In the book An Introduction to Statistical Learning, chapter 2, it is mentioned that Expected MSE has 3 components:
$E(y_0-\widehat{f}(x_0))^2=Var(\widehat{f}(x_0))+[Bias(\widehat{f}(x_0))]^2+Var(\epsilon )$
My question in this case is about the Bias component, and the text tells that this Bias is due to trying to estimate a complex model using a simpler model $\widehat{f}$, for example trying to fit a linear model to a data with a non-linear underlying best estimate.
My question is what kind of Bias is it? Because Bias I have come across before in statistics context is about data collection/survey when sample is not representing the population because some items in the population have unequal or lower or 0 probability of being included in the sample. And in that context of sampling, bias is either Selection Bias and Response Bias.
Second, why this feature called Bias in the first place? Since Bias is related to sampling due to not representing the population properly.
I am looking for an intuition of this concept.