Bias (statistical)
The systematic error introduced by incorrect assumptions in a learning algorithm, causing the model to consistently miss the true underlying relationships.
Think of It Like This
Like a poorly calibrated scale that always reads exactly five pounds too heavy, no matter who stands on it.
In the context of the bias-variance tradeoff, high bias means the model is too simple to capture the complexities of the data, leading to severe underfitting. For example, trying to predict a highly curved quadratic trend using a completely straight linear regression line will invariably result in high statistical bias.