Description: The sampling strategy is a systematic plan designed to select a representative sample from a population in order to obtain data that facilitates analysis and decision-making. This approach is fundamental in data science and statistics, as it allows for inferring characteristics of a broader population without the need to examine each of its elements. Sample selection is carried out using various methods, which can be probabilistic or non-probabilistic, depending on the study’s objectives and the nature of the population. The main characteristics of a good sampling strategy include representativeness, randomness, and bias reduction, ensuring that the results obtained are valid and applicable to the population at large. The relevance of this strategy lies in its ability to optimize resources, time, and costs, allowing researchers and analysts to draw meaningful conclusions from a more manageable data set. In summary, the sampling strategy is an essential tool in quantitative research, facilitating the understanding of complex phenomena through efficient and effective data collection.
