English: The targets and shots in this figure illustrate the difference between statistical bias and statistical noise and how these concepts relate to statistical error. The shots on target b) are biased, because the shots are systematically off in one direction (down to the right from the bullseye). On average, there is an error. Target c) on the other hand does not have a mean error, because the imprecision in each shot in relation to the bullseye cancel each other out. So there is no bias – but there is more noise, because the shots there differ much from each other. The shots on target a) are precise; there is no bias and very little noise. Target d) has it worst, since it suffers from both bias and noise. A final observation here is that if there were no targets, it would be impossible to say that the a) or b) shots are biased, since there would be no true value (the bullseye) to compare them to, but it would still be obvious that the c) and d) shots are noisy. (Adapted from Kahneman, D., Sibony, O. & Sunstein, C. (2021). Noise: A flaw in human judgment. New York: Little, Brown Spark. ISBN 978-0-00-830899-5. OCLC 1242782025.)