatedly testing in different populations so as to approach the target population to which the findings can be applied to and which is the totality of the populations represented by the study populations in all the relevant studies. Having said all the above, it is important to note that all studies should draw their samples in a way they represent the population from which the samples are drawn. This forms the basis for the statistical inference and the validity and generalization of epidemiological findings. In addition, any studies based on big data are also sampling studies, in which unfortunately the sampling population is often unclear, which makes generalization of research findings difficult.