English: Inspired by Kahneman, D., Sibony, O. & Sunstein, C. (2021), the figure shows the components of noise in human judgment, as laid out in that book, and illustrates that such statistical noise (which is unwanted variability) together with statistical bias (which is a shift in the average away from the true value that exists for certain types of judgments) together make up the size of the total error in the judgments in question. The proportions between the different noise components and the proportions between the statistical noise and the statistical bias vary from judgment to judgment, but the figure show the proportions that are most common according to Kahneman, Sibony and Sunstein: Typically, there is more noise than bias and more pattern noise than level noise, and typically stable pattern noise is the largest noise component. Finally, note the difference between statistical bias and psychological bias: statistical bias is a purely mathematical result of the judgments being systematically wrong in one direction, whereas psychological bias can arise from a multitude of cognitive effects in thinking, as well as from prejudices, and can in turn give rise both to statistical bias and to noise.