In statistics, the difference between an<unk> 's expected value and the true value of the parameter being estimated is called the<unk> . An<unk>  or decision rule having nonzero<unk>  is said to be<unk> ed.
Although the term<unk>  sounds pejorative, it is not necessarily used in that way in statistics.<unk> ed<unk> s may have desirable properties.  Not only do they sometimes have a smaller mean squared error than any unbiased<unk> , but in some cases the only unbiased<unk> s are not even within the convex hull of the parameter space, so their use is absurd.
Bias of an estimator
Attributable risk
Cognitive bias
Random variable
Absolute risk reduction
Sample size
Length time bias
Absolute deviation