The mean and variance of the error for cross validation splits can tell us more about the sources of error in our model. In this video we discuss how the expected error of a model (the so-called bias) and the variance of a model interact with each other. Surprisingly, there is an inherent tradeoff between bias and variance. A more complex model will be able to approximate a function well, with low bias. And a simpler model will have low variance.