Bias in estimation refers to the systematic error that causes an estimator to consistently overestimate or underestimate the true value of a parameter. This concept is crucial as it affects the accuracy and reliability of statistical inferences made from data. When estimating parameters, such as coefficients in a regression model, bias can arise due to various factors including omitted variable bias, measurement error, or weak instruments, leading to incorrect conclusions about relationships within the data.
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