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n the first section, our discussion runs in familiar statistical terms of bias and precision, or efficiency, or expected loss. Unbiasedness means being right on average, where the average is taken over an infinite number of repetitions using the same set of subjects in the trial, but with no limits on how far any one estimate is from the truth, while precision means being close to the truth on average; an estimator that is far from the truth in one direction half of the time and equally far from the truth in the other direction half of the time is unbiased, but it is imprecise.
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