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.
About the Author
Dr. Vikas Shah MBE DL has significant experience in founding, leading and exiting businesses to trade, private-equity and listed groups. He is currently a Non-Executive Board Member of the UK Government's Department for Energy Security & Net Zero (DESNZ). He also serves as a Non-Executive Director for the Solicitors Regulation Authority, The Institute of Directors, and Enspec Power. He is Co-Founder of leading venture lab Endgame and sits as Entrepreneur in Residence at The University of Manchester's Innovation Factory. Vikas was awarded an MBE for Services to Business and the Economy in the Queen's 2018 New Year's Honours List. In 2021, he became a Deputy Lieutenant of the Greater Manchester Lieutenancy. He holds an Honorary Professorship of Business at The Alliance Business School, University of Manchester, an Honorary Professorial Fellowship at Lancaster University Management School (LUMS), and was awarded an Honorary Doctorate in Business Administration from the University of Salford in 2022.