The paper reviews and discusses the statistical aspects of the phenomenon called ‘noise’ which Daniel Kahneman, the Nobel Prize winning psychologist, and his colleagues present in their new book entitled ‘Noise: A Flaw in Human Judgment’. Noise is understood by the authors as an unexpected and undesirable variation present in people’s judgments. The variability of judgments influences decisions which are made on the basis of those judgments and, consequently, may have a negative impact on the operations of various institutions. This is the main concern presented and analyzed in this book.
The objective of this paper is to look at the relationship between bias and noise – the two major components of the mean squared error (MSE) – from a different perspective which is absent in the book. Although the author agrees that each of the two components contributes equally to MSE, he claims that in some circumstances a reduction of noise can make accurate inference not less, but more difficult. It is justified that the actual impact of noise cannot be accurately determined without considering both bias and noise simultaneously.
noise, bias, mean squared error, statistical inference
C12, C13, C18, D80
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