Why Use a Biased Estimator?

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  • Thread starter Thread starter thrillhouse86
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Discussion Overview

The discussion revolves around the use of biased versus unbiased estimators in statistical analysis. Participants explore the rationale behind using biased estimators, particularly in practical scenarios where unbiased options may not be available.

Discussion Character

  • Exploratory, Technical explanation, Conceptual clarification

Main Points Raised

  • One participant expresses confusion about the rationale for using biased estimators, questioning their utility since they differ from the population statistic.
  • Another participant suggests that biased estimators may be necessary when unbiased options are unavailable, citing that many sensors produce biased measurements.
  • A subsequent participant seeks clarification, asking if the use of biased estimators is primarily driven by practical constraints rather than preference.
  • A later reply confirms that practical issues often necessitate the use of biased estimators.

Areas of Agreement / Disagreement

Participants generally agree that while biased estimators are not preferred, practical considerations may require their use. The discussion reflects a shared understanding of the limitations of biased estimators in certain contexts.

Contextual Notes

Participants do not delve into specific definitions of bias or the mathematical implications of using biased estimators, leaving some assumptions and details unresolved.

thrillhouse86
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Hey All,

I am comfortable with the idea of biased and unbiased estimators, but what I don't understand is why you would ever want to use a biased estimator ? at the end of the day doesn't it mean that the sample statistic is different from the population statistic you are trying to estimate ?
 
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thrillhouse86 said:
but what I don't understand is why you would ever want to use a biased estimator ?
The simple answer is that the biased estimator might be all you have available. Lots of sensors yield biased measurements. The sensor model had better include an estimation of the bias to yield an answer closer to the truth than that produced directly by the sensor.
 
So does that mean you never really want a biased estimator, but sometimes practical issues force you to work with one ?
 
Exactly.
 
cool thanks D H
 

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