Sample representativeness is the condition that allows the results observed in a sample to be extrapolated to the accessible population or the target population. In this regard, a sample can be considered representative if two fundamental requirements are met. First, participants must be selected using a random sampling procedure, ensuring that all individuals in the target population have the same probability of being included in the study. Second, the sample size must be adequate, meaning that it should numerically reflect the population of origin with respect to the distribution of the variable(s) under study, which requires an appropriate estimation of sample size. Under these conditions, sample analysis enables inference, extrapolation, and generalization of conclusions to the target population with a high degree of certainty. Accordingly, a sample is regarded as representative of the target population when the distribution and values of the variables can be reproduced within calculable margins of error. From this perspective, the primary objective of sampling is to examine the relationship between the distribution of one or more variables in the target population and their distribution in the sample under study. To achieve this, it is essential, among other aspects, to clearly define the inclusion criteria, which specify the clinical, demographic, temporal, and geographical characteristics of the study population, as well as the exclusion criteria, which identify characteristics that may interfere with data quality or the interpretation of the results. The aim of this manuscript is to provide an update study document regarding the most commonly used sampling strategies in epidemiology and clinical research.

KEY WORDS: "Sampling Studies"[Mesh]; "Probability"[Mesh]; Study, Sampling; Probability Sample; Samples, Probability.