Description: A biased estimator is a concept in statistics that refers to an estimator whose expected value does not coincide with the true value of the parameter being estimated. This means that, on average, the estimator tends to overestimate or underestimate the actual parameter. The presence of bias can result from various reasons, such as selecting a non-representative sample, measurement errors, or applying an inappropriate model. In the context of statistical modeling and machine learning, bias can arise during the training process, where the model may learn patterns that do not adequately reflect reality due to limited or noisy training data. It is important to note that a biased estimator is not necessarily useless; in some cases, it may be preferable to an unbiased estimator if it has significantly lower variance. However, bias must be carefully considered, as it can affect the accuracy and validity of inferences made from the model’s results. In summary, the biased estimator is a key concept in statistics and machine learning, highlighting the importance of data quality and the suitability of models used to make accurate estimates.
