Thus, a sample will be representative or not, only if it was selected at random, i.e., that all the subjects of the target population had the same possibility of being selected in this sample and therefore be included in the study, and on the other hand, that the number of subjects selected numerically represent the population that gave rise to it with respect to the distribution of the variable under study in the population, that is, the estimation of the sample size. Consequently, the analysis of a sample allows us to make inferences or generalize conclusions to the target population with a high degree of certainty, such that a sample is considered representative of the target population, when the distribution and value of the different variables can be reproduced with calculable error margins. So, the aim of sampling is to study the relationships between the distribution of a variable in the target population and the distribution of the same variable in the study sample. For this purpose, it is essential, among other things, to define the inclusion criteria (clinical, demographic, temporal and geographical characteristics of the subjects that make up the study population) and the exclusion criteria (characteristics of the subjects that may interfere with the quality of the data Or the interpretation of results). The aim of this manuscript is to provide general knowledge regarding sampling techniques most commonly used in clinical research.
KEY WORDS: Sampling Studies; Study; Sampling; Probability Sample; Samples; Probability.