The bias of an estimator is the difference between the expected value of the estimator and the true value of the parameter being estimated. A biased estimator systematically overestimates or underestimates the parameter, while an unbiased estimator has an expected value equal to the true parameter value.

Bias Of An Estimator

bias of an estimator

difference between the expected value

expected value of the estimator

true value of the parameter

A biased estimator is a statistical estimator whose expected value does not equal the true value of the parameter being estimated, leading to systematic errors in estimation. Recognizing and correcting for bias is crucial in statistical analysis to ensure accurate and reliable results.

biased estimator

systematically overestimates

Systematically underestimates

An unbiased estimator is a statistical technique used to estimate a population parameter, where the expected value of the estimator equals the true parameter value, ensuring that it does not systematically overestimate or underestimate. This property is crucial for ensuring the accuracy and reliability of statistical inferences drawn from sample data.

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