👉 In statistics, an "overbias" is a phenomenon where a model's predictions are not as good as they would be if the data were independent and identically distributed. This can occur when the model's parameters are estimated using biased estimators, such as maximum likelihood or maximum likelihood with constraints. For example, consider a simple linear regression model with two variables: one is a constant (e.g., 0) and the other is dependent on the first variable. If the data points are independent
