The easiest way to understand noise is by thinking about measurements. Suppose you are measuring a line with a very fine ruler. You will expect some variability such that you will not get the same number every time when you measure. That variability is noise. In the mathematics of accuracy, the expression for total error is very simple and quite compelling. It is bias-squared plus noise-squared. Bias and noise are both contributing to error and in that equation, they do so on the same basis.