'Student': a Statistical Biography of William Sealy Gosset
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School of Social Sciences Economics Division University of Southampton Southampton SO17 1BJ, UK Discussion Papers in Economics and Econometrics Mathematics in the Statistical Society 1883-1933 John Aldrich No. 0919 This paper is available on our website http://www.southampton.ac.uk/socsci/economics/research/papers ISSN 0966-4246 Mathematics in the Statistical Society 1883-1933* John Aldrich Economics Division School of Social Sciences University of Southampton Southampton SO17 1BJ UK e-mail: [email protected] Abstract This paper considers the place of mathematical methods based on probability in the work of the London (later Royal) Statistical Society in the half-century 1883-1933. The end-points are chosen because mathematical work started to appear regularly in 1883 and 1933 saw the formation of the Industrial and Agricultural Research Section– to promote these particular applications was to encourage mathematical methods. In the period three movements are distinguished, associated with major figures in the history of mathematical statistics–F. Y. Edgeworth, Karl Pearson and R. A. Fisher. The first two movements were based on the conviction that the use of mathematical methods could transform the way the Society did its traditional work in economic/social statistics while the third movement was associated with an enlargement in the scope of statistics. The study tries to synthesise research based on the Society’s archives with research on the wider history of statistics. Key names : Arthur Bowley, F. Y. Edgeworth, R. A. Fisher, Egon Pearson, Karl Pearson, Ernest Snow, John Wishart, G. Udny Yule. Keywords : History of Statistics, Royal Statistical Society, mathematical methods. -
Two Principles of Evidence and Their Implications for the Philosophy of Scientific Method
TWO PRINCIPLES OF EVIDENCE AND THEIR IMPLICATIONS FOR THE PHILOSOPHY OF SCIENTIFIC METHOD by Gregory Stephen Gandenberger BA, Philosophy, Washington University in St. Louis, 2009 MA, Statistics, University of Pittsburgh, 2014 Submitted to the Graduate Faculty of the Kenneth P. Dietrich School of Arts and Sciences in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Pittsburgh 2015 UNIVERSITY OF PITTSBURGH KENNETH P. DIETRICH SCHOOL OF ARTS AND SCIENCES This dissertation was presented by Gregory Stephen Gandenberger It was defended on April 14, 2015 and approved by Edouard Machery, Pittsburgh, Dietrich School of Arts and Sciences Satish Iyengar, Pittsburgh, Dietrich School of Arts and Sciences John Norton, Pittsburgh, Dietrich School of Arts and Sciences Teddy Seidenfeld, Carnegie Mellon University, Dietrich College of Humanities & Social Sciences James Woodward, Pittsburgh, Dietrich School of Arts and Sciences Dissertation Director: Edouard Machery, Pittsburgh, Dietrich School of Arts and Sciences ii Copyright © by Gregory Stephen Gandenberger 2015 iii TWO PRINCIPLES OF EVIDENCE AND THEIR IMPLICATIONS FOR THE PHILOSOPHY OF SCIENTIFIC METHOD Gregory Stephen Gandenberger, PhD University of Pittsburgh, 2015 The notion of evidence is of great importance, but there are substantial disagreements about how it should be understood. One major locus of disagreement is the Likelihood Principle, which says roughly that an observation supports a hypothesis to the extent that the hy- pothesis predicts it. The Likelihood Principle is supported by axiomatic arguments, but the frequentist methods that are most commonly used in science violate it. This dissertation advances debates about the Likelihood Principle, its near-corollary the Law of Likelihood, and related questions about statistical practice. -
The Likelihood Principle
1 01/28/99 ãMarc Nerlove 1999 Chapter 1: The Likelihood Principle "What has now appeared is that the mathematical concept of probability is ... inadequate to express our mental confidence or diffidence in making ... inferences, and that the mathematical quantity which usually appears to be appropriate for measuring our order of preference among different possible populations does not in fact obey the laws of probability. To distinguish it from probability, I have used the term 'Likelihood' to designate this quantity; since both the words 'likelihood' and 'probability' are loosely used in common speech to cover both kinds of relationship." R. A. Fisher, Statistical Methods for Research Workers, 1925. "What we can find from a sample is the likelihood of any particular value of r [a parameter], if we define the likelihood as a quantity proportional to the probability that, from a particular population having that particular value of r, a sample having the observed value r [a statistic] should be obtained. So defined, probability and likelihood are quantities of an entirely different nature." R. A. Fisher, "On the 'Probable Error' of a Coefficient of Correlation Deduced from a Small Sample," Metron, 1:3-32, 1921. Introduction The likelihood principle as stated by Edwards (1972, p. 30) is that Within the framework of a statistical model, all the information which the data provide concerning the relative merits of two hypotheses is contained in the likelihood ratio of those hypotheses on the data. ...For a continuum of hypotheses, this principle -
WILLIAM GEMMELL COCHRAN July 15, 1909-March29, 1980
NATIONAL ACADEMY OF SCIENCES WILLIAM GEMMELL C OCHRAN 1909—1980 A Biographical Memoir by MORRIS HANSEN AND FREDERICK MOSTELLER Any opinions expressed in this memoir are those of the author(s) and do not necessarily reflect the views of the National Academy of Sciences. Biographical Memoir COPYRIGHT 1987 NATIONAL ACADEMY OF SCIENCES WASHINGTON D.C. WILLIAM GEMMELL COCHRAN July 15, 1909-March29, 1980 BY MORRIS HANSEN AND FREDERICK MOSTELLER ILLIAM GEMMELL COCHRAN was born into modest Wcircumstances on July 15, 1909, in Rutherglen, Scot- land. His father, Thomas, the eldest of seven children, had begun his lifetime employment with the railroad at the age of thirteen. The family, consisting of Thomas, his wife Jean- nie, and sons Oliver and William, moved to Gourock, a hol- iday resort town on the Firth of Clyde, when William was six, and to Glasgow ten years later. Oliver has colorful recollections of their childhood. At age five, Willie (pronounced Wully), as he was known to family and friends, was hospitalized for a burst appendix, and his life hung in the balance for a day. But soon he was home, wearying his family with snatches of German taught him by a German patient in his nursing-home ward. Willie had a knack for hearing or reading something and remembering it. Oliver recalls that throughout his life, Willie would walk or sit around reciting poems, speeches, advertisements, mu- sic hall songs, and in later life oratorios and choral works he was learning. Until Willie was sixteen, the family lived in an apartment known in Scotland as a "two room and kitchen"—a parlor- cum-dining room (used on posh occasions, about twelve times a year), a bedroom used by the parents, and a kitchen. -
The Development of Statistical Design and Analysis Concepts at Rothamsted
The Development of Statistical Design and Analysis Concepts at Rothamsted Roger Payne, VSN International,Hemel Hempstead, Herts, UK (and Rothamsted Research, Harpenden, Herts UK). email: [email protected] DEMA 2011 1/9/11 Rothamsted 100 years ago •significance test (Arbuthnot 1710) •Bayes theorem (Bayes 1763) •least squares (Gauss 1809, Legendre 1805) •central limit theorem (Laplace 1812) • distributions of large-samples tend to become Normal •"Biometric School" Karl Pearson, University College • correlation, chi-square, method of moments •t-test (Gosset 1908) •Fisher in Stats Methods for Research Workers • "..traditional machinery of statistical processes is wholly unsuited to the needs of practical research. Not only does it take a cannon to shoot a sparrow, but it misses the sparrow!" .. Rothamsted • Broadbalk – set up by Sir John Lawes in 1843 to study the effects of inorganic fertilisers on crop yields • had some traces of factorial structure, but no replication, no randomization and no blocking • and not analysed statistically until 1919.. Broadbalk •treatments on the strips 01 (Fym) N4 11 N4 P Mg 21 Fym N3 12 N1+3+1 (P) K2 Mg2 22 Fym 13 N4 P K 03 Nil 14 N4 P K* (Mg*) 05 (P) K Mg 15 N5 (P) K Mg 06 N1 (P) K Mg 16 N6 (P) K Mg 07 N2 (P) K Mg 17 N1+4+1 P K Mg 08 N3 (P) K Mg 18 N1+2+1 P K Mg 09 N4 (P) K Mg 19 N1+1+1 K Mg 10 N4 20 N4 K Mg •hints of factorial structure (e.g. -
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. -
Frequentist and Bayesian Statistics
Faculty of Life Sciences Frequentist and Bayesian statistics Claus Ekstrøm E-mail: [email protected] Outline 1 Frequentists and Bayesians • What is a probability? • Interpretation of results / inference 2 Comparisons 3 Markov chain Monte Carlo Slide 2— PhD (Aug 23rd 2011) — Frequentist and Bayesian statistics What is a probability? Two schools in statistics: frequentists and Bayesians. Slide 3— PhD (Aug 23rd 2011) — Frequentist and Bayesian statistics Frequentist school School of Jerzy Neyman, Egon Pearson and Ronald Fischer. Slide 4— PhD (Aug 23rd 2011) — Frequentist and Bayesian statistics Bayesian school “School” of Thomas Bayes P(D|H) · P(H) P(H|D)= P(D|H) · P(H)dH Slide 5— PhD (Aug 23rd 2011) — Frequentist and Bayesian statistics Frequentists Frequentists talk about probabilities in relation to experiments with a random component. Relative frequency of an event, A,isdefinedas number of outcomes consistent with A P(A)= number of experiments The probability of event A is the limiting relative frequency. Relative frequency 0.0 0.2 0.4 0.6 0.8 1.0 020406080100 n Slide 6— PhD (Aug 23rd 2011) — Frequentist and Bayesian statistics Frequentists — 2 The definition restricts the things we can add probabilities to: What is the probability of there being life on Mars 100 billion years ago? We assume that there is an unknown but fixed underlying parameter, θ, for a population (i.e., the mean height on Danish men). Random variation (environmental factors, measurement errors, ...) means that each observation does not result in the true value. Slide 7— PhD (Aug 23rd 2011) — Frequentist and Bayesian statistics The meta-experiment idea Frequentists think of meta-experiments and consider the current dataset as a single realization from all possible datasets. -
Statistical Inference a Work in Progress
Ronald Christensen Department of Mathematics and Statistics University of New Mexico Copyright c 2019 Statistical Inference A Work in Progress Springer v Seymour and Wes. Preface “But to us, probability is the very guide of life.” Joseph Butler (1736). The Analogy of Religion, Natural and Revealed, to the Constitution and Course of Nature, Introduction. https://www.loc.gov/resource/dcmsiabooks. analogyofreligio00butl_1/?sp=41 Normally, I wouldn’t put anything this incomplete on the internet but I wanted to make parts of it available to my Advanced Inference Class, and once it is up, you have lost control. Seymour Geisser was a mentor to Wes Johnson and me. He was Wes’s Ph.D. advisor. Near the end of his life, Seymour was finishing his 2005 book Modes of Parametric Statistical Inference and needed some help. Seymour asked Wes and Wes asked me. I had quite a few ideas for the book but then I discovered that Sey- mour hated anyone changing his prose. That was the end of my direct involvement. The first idea for this book was to revisit Seymour’s. (So far, that seems only to occur in Chapter 1.) Thinking about what Seymour was doing was the inspiration for me to think about what I had to say about statistical inference. And much of what I have to say is inspired by Seymour’s work as well as the work of my other professors at Min- nesota, notably Christopher Bingham, R. Dennis Cook, Somesh Das Gupta, Mor- ris L. Eaton, Stephen E. Fienberg, Narish Jain, F. Kinley Larntz, Frank B. -
Good, Irving John Employment Grants Degrees and Honors
CV_July_24_04 Good, Irving John Born in London, England, December 9, 1916 Employment Foreign Office, 1941-45. Worked at Bletchley Park on Ultra (both the Enigma and a Teleprinter encrypting machine the Schlüsselzusatz SZ40 and 42 which we called Tunny or Fish) as the main statistician under A. M. Turing, F. R. S., C. H. O. D. Alexander (British Chess Champion twice), and M. H. A. Newman, F. R. S., in turn. (All three were friendly bosses.) Lecturer in Mathematics and Electronic Computing, Manchester University, 1945-48. Government Communications Headquarters, U.K., 1948-59. Visiting Research Associate Professor, Princeton, 1955 (Summer). Consultant to IBM for a few weeks, 1958/59. (Information retrieval and evaluation of the Perceptron.) Admiralty Research Laboratory, 1959-62. Consultant, Communications Research Division of the Institute for Defense Analysis, 1962-64. Senior Research Fellow, Trinity College, Oxford, and Atlas Computer Laboratory, Science Research Council, Great Britain, 1964-67. Professor (research) of Statistics, Virginia Polytechnic Institute and State University since July, 1967. University Distinguished Professor since Nov. 19, 1969. Emeritus since July 1994. Adjunct Professor of the Center for the Study of Science in Society from October 19, 1983. Adjunct Professor of Philosophy from April 6, 1984. Grants A grant, Probability Estimation, from the U.S. Dept. of Health, Education and Welfare; National Institutes of Health, No. R01 Gm 19770, was funded in 1970 and was continued until July 31, 1989. (Principal Investigator.) Also involved in a grant obtained by G. Tullock and T. N. Tideman on Public Choice, NSF Project SOC 78-06180; 1977-79. Degrees and Honors Rediscovered irrational numbers at the age of 9 (independently discovered by the Pythagoreans and described by the famous G.H.Hardy as of “profound importance”: A Mathematician’s Apology, §14). -
“It Took a Global Conflict”— the Second World War and Probability in British
Keynames: M. S. Bartlett, D.G. Kendall, stochastic processes, World War II Wordcount: 17,843 words “It took a global conflict”— the Second World War and Probability in British Mathematics John Aldrich Economics Department University of Southampton Southampton SO17 1BJ UK e-mail: [email protected] Abstract In the twentieth century probability became a “respectable” branch of mathematics. This paper describes how in Britain the transformation came after the Second World War and was due largely to David Kendall and Maurice Bartlett who met and worked together in the war and afterwards worked on stochastic processes. Their interests later diverged and, while Bartlett stayed in applied probability, Kendall took an increasingly pure line. March 2020 Probability played no part in a respectable mathematics course, and it took a global conflict to change both British mathematics and D. G. Kendall. Kingman “Obituary: David George Kendall” Introduction In the twentieth century probability is said to have become a “respectable” or “bona fide” branch of mathematics, the transformation occurring at different times in different countries.1 In Britain it came after the Second World War with research on stochastic processes by Maurice Stevenson Bartlett (1910-2002; FRS 1961) and David George Kendall (1918-2007; FRS 1964).2 They also contributed as teachers, especially Kendall who was the “effective beginning of the probability tradition in this country”—his pupils and his pupils’ pupils are “everywhere” reported Bingham (1996: 185). Bartlett and Kendall had full careers—extending beyond retirement in 1975 and ‘85— but I concentrate on the years of setting-up, 1940-55. -
“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. -
The Emergence of Modern Statistics in Agricultural Science
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by DepositOnce Giuditta Parolini The emergence of modern statistics in agricultural science Analysis of variance, experimental design and the reshaping of research at Rothamsted Experimental Station, 1919–1933 Postprint This version is available at https://doi.org/10.14279/depositonce-6739. Suggested Citation Parolini, Giuditta: The emergence of modern statistics in agricultural science: Analysis of variance, experimental design and the reshaping of research at Rothamsted Experimental Station, 1919–1933. - In: Journal of the History of Biology. - ISSN: 1573-0387. - 48 (2014) 2. - pp. 301-335. - DOI: 10.1007/s10739-014-9394-z. (Postprint version is cited, page numbers may differ.) Terms of Use This is a post-peer-review, pre-copyedit version of an article published in Journal of the History of Biology. The final authenticated version is available online at: https://doi.org/10.1007/s10739-014-9394-z. Powered by TCPDF (www.tcpdf.org) The Emergence of Modern Statistics in Agricultural Science: Analysis of Variance, Experimental Design and the Reshaping of Research at Rothamsted Experimental Station, 1919-1933. Giuditta Parolini Postdoctoral fellow, Technische Universität Berlin and Berliner Zentrum für Wissensgeschichte Statistical methods have transformed experimental practices in the biological, medical, and social sciences during the twentieth century. Qualitative evidence has largely been replaced by quantitative results and the tools of statistical inference have helped foster a new ideal of objectivity in scientific knowledge (Gigerenzer et al., 1989; Porter, 1996, chapter 8). Increasingly engaged as consultants in experimental research as well as in business and administration, statisticians have represented themselves as the “backroom boys” (Fisher, 1953, p.