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I am confused with the concepts of stratified sampling and oversampling for imbalanced datasets. From what I read in this question here: why-use-stratified-cross-validation-why-does-this-not-damage-variance-related , stratified sampling helps to make sure the sample contains the sample proportion of each class, so that the sample is representative.

But in many other posts I have read that oversampling rare events for imbalanced datasets is some helpful technique to handle high false positive or false negative problems.

Aren't the 2 sampling ideas contrary with each other? As in one is to make sure the sample is representative of the population, while the other is balancing the sample classes manually? I know I might confuse some fundamental concepts here. Can anyone help?

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