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Detailed explanation-1: -In general, sampling errors can be placed into four categories: population-specific error, selection error, sample frame error, or non-response error. A population-specific error occurs when the researcher does not understand who they should survey.

Detailed explanation-2: -Sampling error can be defined as a statistical error that occurs when a researcher fails to select a sample that is representative of the entire population.

Detailed explanation-3: -Which of the following best describes sampling error? Sampling error occurs when messages or people are inadvertently selected from a subset of the population.

Detailed explanation-4: -For supervised learning applications in machine learning and statistical learning theory, generalization error (also known as the out-of-sample error or the risk) is a measure of how accurately an algorithm is able to predict outcome values for previously unseen data.