Which statement regarding the bias-variance tradeoff is true?

The correct statement is that bias can arise due to model assumptions. In statistics and machine learning, bias refers to the error introduced by approximating a real-world problem, which may be complex, by a simplistic model. When a model makes strong assumptions about the data, such as linearity when the relationship is actually nonlinear, it can lead to systematic errors in predictions. This systematic error is what we refer to as bias. For example, using a linear regression model to fit data that actually follows a quadratic pattern can lead to significant bias.

Understanding this concept is crucial for practitioners, as one must balance the model's complexity (or flexibility) to avoid underfitting (high bias) while managing overfitting (high variance) to achieve an optimal model performance. This relationship is a core aspect of the bias-variance tradeoff in predictive modeling, where the goal is to achieve a model with low bias and low variance to improve overall predictive accuracy.