Description: Bias correction is a statistical method used to adjust bias in an estimator to improve its precision and accuracy. Bias refers to the systematic difference between the expected value of an estimator and the actual value of the parameter being estimated. In other words, a biased estimator tends to consistently overestimate or underestimate the true value. Bias correction aims to mitigate this issue, allowing the results obtained to be more representative of reality. This process may involve applying mathematical and statistical techniques that adjust the data or the estimation methods used. Bias correction is fundamental in various areas of statistics and data science, as it ensures that inferences made from the data are valid and reliable. Without proper bias correction, decisions based on statistical analyses may be erroneous, which could have significant consequences in fields such as scientific research, economics, and public health.
