Accuracy, Precision, and Bias
We consider both bias and precision with respect to how well an estimator performs over many, many samples of the same size. The average of these multiple samples is called the expected value of the estimator.
Bias is a measure of how far the expected value of the estimate is from the true value of the parameter being estimated.
Precision is a measure of how similar the multiple estimates are to each other, not how close they are to the true value (which is bias).
Precision and bias are two different components of Accuracy.