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A statistic is called an unbiased estimator of a populace parameter if the suppose of the sampling distribution of the statistic is equal to the value of the parameter. For example, the sample suppose,

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, is an unbiased estimator of the populace mean,
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. In icons,
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. On the other hand also, since , the sample traditional deviation, , offers a biased estimate of .


For a little population of positive integers, this Demonstration illustprices unbiased versus biased estimators by displaying all feasible samples of a given size, the equivalent sample statistics, the expect of the sampling circulation, and the value of the parameter. Note: for the sample proportion, it is the proportion of the populace that is even that is taken into consideration.

See more: Which Event Always Involves A Chemical Change? ? Examples Of Physical Changes And Chemical Changes


Contributed by: Marc Brodie (Wheeling Jesuit University) (March 2011)Open content licensed under CC BY-NC-SA


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Snapshots 4 and 5 show the fact that also if a statistic (in this case the median) is not an unbiased estimator of the parameter, it is feasible for the suppose of the sampling circulation to equal the value of the parameter for a particular populace.


Permanent Citation

Marc Brodie (Wheeling Jesuit University) "Unbiased and Biased Estimators" http://slrfc.org/UnbiasedAndBiasedEstimators/ slrfc.org slrfc.org Project Published: March 7 2011