👉 In statistics and machine learning, a bias is an inherent feature or characteristic of a statistical model that results in predictions that are not entirely accurate. Bias can be either positive (e.g., underestimating the variance) or negative (e.g., overestimating the error rate). In other words, it refers to how the model's parameters and assumptions affect its predictions. Examples of biases include: 1. Underestimation of variance: A model with a small bias towards predicting more values than observations