Comparing and contrasting the reality of subjectivity in the workof history's great scientists and the modern Bayesian approach tostatistical analysis Scientists and researchers are taught to analyze their data from anobjective point of view, allowing the data to speak for themselvesrather than assigning them meaning based on expectations oropinions. But scientists have never behaved fully objectively.Throughout history, some of our greatest scientific minds haverelied on intuition, hunches, and personal beliefs to make sense ofempirical data-and these subjective influences have often aided inhumanity's greatest scientific achievements. The authors argue thatsubjectivity has not only played a significant role in theadvancement of science, but that science will advance more rapidlyif the modern methods of Bayesian statistical analysis replace someof the classical twentieth-century methods that have traditionallybeen taught. To accomplish this goal, the authors examine the lives and work ofhistory's great scientists and show that even the most successfulhave sometimes misrepresented findings or been influenced by theirown preconceived notions of religion, metaphysics, and the occult,or the personal beliefs of their mentors. Contrary to popularbelief, our greatest scientific thinkers approached their data witha combination of subjectivity and empiricism, and thus informallyachieved what is more formally accomplished by the modern Bayesianapproach to data analysis. Yet we are still taught that science is purely objective. Thisinnovative book dispels that myth using historical accounts andbiographical sketches of more than a dozen great scientists,including Aristotle, Galileo Galilei, Johannes Kepler, WilliamHarvey, Sir Isaac Newton, Antoine Levoisier, Alexander vonHumboldt, Michael Faraday, Charles Darwin, Louis Pasteur, GregorMendel, Sigmund Freud, Marie Curie, Robert Millikan, AlbertEinstein, Sir Cyril Burt, and Margaret Mead. Also included is adetailed treatment of the modern Bayesian approach to dataanalysis. Up-to-date references to the Bayesian theoretical andapplied literature, as well as reference lists of the primarysources of the principal works of all the scientists discussed,round out this comprehensive treatment of the subject. Readers will benefit from this cogent and enlightening view of thehistory of subjectivity in science and the authors' alternativevision of how the Bayesian approach should be used to further thecause of science and learning well into the twenty-first century.