Basics of Uncertainty

This chapter presents an introduction to the basic concept of uncertainty, which is that every result is an instance taken from a distribution of potential measurements. Describing this distribution is an important part of characterizing a result. This is a complex topic and the literature has not always been consistent in the use of specific terms, for example the term ‘error’ and ‘uncertainty’. One approach is the Guide to the Expression of Uncertainty in Measurement (GUM). Random variables and uncertainty models are introduced. The concepts of probabilities, means and standard deviations are described. The special case of uncertainties in Poisson distributions are presented. Example cases of ‘bottom-up’ uncertainty and error propagation are given. There is a discussion of the uncertainties involved with detector calibration. The important topic of total measurement uncertainty is presented. ‘Top-down’ and ‘bottom-up’ uncertainties are contrasted. There is a brief description of measurement control and the chapter concludes with a description of International Target Values.