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Title:
Errors, blunders, and lies : how to tell the difference / David S. Salsburg.
Author:
Salsburg, David, 1931-
Publication Information:
Boca Raton : CRC Press, Taylor & Francis Group, [2017]
Call Number:
Q172.5.E77 S38 2017
Abstract:
We live in a world that is not quite 'right'. The central tenet of statistical inquiry is that Observation = Truth + Error because even the most careful of scientific investigations have always been bedevilled by uncertainty. Our attempts to measure things are plagued with small errors. Our attempts to understand our world are blocked by blunders. And, unfortunately, in some cases, people have been known to lie. In this follow-up to his acclaimed best-seller, The Lady Tasting Tea, David Salsburg opens a door to the widespread use of statistical methods by looking at historical examples of errors, blunders and lies from areas as diverse as archaeology, law, economics, medicine, psychology, sociology, Biblical studies, history, and war-time espionage. In doing so, he shows how, upon closer statistical investigation, errors and blunders often lead to useful information. And how statistical methods have been used to uncover falsified data. Beginning with Edmund Halley's examination of the Transit of Venus and ending with discussions of how many tanks Rommel had during the Second World War and whether modern African censuses contain falsified data, the author invites the reader to come along on this easily-accessible and fascinating journey of how to identify the nature of errors, minimize the effects of blunders, and figure out who the liars are.
ISBN:
9781498795784

9781138726987
Series:
ASA-CRC series on statistical reasoning in science and society

ASA-CRC Series on Statistical Reasoning in Science and Society.
Physical Description:
xiii, 154 pages : illustrations ; 22 cm.
Contents:
The transit of Venus -- Probability versus likelihood -- The central limit conjecture -- Measuring disease -- Other uses of multilinear models -- When multilinear regression is not adequate -- Correlation versus causation -- Regression and big data -- Contaminated distributions -- The Princeton robustness study -- When the blunder is what you want -- Parsing "blunders" -- The reigns of kings -- Searching for the "real" Davy Crockett -- Detecting falsified counts -- Uncovering secrets -- Errors, blunders, or curbstoning?
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