Machine Learning Resources

What is Stratified Sampling?

At a high level, sampling can be divided into probability sampling and non-probability sampling. Probability sampling means that each unit in the population has some probability of being selected. The most common types of probability sampling include:

Stratified Sampling: A stratified sample identifies an attribute that separates a population, such as gender, race/ethnicity, or geographic state, and then samples in such a way that ensures each class is proportionally represented across the study. For example, if 50% of students at a college are from the United States, 25% are from China, and 25% are from India, a stratified sample would be formed by randomly sampling students of each nationality to maintain a similar proportion in the sample compared to the population

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