DOCSLIB.ORG

Mathematical Statistics and the ISI (1885-1939)

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Mathematical Statistics and the ISI (1885-1939) Mathematical Statistics and the ISI (1885-1939) Stephen M. Stigler University of Chicago, Department of Statistics 5734 University Avenue Chicago IL 60637, USA [email protected] 1. Introduction From its inception in 1885, the International Statistical Institute took all of statistics as its province. But the understood meaning of “statistics” has undergone significant change over the life of the Institute, and it is of interest to inquire how that change, whether evolutionary or revolutionary, has been reflected in the business of the Institute, in its meetings and its membership. I propose to consider this question in regard to one particular dimension, that of mathematical statistics, and one particular period, that from the rebirth of international statistics in 1885 until the eve of World War II. Mathematical statistics has been a recognized division of statistics at least since the 1860s, when Quetelet at the Congress in the Hague admonished the assembled statisticians not to divide the école mathématique from the école historique, but to take statistics whole, to open the doors to statistics in the broadest sense in all its flavors (Stigler, 2000). But even with this recognition, mathematical statistics only emerged as a discipline later. I have argued elsewhere that this emergence can be dated to the year 1933, and even if one thinks that date absurdly precise, there was a distinct and palpable change that was evident around that time (Stigler, 1999, Ch. 8). It was then that changes initiated a half-century earlier by Galton and Edgeworth and developed subsequently by Karl Pearson and Udny Yule, were seized upon and transformed by Ronald Fisher and Jerzy Neyman and Egon S. Pearson, and the intellectual practices of all of statistics were changed forever. How were these epochal changes reflected in the ISI? The impact of any such change upon a society would be expected to be most directly reflected in its publications and meetings. Early in the 1930s the Royal Statistical Society initiated a series of review papers intended to summarize the exciting new work in mathematical statistics, but it was not long before they recognized this work was developing so rapidly and extensively that they could not keep up with it in this way, and by the mid-1930s they opened the gates of their journal to the work itself. The American Statistical Association reacted by helping to found a new journal, the Annals of Mathematical Statistics, and while a budgetary crisis soon led them to abandon this offspring, with wise leadership it managed to create a new sponsoring society in 1935, the Institute of Mathematical Statistics, and the Annals flourished in this new home. But what of the ISI? 2. The ISI before 1939 Mathematical statistics was not entirely absent from the publications of the ISI before 1939, but it was nearly absent. In the entire period there was scarcely any evidence of the new and energetic development of mathematical statistics in the publications of the ISI. The one substantial article on mathematical statistics in the ISI Bulletin was “Contribution to the theory of certain test criteria,” published by Jerzy Neyman in 1929, nineteen years before Neyman was elected to membership. There were a very few other works of mathematical interest: In 1936 Georges Darmois published an expansion on an influential article he had published the year before in the Comptes Rendus de l’Académie des Sciences de Paris (Darmois, 1935, 1936), an article that developed the relationship of sufficient statistics to the exponential family and is today remembered in the phrase “Darmois-Koopman Theorem,” or sometimes the “Fisher-Darmois-Koopman-Pitman Theorem,” it being generally agreed that this is a classic case of simultaneous independent discovery, since the latter three authors were operating independently, all being aware of Fisher’s earlier work. Barankin and Maitra (1963) discuss the distinctions among the works. And Cornish and Fisher (1937) published the expansion that bears their name, notwithstanding its anticipation by Theile in 1899 (Lauritzen, 2002). Aside from these, there were a small handful of shorter or less- developed articles that reviewed other mathematical work, such as articles by J. O. Irwin on factor analysis (1934) and Octav Onicescu on Markov chains (1936). In 1934 Maurice Fréchet orchestrated a “report” on the use of the correlation coefficient, and he took the opportunity to publish several correspondents’ reactions to a deliberately provocative paragraph he circulated endorsing the coefficient and suggesting that the values .30 and .50 for r could be taken as limits separating non-existent, weak, and strong relationships between two variables (Frechét (1934), Armatte, 2001). Nearly all of the correspondents (including Fisher, Hotelling, Gini, Rietz, von Mises, Frisch, Gumbel) found the statement wanting in some respect. Fisher’s contribution summarized the history of what we now tend to call Pearson’s r, as “the product moment coefficient used by Bravais, and later introduced by Pearson in connection with the researches of Francis Galton.” Fréchet took the hint and subsequently referred to r as “the Bravais-Galton coefficient.” All of this is interesting reading, and the report may have been quite useful for the ISI members, but it was neither novel mathematics nor statistics, even in 1934. 3. The reason mathematical statistics was largely absent The 1920s and 1930s were a period of exciting development in mathematical statistics. Why was there so little evidence of this in the ISI? The ISI did count among its members several who contributed to this development. When the ISI was created in 1885, some members of the older schools were among the first members: Wilhelm Lexis and J. B. J. Liagre. Between 1886 and 1905, Francis Galton, Francis Edgeworth, Arthur Bowley, Ladislaus von Bortkiewicz, and G. Udny Yule were elected members. By 1927 they were joined by A. A. Tchouprov [Chuprov], George Knibbs, Emile Borel, Irving Fisher, and David Heron, all men who could be expected to appreciate mathematical work and at least occasionally contribute to it. They were followed by R. A. Fisher and Maurice Fréchet in 1931, Georges Darmois in 1935, and E. S. Pearson and P. C. Mahalonobis in 1937. These people were all quite formidable mathematical talents, but the most revealing aspect of this list is those whose names are not on it: Karl Pearson, Harold Jeffreys, A. A. Kolmogorov, Joseph Doob, Abraham Wald, Harald Cramér, Daniel Dugué, Maurice Bartlett, Harold Hotelling, William Cochran, Samuel S. Wilks, Walter Shewhart, and even Jerzy Neyman. All of these were writing important articles in mathematical statistics before 1937. Most of them were elected members in the decade 1947-1957, after the ISI resumed meetings following World War II, and Wald would surely have been elected had he not died in December 1950 (Neyman had nominated him and was actively campaigning for his admission). Only Karl Pearson and Harold Jeffreys among the names on this list were never members of the ISI. This list of names suggests a principal reason that the ISI was so slow in noticing, much less adopting and extending the new mathematical approaches. The ISI was conceived of in 1885 as an elite organization, and election was taken quite seriously. This had the advantage of guaranteeing the intellectual strength and stability of the organization. But it had one unfortunate consequence: If only well-established scholars were elected, then there would be a consequent delay in inducting young workers in new areas. Even those few among the new generation of mathematical statisticians who were members, like Fisher after 1931, would not have found the broad senior membership of the ISI, with a strong emphasis on national and economic statistics, an ideal audience for their more mathematical work, and the publishing delays (two to four years for the Bulletin in the mid-1930s) would not have been an attraction. The experience of the International Congresses of Mathematicians, held every four years but without elected membership, was to a degree different from that of the ISI. There, over the same period there was somewhat more evidence of mathematical statistics than in the ISI (Krengel, 1997). Most of this was on probability, but these Congresses also witnessed some important works on statistics, including by Fisher and by Neyman. While the traces of mathematical statistics in the publications and proceedings of the ISI before World War II were barely detectable, there was nonetheless great vitality in the meetings and much else of methodological interest, particularly as regards sampling. One particularly important line of work was initiated by A. Kiaer in 1895 under the title Representative Sampling and developed mostly within the ISI. The story of this development and the surrounding debates is told in great and interesting scholarly detail by Kruskal and Mosteller (1980). 4. After the war The ISI was essentially inactive between 1938 and 1947. The Prague session closed prematurely on its second day, September 13, 1938; Hitler’s speech of September 12 on German claims to portions of Czechoslovakia had precipitated the crisis that would lead to Chamberlain’s ill-fated trip to Munich September 15. In 1947 the meetings resumed in New York (Nixon, 1960). With the renewal of meetings came a renewal of spirit, and an influx of new members. Jerzy Neyman was elected in 1948 and was to play a vital role in finally bringing mathematical statistics to play an active role in the ISI. Neyman worked to nominate and elect new members, including some of the most active younger mathematical statisticians, and he was instrumental in starting the movement in 1958 that was to result eventually in the founding of the Bernoulli Society in 1975 (Teugels, 2000, 2001). The number of ISI members more than doubled in the decade following the 1947 session, and this new blood would help produce a period of great excitement and growth in international statistics.
Load more

Recommended publications
  • National Academy Elects IMS Fellows Have You Voted Yet?
    Volume 38 • Issue 5 IMS Bulletin June 2009 National Academy elects IMS Fellows CONTENTS The United States National Academy of Sciences has elected 72 new members and 1 National Academy elects 18 foreign associates from 15 countries in recognition of their distinguished and Raftery, Wong continuing achievements in original research. Among those elected are two IMS Adrian Raftery 2 Members’ News: Jianqing Fellows: , Blumstein-Jordan Professor of Statistics and Sociology, Center Fan; SIAM Fellows for Statistics and the Social Sciences, University of Washington, Seattle, and Wing Hung Wong, Professor 3 Laha Award recipients of Statistics and Professor of Health Research and Policy, 4 COPSS Fisher Lecturer: Department of Statistics, Stanford University, California. Noel Cressie The election was held April 28, during the business 5 Members’ Discoveries: session of the 146th annual meeting of the Academy. Nicolai Meinshausen Those elected bring the total number of active members 6 Medallion Lecture: Tony Cai to 2,150. Foreign associates are non-voting members of the Academy, with citizenship outside the United States. Meeting report: SSP Above: Adrian Raftery 7 This year’s election brings the total number of foreign 8 New IMS Fellows Below: Wing H. Wong associates to 404. The National Academy of Sciences is a private 10 Obituaries: Keith Worsley; I.J. Good organization of scientists and engineers dedicated to the furtherance of science and its use for general welfare. 12-3 JSM program highlights; It was established in 1863 by a congressional act of IMS sessions at JSM incorporation signed by Abraham Lincoln that calls on 14-5 JSM tours; More things to the Academy to act as an official adviser to the federal do in DC government, upon request, in any matter of science or 16 Accepting rejections technology.
    [Show full text]
  • Memorial to Sir Harold Jeffreys 1891-1989 JOHN A
    Memorial to Sir Harold Jeffreys 1891-1989 JOHN A. HUDSON and ALAN G. SMITH University of Cambridge, Cambridge, England Harold Jeffreys was one of this century’s greatest applied mathematicians, using mathematics as a means of under­ standing the physical world. Principally he was a geo­ physicist, although statisticians may feel that his greatest contribution was to the theory of probability. However, his interest in the latter subject stemmed from his realization of the need for a clear statistical method of analysis of data— at that time, travel-time readings from seismological stations across the world. He also made contributions to astronomy, fluid dynamics, meteorology, botany, psychol­ ogy, and photography. Perhaps one can identify Jeffreys’s principal interests from three major books that he wrote. His mathematical skills are displayed in Methods of Mathematical Physics, which he wrote with his wife Bertha Swirles Jeffreys and which was first published in 1946 and went through three editions. His Theory o f Probability, published in 1939 and also running to three editions, espoused Bayesian statistics, which were very unfashionable at the time but which have been taken up since by others and shown to be extremely powerful for the analysis of data and, in particular, image enhancement. However, the book for which he is probably best known is The Earth, Its Origin, History and Physical Consti­ tution, a broad-ranging account based on observations analyzed with care, using mathematics as a tool. Jeffreys’s scientific method (now known as Inverse Theory) was a logical process, clearly stated in another of his books, Scientific Inference.
    [Show full text]
  • A Parsimonious Tour of Bayesian Model Uncertainty
    A Parsimonious Tour of Bayesian Model Uncertainty Pierre-Alexandre Mattei Université Côte d’Azur Inria, Maasai project-team Laboratoire J.A. Dieudonné, UMR CNRS 7351 e-mail: [email protected] Abstract: Modern statistical software and machine learning libraries are enabling semi-automated statistical inference. Within this context, it appears eas- ier and easier to try and fit many models to the data at hand, thereby reversing the Fisherian way of conducting science by collecting data after the scientific hypothesis (and hence the model) has been determined. The renewed goal of the statistician becomes to help the practitioner choose within such large and heterogeneous families of models, a task known as model selection. The Bayesian paradigm offers a systematized way of as- sessing this problem. This approach, launched by Harold Jeffreys in his 1935 book Theory of Probability, has witnessed a remarkable evolution in the last decades, that has brought about several new theoretical and methodological advances. Some of these recent developments are the focus of this survey, which tries to present a unifying perspective on work carried out by different communities. In particular, we focus on non-asymptotic out-of-sample performance of Bayesian model selection and averaging tech- niques, and draw connections with penalized maximum likelihood. We also describe recent extensions to wider classes of probabilistic frameworks in- cluding high-dimensional, unidentifiable, or likelihood-free models. Contents 1 Introduction: collecting data, fitting many models . .2 2 A brief history of Bayesian model uncertainty . .2 3 The foundations of Bayesian model uncertainty . .3 3.1 Handling model uncertainty with Bayes’s theorem .
    [Show full text]
  • School of Social Sciences Economics Division University of Southampton Southampton SO17 1BJ, UK
    School of Social Sciences Economics Division University of Southampton Southampton SO17 1BJ, UK Discussion Papers in Economics and Econometrics Professor A L Bowley’s Theory of the Representative Method John Aldrich No. 0801 This paper is available on our website http://www.socsci.soton.ac.uk/economics/Research/Discussion_Papers ISSN 0966-4246 Key names: Arthur L. Bowley, F. Y. Edgeworth, , R. A. Fisher, Adolph Jensen, J. M. Keynes, Jerzy Neyman, Karl Pearson, G. U. Yule. Keywords: History of Statistics, Sampling theory, Bayesian inference. Professor A. L. Bowley’s Theory of the Representative Method * John Aldrich Economics Division School of Social Sciences University of Southampton Southampton SO17 1BJ UK e-mail: [email protected] Abstract Arthur. L. Bowley (1869-1957) first advocated the use of surveys–the “representative method”–in 1906 and started to conduct surveys of economic and social conditions in 1912. Bowley’s 1926 memorandum for the International Statistical Institute on the “Measurement of the precision attained in sampling” was the first large-scale theoretical treatment of sample surveys as he conducted them. This paper examines Bowley’s arguments in the context of the statistical inference theory of the time. The great influence on Bowley’s conception of statistical inference was F. Y. Edgeworth but by 1926 R. A. Fisher was on the scene and was attacking Bayesian methods and promoting a replacement of his own. Bowley defended his Bayesian method against Fisher and against Jerzy Neyman when the latter put forward his concept of a confidence interval and applied it to the representative method. * Based on a talk given at the Sample Surveys and Bayesian Statistics Conference, Southampton, August 2008.
    [Show full text]
  • The Enigma of Karl Pearson and Bayesian Inference
    The Enigma of Karl Pearson and Bayesian Inference John Aldrich 1 Introduction “An enigmatic position in the history of the theory of probability is occupied by Karl Pearson” wrote Harold Jeffreys (1939, p. 313). The enigma was that Pearson based his philosophy of science on Bayesian principles but violated them when he used the method of moments and probability-values in statistics. It is not uncommon to see a divorce of practice from principles but Pearson also used Bayesian methods in some of his statistical work. The more one looks at his writings the more puzzling they seem. In 1939 Jeffreys was probably alone in regretting that Pearson had not been a bottom to top Bayesian. Bayesian methods had been in retreat in English statistics since Fisher began attacking them in the 1920s and only Jeffreys ever looked for a synthesis of logic, statistical methods and everything in between. In the 1890s, when Pearson started, everything was looser: statisticians used inverse arguments based on Bayesian principles and direct arguments based on sampling theory and a philosopher-physicist-statistician did not have to tie everything together. Pearson did less than his early guide Edgeworth or his students Yule and Gosset (Student) to tie up inverse and direct arguments and I will be look- ing to their work for clues to his position and for points of comparison. Of Pearson’s first course on the theory of statistics Yule (1938, p. 199) wrote, “A straightforward, organized, logically developed course could hardly then [in 1894] exist when the very elements of the subject were being developed.” But Pearson never produced such a course or published an exposition as integrated as Yule’s Introduction, not to mention the nonpareil Probability of Jeffreys.
    [Show full text]
  • Women Mathematicians in France in the Mid-Twentieth Century Introduction
    * Women mathematicians in France in the mid-twentieth century Yvette Kosmann-Schwarzbach * To appear in the BSHM Bulletin: Journal of the British Society for the History of Mathematics (2015) DOI 10.1080/17498430.2014.976804 A short outline of the French system of “Écoles normales supérieures” and “agrégations” serves as the introduction to our account of the careers of the five women who completed a doctorate in mathematics in France before 1960 and became internationally known scientists: Marie-Louise Dubreil-Jacotin (1905-1972), Marie-Hélène Schwartz (1913-2013), Jacqueline Ferrand (1918-2014), Paulette Libermann (1919-2007) and Yvonne Choquet-Bruhat (b. 1923). This is followed by a more general description of the place of women on the mathematical scene in France between 1930 and 1960, together with a brief sketch of the accomplishments of some other women and the identification of all those who were active in research before 1960 and became professors in the French university system. Introduction The intersection of the fields of history of mathematics and history of women is notoriously small. From Hypatia of Alexandria to Emmy Noether, very few women are known to have contributed to the development of mathematics, and the number of those born in France is even smaller. Were there any before Gabrielle-Émilie Le Tonnelier de Breteuil, marquise Du Châtelet-Lomond (1706-1749), the little-known Nicole-Reine Étable de Labrière Lepaute (1723-1788) and the even lesser known Marie Anne Victoire Pigeon d’Osangis (1724-1767)?Were there any between Sophie Germain (1776-1831) and those whose names and accomplishments will appear in this article? There were two French lady scientists working with the astronomer Jérôme de Lalande, Louise du Pierry (1746-1806) and Jeanne Lefrançais de Lalande (1769-1832).
    [Show full text]
  • Bayesian Data-Analysis Toolbox User Manual
    Bayesian Data-Analysis Toolbox Release 4.23, Manual Version 3 G. Larry Bretthorst Biomedical MR Laboratory Washington University School Of Medicine, Campus Box 8227 Room 2313, East Bldg., 4525 Scott Ave. St. Louis MO 63110 http://bayes.wustl.edu Email: [email protected] September 18, 2018 Appendix C Thermodynamic Integration Thermodynamic Integration is a technique used in Bayesian probability theory to compute the posterior probability for a model. As a reminder, if a set of m models is designated as M 2 f1; 2; : : : ; mg, then one can compute the posterior probability for the models by an application of Bayes' Theorem [1] P (MjDI) / P (MjI)P (DjMI) (C.1) where we have dropped a normalization constant, M = 1 means we are computing the posterior probability for model 1, M = 2 the probability for model 2, etc. The three terms in this equation, going from left to right, are the posterior probability for the model indicator given the data and the prior information, P (MjDI), the prior probability for the model given only the prior information, P (MjI), and the marginal direct probability for the data given the model and the prior information, P (DjMI). The marginal direct probability for the data given a model can be computed from the joint posterior probability for the data and the model parameters, which we will call Ω, given the Model and the prior information Z P (DjMI) = dΩP (ΩjMI)P (DjΩMI): (C.2) Unfortunately, the integrals on the right-hand side of this equation can be very high dimensional. Consequently, although we know exactly what calculation must be done to compute the marginal direct probability, in most applications the integrals are not tractable.
    [Show full text]
  • The Emergence of French Statistics. How Mathematics Entered the World of Statistics in France During the 1920S
    The emergence of French statistics. How mathematics entered the world of statistics in France during the 1920s. Rémi Catellier, Laurent Mazliak To cite this version: Rémi Catellier, Laurent Mazliak. The emergence of French statistics. How mathematics entered the world of statistics in France during the 1920s.. 2009. hal-00397750v1 HAL Id: hal-00397750 https://hal.archives-ouvertes.fr/hal-00397750v1 Preprint submitted on 23 Jun 2009 (v1), last revised 21 Feb 2011 (v2) HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. The emergence of French statistics How mathematics entered the world of statistics in France during the 1920s Remi´ CATELLIER1 and Laurent MAZLIAK2 Abstract This paper concerns the emergence of modern mathematical statistics in France after First World War. Emile Borel’s achievements are presented, and especially his creation of two institutions where mathematical statistics were developed, the Statistical Institute of Paris University, (ISUP) in 1922 and above all the Henri Poincar´eInstitute (IHP) in 1928. At the IHP, a new journal Annales de l’Institut Henri Poincar was created in 1931. We present the first papers dealing with mathematical statistics. INTRODUCTION The important transformations in the field of the mathematics of randomness between around 1910 and 1930 are now rather well listed.
    [Show full text]
  • “I Didn't Want to Be a Statistician”
    “I didn’t want to be a statistician” Making mathematical statisticians in the Second World War John Aldrich University of Southampton Seminar Durham January 2018 1 The individual before the event “I was interested in mathematics. I wanted to be either an analyst or possibly a mathematical physicist—I didn't want to be a statistician.” David Cox Interview 1994 A generation after the event “There was a large increase in the number of people who knew that statistics was an interesting subject. They had been given an excellent training free of charge.” George Barnard & Robin Plackett (1985) Statistics in the United Kingdom,1939-45 Cox, Barnard and Plackett were among the people who became mathematical statisticians 2 The people, born around 1920 and with a ‘name’ by the 60s : the 20/60s Robin Plackett was typical Born in 1920 Cambridge mathematics undergraduate 1940 Off the conveyor belt from Cambridge mathematics to statistics war-work at SR17 1942 Lecturer in Statistics at Liverpool in 1946 Professor of Statistics King’s College, Durham 1962 3 Some 20/60s (in 1968) 4 “It is interesting to note that a number of these men now hold statistical chairs in this country”* Egon Pearson on SR17 in 1973 In 1939 he was the UK’s only professor of statistics * Including Dennis Lindley Aberystwyth 1960 Peter Armitage School of Hygiene 1961 Robin Plackett Durham/Newcastle 1962 H. J. Godwin Royal Holloway 1968 Maurice Walker Sheffield 1972 5 SR 17 women in statistical chairs? None Few women in SR17: small skills pool—in 30s Cambridge graduated 5 times more men than women Post-war careers—not in statistics or universities Christine Stockman (1923-2015) Maths at Cambridge.
    [Show full text]
  • Signifying Nothing: Reply to Hoover and Siegler
    forthcoming, Journal of Economic Methodology, March 2008 Signifying Nothing: Reply to Hoover and Siegler by Deirdre N. McCloskey and Stephen T. Ziliak University of Illinois at Chicago and Roosevelt University, April 2007 [email protected], [email protected] We invite distribution of the paper in this form, and especially any correspondence, and most especially any quarrels with it. Abstract After William Gosset (1876-1937), the “Student” of Student’s t, the best statisticians have distinguished economic (or agronomic or psychological or medical) significance from merely statistical “significance” at conventional levels. A singular exception among the best was Ronald A. Fisher, who argued in the 1920s that statistical significance at the .05 level is a necessary and sufficient condition for establishing a scientific result. After Fisher many economists and some others—but rarely physicists, chemists, and geologists, who seldom use Fisher-significance— have mixed up the two kinds of significance. We have been writing on the matter for some decades, with other critics in medicine, sociology, psychology, and the like. Hoover and Siegler, despite a disdainful rhetoric, agree with the logic of our case. Fisherian “significance,” they agree, is neither necessary nor sufficient for scientific significance. But they claim that economists already know this and that Fisherian tests can still be used for specification searches. Neither claim seems to be true. Our massive evidence that economists get it wrong appears to hold up. And if rhetorical standards are needed to decide the importance of a coefficient in the scientific conversation, so are they needed when searching for an equation to fit.
    [Show full text]
  • In Pursuit of a Science of Agriculture: the Role of Statistics in Field Experiments
    Giuditta Parolini In pursuit of a science of agriculture: the role of statistics in field experiments Article, Postprint version This version is available at http://dx.doi.org/10.14279/depositonce-6754. Suggested Citation Parolini, Giuditta: In pursuit of a science of agriculture: the role of statistics in field experiments. - In: History and philosophy of the life sciences. - ISSN: 1742-6316 (online), 0391-9714 (print). - 37 (2015), 3. - pp. 261–281. - DOI: 10.1007/s40656-015-0075-9. (Postprint is cited, page numbers differ.) This is a post-peer-review, pre-copyedit version of an article published in History and philosophy of the life sciences. The final authenticated version is available online at: http://dx.doi.org/10.1007/s40656-015-0075-9. Terms of Use Copyright applies. A non-exclusive, non-transferable and limited right to use is granted. This document is intended solely for personal, non-commercial use. Powered by TCPDF (www.tcpdf.org) In Pursuit of a Science of Agriculture: The Role of Statistics in Field Experiments In Pursuit of a Science of Agriculture: The Role of Statistics in Field Experiments Giuditta Parolini Postdoctoral fellow, Technische Universität Berlin and Berliner Zentrum für Wissensgeschichte Introduction “I do not always agree with Sir Ronald Fisher, but it is due to him that the standard of presentation of results in agriculture is better than in any of the so-called exact sciences; and this is a state of affairs that physicists should cease to tolerate” (Jeffreys 1953: 119). In this way, Harold Jeffreys, geophysicist and statistician, concluded his address to Section A (Physics and Mathematics) of the British Association for the Advancement of Science.
    [Show full text]
  • Interpreting Probability Controversies and Developments in the Early Twentieth Century
    Interpreting Probability Controversies and Developments in the Early Twentieth Century DAVID HOWIE PUBLISHED BY THE PRESS SYNDICATE OF THE UNIVERSITY OF CAMBRIDGE The Pitt Building, Trumpington Street, Cambridge, United Kingdom CAMBRIDGE UNIVERSITY PRESS The Edinburgh Building, Cambridge CB2 2RU, UK 40 West 20th Street, New York, NY 10011-4211, USA 477 Williamstown Road, Port Melbourne, VIC 3207, Australia Ruiz de Alarc´on 13, 28014 Madrid, Spain Dock House, The Waterfront, Cape Town 8001, South Africa http://www.cambridge.org C David Howie 2002 This book is in copyright.Subject to statutory exception and to the provisions of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University Press. First published 2002 Printed in the United Kingdom at the University Press, Cambridge Typeface Times Roman 10.25/13 pt. System LATEX2ε [TB] A catalog record for this book is available from the British Library. Library of Congress Cataloging in Publication Data Howie, David, 1970– Interpreting probability : controversies and developments in the early twentieth century / David Howie. p.cm.–(Cambridge studies in probability, induction, and decision theory) Includes bibliographical references and index. ISBN 0-521-81251-8 1.Probabilities. 2.Bayesian statistical decision theory. 3.Jeffreys, Harold, Sir, 1891–1989 4.Fisher, Ronald Aylmer, Sir, 1890–1962. I.Title. II.Series. QA273.A4 H69 2002 2001052430 ISBN 0 521 81251 8 hardback Contents Acknowledgments page xi 1 Introduction
    [Show full text]