Bias and variability are fundamental concepts in statistics and machine learning that describe the accuracy and consistency of a model's predictions. A model with high bias tends to oversimplify and miss important patterns, while a model with high variability is overly sensitive to fluctuations in the training data, leading to poor generalization to new data.