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Definition: In statistics and probability theory, a biased estimator is an estimator that has some bias. In other words, it can be expected to produce estimates with values closer or farther from the population parameters than if the estimator were unbiased. Bias refers to how well an estimate performs under different conditions of data. For example, in a situation where all observations are perfectly equal in size (i.e., no measurement error), the bias is zero. However, when there's some degree of variability among observations, the bias

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Definition: In statistics, an unbiased estimator is an estimator that does not give any bias when it is applied to a sample of data. This means that the estimate will be as close to the actual value in the population as possible. In other words, if the true value is known, then the unbiased estimator will have the same mean squared error (MSE) or MSE with respect to the true value, regardless of how many times the estimator is applied. For example, consider a sample of 10

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