Been reading some introductory text on statistical modelling and in particular the concepts of reducible and irreducible errors.
As I understand it, reducible error is the bit we can have any hope of minimising by fitting better models or making our models richer by capturing more of the underlying phenomenon.
The thing that confused me was that the author says that the irreducible error could be due to measurement errors or not being able to capture all the features of interest to model the underlying phenomenon in a better way and due to inherent variability in the data. This last phrase has me confused. I thought the underlying variability would arise due to the measurement error. I am not sure I quite understand what is meant by that phrase.
Also, the issue with not being able to capture all the relevant features. Should this not come under reducible errors as if we did measure these features we could produce a better model perhaps?