Introduction by I. J. Good

Module by: Irving Good

Summary: I.J. Good's introduction to the collection of essays, The Good Book: Thirty Years of Comments, Conjectures and Conclusions by I.J. Good, edited by David Banks and Eric P. Smith. The book is available in print from Rice University Press (http://ricepress.rice.edu).

(This module helps introduce The Good Book: Thirty Years of Comments, Conjectures and Conclusions, by I.J. Good. The book is available for purchase from the Rice University Press Store. You can also visit the Rice University Press web site.)

I greatly welcomed David Banks's proposal for him to edit a book of my CCCs (Comments, Conjectures and Conclusions; a few written jointly with other authors), which appeared in my column in JSCS (Journal of Statistical Computation and Simulation). David Banks has invited me to write an introduction and to explain also how this column originated. I'll do the latter first.

Ever since my youth, I have believed that Brevity is the Soul of Wit,1 as exemplified in The Scientist Speculates, An Anthology of Partly Baked Ideas2 (the Preface was the brevity: “The intention of this anthology is to raise more questions than it answers”) and in the twenty-eight columns of pbis (partially baked ideas) in the Mensa Bulletin and Journal. These were assembled in a Technical Report.3 After discussion with Richard G. Krutchkoff, the editor of JSCS, he invited me to edit columns of CCCs. They began in 1977 and contained many items by other authors which are not represented in the present book.

I greatly liked also the three extremely well-written and kind appreciations of my work by Stephen Fienberg, Nozer Singpurwalla, and James R. Thompson & David W. Scott, which they had prepared largely for my ninetieth birthday party. Those appreciations and this introduction are not especially related to my brevities. The brevities in this book speak for themselves, and the brevity of this introduction is therefore appropriate.

Because I was once regarded as a statistical dissident, let me discuss establishment and dissident views in general.

Thomas Kuhn, who is well known by philosophers of science, defined a period of normal science as one in which a specific paradigm is usually adopted. Call it the Establishment view and assume there is one for each named science. But there are dissidents who are at first ignored by the Establishment, but who eventually form a new Establishment and another “normal” period. This process isn't restricted to scientific topics; compare the one-volume (abridged) edition (1932) of The Golden Bough by J.G. Frazer (originally published in twelve volumes in 1922). Frazer mentions various places, especially in parts of Africa, where priests and kings were killed after a fixed period or after a public calamity such as a drought. Or consider the sixteenth-century aphorism by John Harington, “Treason doth never prosper: what's the reason? For if it prosper, none dare call it treason.” Paradigm changes can be regarded as revolutions, but they are not necessarily sudden and there can be a period in which there are two or more paradigms. For example, there is a mixture of the caloric theory and its explanation, Statistical Mechanics; and a mixture between prerelativity and relativity. (It is said that von Newman told a policeman that a house had struck von Newman's car, thus wittily applying the principle of relativity to an inappropriate situation.) In Statistics we have a mixture of frequentism and Bayesianism. Frequentism is based on the use of P-values for the acceptance and refutation of hypotheses where refutation can be called inexactification as I expressed it in a lecture at Berkeley some years ago. Bayesianism in its turn can be objective (impersonal) or subjective (personal). These depend respectively on impersonal and personal probabilities. (One application of impersonal probabilities is to the metaphysical problem of why there should be something rather than nothing. Nothingness is simpler than somethingness.) Sometimes it is convenient to use one methodology and sometimes another. I experienced this phenomenon during World War II, when working as a cryptanalyst in Bletchley Park. (See, for example, my chapter “Enigma and Fish” in Codebreakers: The Inside Story of Bletchley Park, edited by F.H. Hinsley and Allan Stripp, Oxford University Press, 1994. Or see my work in the book edited by Copeland mentioned in Note 4.) To give an example: in one procedure of many (setting wheels 1 and 2 of a German enciphering machine SZ42, which we called Fish or Tunny), there were 31×41=127131×41=1271 hypotheses, each with prior probability 1/12711/1271. But if a score of a setting, after a Colossus4 run, was close to four standard deviations above the mean, then the posterior odds were not 30 (as we sometimes inferred incorrectly from a P-value of 1/30,0001/30,000) but only about 1 (evens) from a Bayesian point of view. But we would also refer to the sigma-age or sigmage of a bulge above the mean.

In a discussion at the Second Valencia meeting on Statistics in 1983, Lindley predicted that the 21st century would be Bayesian, and I predicted it would be a century of the Bayes/non-Bayes compromise.5Mixture might be a better term than compromise. There are children now who will judge, at the end of the century, who was more correct. This statement is conditional on there being anyone alive at that time!

This book is about statistical brevity and about the views of one of the early dissidents. Some readers might wish to expand some items into full-length dissertations.

For getting this book into suitable electronic form, I am most grateful to Eric P. Smith, the present head of the Statistics Department at Virginia Tech, Linda Breeding, and Byron Smith.

I. J. Good, January 12, 2008

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This work is licensed by Irving Good under a Creative Commons Attribution License (CC-BY 2.0).

Last edited by Frederick Moody on Sep 12, 2008 12:47 pm GMT-5.