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Why do we need sampling? What are the benefits for data analysis? When building models, we sometimes find that the amount of data required for training is too large, which prolongs the training time. The purpose of sampling is to select a representative finite sample from a large population and perform statistical analysis on it, thereby inferring the characteristics or properties of the overall population.

From this, we can see that a sample is a subset of the population. Sampling methods refer to how we reasonably select a subset of the population as a sample. Since the sample size is smaller than the population, we hope that the sample statistics calculated from the selected sample approximate the population statistics, minimizing the bias introduced by sampling.