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|Born|| June 16, 1915|
New Bedford, Massachusetts, USA
<tr><th>Died</th><td>July 26, 2000
Tukey obtained a A.B. in 1936 and Sc.M. in 1937, both in Chemistry, from Brown University, before moving to Princeton University where he received his Ph.D. in mathematics. During World War II, Tukey worked at the Fire Control Research Office and collaborated with Samuel Wilks and William Cochran. After the war, he returned to Princeton, dividing his time between the university and AT&T Bell Laboratories.
His statistical interests were many and varied. He is particularly remembered for his development with James Cooley of the Cooley-Tukey Fast Fourier transform algorithm. In 1970, he contributed significantly to what is today known as the jackknife estimation - also termed Quenouille-Tukey jackknife. He introduced the box plot in his 1977 book, Exploratory Data Analysis.
He also contributed to statistical practice and articulated the important distinction between exploratory data analysis and confirmatory data analysis, believing that much statistical methodology placed too great an emphasis on the latter. Though he believed in the utility of separating the two types of analysis, he pointed out that sometimes, especially in natural science, this was problematic and termed such situations uncomfortable science.
He wrote four papers with his fifth cousin Paul Tukey, who was an undergraduate at Princeton when they met.
Among many contributions to civil society, Tukey served on a committee of the American Statistical Association that produced a report challenging the conclusions of the Kinsey Report, Statistical Problems of the Kinsey Report on Sexual Behavior in the Human Male.
Tukey coined many statistical terms that have become part of common usage, but the two most famous coinages attributed to him were related to computer science. While working with John von Neumann on early computer designs, Tukey introduced the word "bit" as a contraction of binary digit. Tukey used the term "software" in a computing context in a 1958 article and this may have been the first published use.
A D Gordon offered the following summary of Tukey's principles for statistical practice:
... the usefulness and limitation of mathematical statistics; the importance of having methods of statistical analysis that are robust to violations of the assumptions underlying their use; the need to amass experience of the behaviour of specific methods of analysis in order to provide guidance on their use; the importance of allowing the possibility of data's influencing the choice of method by which they are analysed; the need for statisticians to reject the role of 'guardian of proven truth', and to resist attempts to provide once-for-all solutions and tidy over-unifications of the subject; the iterative nature of data analysis; implications of the increasing power, availability and cheapness of computing facilities; the training of statisticians.
- "bit stands for binary-unit"[How to reference and link to summary or text]
- "Far better an approximate answer to the right question, which is often vague, than the exact answer to the wrong question, which can always be made precise." J. W. Tukey (1967), "The future of data analysis". Annals of Mathematical Statistics 33, 1-67.
- "The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data." J. W. Tukey (1986), "Sunset salvo". The American Statistician 40(1). Online at http://www.jstor.org/view/00031305/di020589/02p0102y/0
- Hoaglin, D C; Mosteller, F & Tukey, John Wilder (Eds) (1985). Exploring Data Tables, Trends and Shapes. ISBN 0-471-09776-4.
- Hoaglin, D C; Mosteller, F & Tukey, John Wilder (Eds) (1983). Understanding Robust and Exploratory Data Analysis. ISBN 0-471-09777-2.
- Tukey, John Wilder (1977). Exploratory Data Analysis. ISBN 0-201-07616-0.
- (published in the Annals of Statistics) John W. Tukey: His Life and Professional Contributions
- Memories of John Tukey
- Short biography by Mary Bittrich
- "Remembering John W. Tukey", special issue of Statistical Science
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