This lecture discusses the criteria for good estimators, focusing on consistency and efficiency. Consistency is crucial, but not sufficient for a good estimator. The lecture also covers bias, mean squared error, and the trade-off between bias and variance. It explores the concept of efficiency in estimators and the importance of unbiasedness. Practical examples and theoretical foundations are provided to illustrate these concepts.