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This lecture covers the concepts of bias, variance, and mean squared error in the context of parameter estimation. It discusses unbiased estimators, maximum likelihood estimation, and confidence intervals. The instructor explains how to calculate confidence intervals for unknown and known variances, using normal and student distributions. Examples are provided to illustrate the application of these concepts in practice.