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Statistics: Scientific method · Research methods · Experimental design · Undergraduate statistics courses · Statistical tests · Game theory · Decision theory
 This article is about bias of statistical estimators. For other uses in statistics, see Bias (statistics).
In statistics, bias (or bias function) of an estimator is the difference between this estimator's expected value and the true value of the parameter being estimated. An estimator or decision rule with zero bias is called unbiased. Otherwise the estimator is said to be biased.
In ordinary English, the term bias is pejorative. In statistics, there are problems for which it may be good to use an estimator with a small, but nonzero, bias. In some cases, an estimator with a small bias may have lesser mean squared error or be medianunbiased (rather than meanunbiased, the standard unbiasedness property). The property of medianunbiasedness is invariant under transformations, while the property of meanunbiasedness may be lost under nonlinear transformations.
Definition[]
Suppose we have a statistical model parameterized by θ giving rise to a probability distribution for observed data, , and a statistic θ^{^} which serves as an estimator of θ based on any observed data . That is, we assume that our data follows some unknown distribution (where is a fixed constant that is part of this distribution, but is unknown), and then we construct some estimator that maps observed data to values that we hope are close to . Then the bias of this estimator is defined to be
where E[ ] denotes expected value over the distribution , i.e. averaging over all possible observations .
An estimator is said to be unbiased if its bias is equal to zero for all values of parameter θ.
There are more general notions of bias and unbiasedness. What this article calls "bias" is called "meanbias", to distinguish meanbias from the other notions, the notable ones being "medianunbiased" estimators. The general theory of unbiased estimators is briefly discussed near the end of this article.
In a simulation experiment concerning the properties of an estimator, the bias of the estimator may be assessed using the mean signed difference.
Examples[]
Sample variance[]
Suppose X_{1}, ..., X_{n} are independent and identically distributed (i.i.d) random variables with expectation μ and variance σ^{2}. If the sample mean and uncorrected sample variance are defined as
then S^{2} is a biased estimator of σ^{2}, because
In other words, the expected value of the uncorrected sample variance does not equal the population variance σ^{2}, unless multiplied by a normalization factor. The sample mean, on the other hand, is an unbiased estimator of the population mean μ.
The reason that S^{2} is biased stems from the fact that the sample mean is an ordinary least squares (OLS) estimator for μ: It is such a number that makes the sum Σ(X_{i} − μ)^{2} as small as possible. That is, when any other number is plugged into this sum, the sum can only increase. In particular, the choice m = μ gives, first (or most outcomes)
and then
Note that the usual definition of sample variance is
and this is an unbiased estimator of the population variance. This can be seen by noting the following formula for the term in the inequality for the expectation of the uncorrected sample variance above:
The ratio between the biased (uncorrected) and unbiased estimates of the variance is known as Bessel's correction.
Estimating a Poisson probability[]
A far more extreme case of a biased estimator being better than any unbiased estimator arises from the Poisson distribution:^{[1]}^{[2]}: Suppose X has a Poisson distribution with expectation λ. Suppose it is desired to estimate
(For example, when incoming calls at a telephone switchboard are modeled as a Poisson process, and λ is the average number of calls per minute, then e^{−2λ} is the probability that no calls arrive in the next two minutes.)
Since the expectation of an unbiased estimator δ(X) is equal to the estimand, i.e.
the only function of the data constituting an unbiased estimator is
To see this, note that when decomposing e^{−λ} from the above expression for expectation, the sum that is left is a Taylor series expansion of e^{−λ} as well, yielding e^{−λ}e^{−λ} = e^{−2λ} (see Characterizations of the exponential function).
If the observed value of X is 100, then the estimate is 1, although the true value of the quantity being estimated is very likely to be near 0, which is the opposite extreme. And, if X is observed to be 101, then the estimate is even more absurd: It is −1, although the quantity being estimated must be positive.
The (biased) maximum likelihood estimator
is far better than this unbiased estimator. Not only is its value always positive but it is also more accurate in the sense that its mean squared error
is smaller; compare the unbiased estimator's MSE of
The MSEs are functions of the true value λ. The bias of the maximumlikelihood estimator is:
Maximum of a discrete uniform distribution[]
 Main article: Maximum of a discrete uniform distribution
The bias of maximumlikelihood estimators can be substantial. Consider a case where n tickets numbered from 1 through to n are placed in a box and one is selected at random, giving a value X. If n is unknown, then the maximumlikelihood estimator of n is X, even though the expectation of X is only (n + 1)/2; we can be certain only that n is at least X and is probably more. In this case, the natural unbiased estimator is 2X − 1.
Medianunbiased estimators[]
The theory of medianunbiased estimators was revived by George W. Brown in 1947:
An estimate of a onedimensional parameter θ will be said to be medianunbiased, if, for fixed θ, the median of the distribution of the estimate is at the value θ; i.e., the estimate underestimates just as often as it overestimates. This requirement seems for most purposes to accomplish as much as the meanunbiased requirement and has the additional property that it is invariant under onetoone transformation.^{[3]}
Further properties of medianunbiased estimators have been noted by Lehmann, Birnbaum, van der Vaart and Pfanzagl.^{[citation needed]} In particular, medianunbiased estimators exist in cases where meanunbiased and maximumlikelihood estimators do not exist. Besides being invariant under onetoone transformations, medianunbiased estimators have surprising robustness.^{[citation needed]}
Bias with respect to other loss functions[]
Any meanunbiased minimumvariance estimator minimizes the risk (expected loss) with respect to the squarederror loss function, as observed by Gauss.^{[citation needed]} A medianunbiased estimator minimizes the risk with respect to the absolute loss function, as observed by Laplace.^{[citation needed]} Other loss functions are used in statistical theory, particularly in robust statistics.^{[citation needed]}
Effect of transformations[]
Note that, when a transformation is applied to a meanunbiased estimator, the result need not be a meanunbiased estimator of its corresponding population statistic. That is, for a nonlinear function f and a meanunbiased estimator U of a parameter p, the composite estimator f(U) need not be a meanunbiased estimator of f(p). For example, the square root of the unbiased estimator of the population variance is not a meanunbiased estimator of the population standard deviation.
See also[]
 Omittedvariable bias
 Consistent estimator
 Estimation theory
 Expected loss
 Expected value
 Loss function
 Median
 Statistical decision theory
Notes[]
References[]
 Brown, George W. "On SmallSample Estimation." The Annals of Mathematical Statistics, Vol. 18, No. 4 (Dec., 1947), pp. 582–585. Template:JSTOR
 Lehmann, E.L. "A General Concept of Unbiasedness" The Annals of Mathematical Statistics, Vol. 22, No. 4 (Dec., 1951), pp. 587–592. Template:JSTOR
 Allan Birnbaum. 1961. "A Unified Theory of Estimation, I", The Annals of Mathematical Statistics, Vol. 32, No. 1 (Mar., 1961), pp. 112–135
 van der Vaart, H.R. 1961. "Some Extensions of the Idea of Bias" The Annals of Mathematical Statistics, Vol. 32, No. 2 (Jun., 1961), pp. 436–447.
 Pfanzagl, Johann. 1994. Parametric Statistical Theory. Walter de Gruyter.
 (1999) Classical Inference and the Linear Model, Sixth, xxii+885, London: Arnold.
 (1993) Unbiased estimators and their applications, Dordrect: Kluwer Academic Publishers.
 (1996) Unbiased estimators and their applications, Dordrect: Kluwer Academic Publishers.
Statistics  

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Biases  

Biases in judgment and decision making 

Statistical biases 

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