Learning goal: For independent unbiased Gaussian readings, optimal linear weights are inverse-variance weights. Correlation changes the gain; contamination changes the model assumptions.

Strong shared noise can reduce averaging gains under the available weight constraints. Contamination creates a right tail and changes the variance from the uncontaminated reference.