 We might restrict candidates for \(h(X)\) to linear (or affine) functions of X, for instance. Yet making predictions about the value of Y with a simplified linear model, or some other simplified model, can introduce a systematic prediction error called bias. Bias results from a difference between the central tendency of data generated by the true model, \(r(X)\) (for all \(x \in X\)), and the central tendency of our estimator, \(\mathbb{E}\left[h(X)\right]\), written 