A rigorous graduate-level examination of probability sampling theory, focusing on the mathematical properties of estimators, the mechanics of selection bias, and the use of Monte Carlo simulations to validate sampling designs. Students explore simple random sampling, sampling frame errors, and the 'Big Data Paradox' through proofs and simulation logic.

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