Description: Estimation bias refers to the systematic difference between the expected value of an estimator and the true value of the parameter being estimated. In other words, an estimator is biased if, on average, it does not provide an accurate estimate of the true value. This concept is fundamental in applied statistics, as it affects the validity of results obtained from samples. Bias can arise from various sources, such as errors in data collection, inappropriate sample selection, or incorrect assumptions about the data distribution. Identifying and correcting bias is crucial to ensure that statistical inferences are reliable. An estimator is considered unbiased if its expected value matches the true value of the parameter. However, in practice, it is common for estimators to exhibit some degree of bias, which can influence decision-making based on statistical analyses. Therefore, understanding estimation bias is essential for researchers and analysts seeking to draw accurate and meaningful conclusions from empirical data.
