Random Sampling

Random sampling is a statistical technique used in data science to select a subset of data points from a larger dataset. The selection of data points is done randomly, without any bias or preference towards any particular data point. Random sampling is used to ensure that the selected subset is representative of the larger dataset and can be used to make accurate inferences about the population from which the data was collected. This technique is commonly used in machine learning, where a random sample of data is used to train a model, and in hypothesis testing, where a random sample is used to test a hypothesis about the population.
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