DOCSLIB.ORG

Same 14000-Word Version In

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Same 14000-Word Version In The Introductory Statistics Course: The Entity-Property-Relationship Approach Donald B. Macnaughton* introduction to the simplicity, beauty, and truth of the This paper proposes five concepts for discussion at the scientific method. beginning of an introductory statistics course: (1) entities, Teachers must therefore reshape the introductory (2) properties of entities (which are roughly equivalent to course. Many teachers have already contributed to the variables), (3) a major goal of empirical research: to pre- reshaping, as noted below. This paper proposes further dict and control the values of variables, (4) relationships changes. between variables as a key to prediction and control, and I focus on the introductory statistics course for stu- (5) statistical techniques for studying relationships be- dents who are not majoring in statistics or mathematics, tween variables as a means to accurate prediction and whom I call “non-statistics-majors”. Most students who control. After students have learned the five concepts take an introductory statistics course are members of this they learn standard statistical topics in terms of the con- group. The introductory course for non-statistics-majors cepts. It is recommended that students learn the material is important because it is the main seedbed of public through numerous practical examples. It is argued that the opinion about the field of statistics. approach gives students a lasting appreciation of the vital Section 2 recommends two goals for the introductory role of the field of statistics in empirical research. statistics course. Section 3 proposes a sequence of five KEY WORDS: Statistical education; Prediction; Control; concepts for discussion at the beginning of an introductory Role of statistics in empirical research. course. Section 4 argues that the five concepts provide a broad and deep foundation on which to build the field of statistics. Section 5 reports on tests of the approach in 1. INTRODUCTION three courses. Section 6 discusses considerations for Two former presidents of the American Statistical As- teachers wishing to use the approach, and section 7 gives a sociation have stated that “students frequently view statis- summary. tics as the worst course taken in college” (Hogg 1991, Iman 1994). A third former president has stated that the 2. COURSE GOALS field of statistics is in a “crisis” and the subject has be- 2.1 The Value of Emphasizing Goals come “irrelevant to much of scientific enquiry” (Box Emphasizing the goals of any undertaking helps us to 1995). Many statisticians reluctantly agree with these define and focus on what is most important. Otherwise, remarks. we may take a scattershot approach, which is generally In contrast, many statisticians agree that the field of less efficient. Thus, following Hogg (1990, 1992), I rec- statistics is a fundamental tool of the scientific method ommend that all introductory statistics courses have care- which, in turn, plays a key role in modern society. Thus fully stated goals. Ask yourself—what are the goals of the rather than being a worst course and possibly irrelevant, introductory statistics course most familiar to you? the introductory statistics course ought to be a friendly I have observed goal-setting exercises in which the goals were given much attention for a brief period and then forgotten—a waste of a valuable resource. Since a *Donald B. Macnaughton is president of MatStat Research Consulting teacher’s chosen goals are (by the teacher’s own defini- Inc, 246 Cortleigh Blvd., Toronto Ontario, Canada M5N 1P7. E-mail: [email protected] Portions of this material were presented at the Joint tion) what is most important, I recommend that teachers Statistical Meetings in San Francisco, August 11, 1993, at the Joint Statis- regularly revisit their course goals to ask (a) if the goals tical Meetings in Orlando, August 15, 1995, and at the Joint Statistical are still valid and (b) if the day-to-day operations are ef- Meetings in Dallas, August 12, 1998. The author thanks Professor John fectively serving the goals. Flowers of the School of Physical and Health Education, University of Toronto and Professors Donald F. Burrill and Alexander Even of The Ontario Institute for Studies in Education for testing the approach in their 2.2 A Definition of “Empirical Research” statistics courses. Their comments and their students’ comments helped In recommending goals for the introductory statistics substantially to clarify the ideas. The author also acknowledges insightful course I refer to “empirical research”, which thus deserves comments and criticisms from David R. Bellhouse, Carol J. Blumberg, a definition: Christopher Chatfield, George W. Cobb, Sir David Cox, W. Edwards Deming, David J. Finney, Robert W. Frick, Joan B. Garfield, Robert V. Empirical research is any research in which Hogg, Olaf E. Kraulis, Gudmund R. Iversen, William H. Kruskal, Alexan- data are gathered from the external world der M. Macnaughton, Christine E. McLaren, David S. Moore, Thomas L. and then conclusions are drawn from the Moore, Jerry Moreno, John A. Nelder, Ingram Olkin, Allan J. Rossman, data about the external world. Richard L. Scheaffer, Milo A. Schield, Stephen Senn, Gary Smith, and Paul F. Velleman. The Introductory Statistics Course: The Entity-Property-Relationship Approach 2. Empirical research is a key step of the scientific Other writers who discuss goals for the introductory method, which is crucial in many areas of human en- statistics course include Hogg (1990, 1991), Chromiak, deavor, such as in science, education, business, industry, Hoefler, Rossman, and Tesman (1992), Cobb (1992), and government. (No statement of fact in any branch of Iversen (1992), Watkins, Burrill, Landwehr, and Scheaffer science is accepted until it has been verified through care- (1992), Hoerl and Snee (1995), Gal and Garfield (1997a, ful empirical research.) pp. 2 - 5), and Moore (1997a). 2.3 Topic-Based Goals Have a Serious Drawback 3. FIVE CONCEPTS Many introductory statistics courses have what can be To help define the role of statistics, let us consider a called “topic-based” goals. A teacher using such goals sequence of five concepts I recommend for discussion at does not specify general goals, but instead simply speci- the beginning of an introductory statistics course. To fies a list of statistical topics to be covered in the course— make the approach easy for teachers to use, I present each that is, the teacher specifies a syllabus. For example, a concept mainly as a highly condensed version of how it teacher of a traditional introductory course might aim to might be presented to students. cover (in specified amounts of detail) the topics of prob- (In this paper I discuss the five concepts mostly in ab- ability theory, distribution theory, point and interval esti- stract terms because this gives readers a compact over- mation, and so on. Similarly, a teacher of an activity- view of the recommended approach. The abstractness of based course might make a list of statistical topics and my discussion has led readers of an earlier version of the then assign various activities to the students in order to paper to criticize the approach as being too abstract for cover the topics. students to understand [Macnaughton 1998a, app. C]. Unfortunately, topic-based goals have a serious draw- Please note that I am not suggesting that we present the back: By emphasizing lower-level statistical topics, these concepts to students in the compact form in which I de- goals usually fail to emphasize what is essential, which is scribe them below. Instead, I recommend that the con- to help students to appreciate the vital role or use of the cepts be presented in terms of numerous practical exam- field of statistics. Unless students understand and appre- ples. I further discuss presentation issues in section 6 and ciate the general role of statistics, knowledge of statistical I illustrate how the ideas might be presented to students in topics will be both of little interest to them and of little a paper [1996a].) use. Let us begin with what may be the most fundamental concept of human reality. 2.4 Recommended Goals I recommend that the goals of an introductory statis- Concept 1: Entities tics course be If you stop and observe your train of thought at this 1. to give students a lasting appreciation of the vital role moment, you will probably agree that you think about of the field of statistics in empirical research and things. For example, during the course of a minute or so, 2. to teach students to understand and use some useful you might think about, among other things, a friend, an statistical methods in empirical research. appointment, today’s weather, and an idea. Each of these These goals imply a commitment to the practical ap- things is an example of an entity. plication of statistics in empirical research. Emphasis on Many different types of entities exist, for example practical applications is important because the social value • organisms (e.g., people) of statistics is through its practical applications, and not • inanimate physical objects (directly) through theory or mathematics. Thus we stand a • events much better chance of impressing students with the use- • processes fulness of statistics if we discuss practical applications. • ideas, emotions (We must also discuss generalizations that link statis- • societal entities (e.g., educational institutions, govern- tical ideas together, but [to maximize appreciation] practi- ments, businesses) cal applications deserve strong emphasis. More on gener- • symbols alizations below.) • forces The first goal refers to the role of statistics in empiri- • waves cal research. Clearly, to satisfy this goal, we need a pre- • mathematical entities (e.g., sets, numbers, vectors). cise sense of what the role is. I propose a definition of the Entities are fundamental units of human reality be- role in subsection 4.7. I discuss in a paper how we might cause everything in human reality is an entity.
Load more

Recommended publications
  • JSM 2017 in Baltimore the 2017 Joint Statistical Meetings in Baltimore, Maryland, Which Included the CONTENTS IMS Annual Meeting, Took Place from July 29 to August 3
    Volume 46 • Issue 6 IMS Bulletin September 2017 JSM 2017 in Baltimore The 2017 Joint Statistical Meetings in Baltimore, Maryland, which included the CONTENTS IMS Annual Meeting, took place from July 29 to August 3. There were over 6,000 1 JSM round-up participants from 52 countries, and more than 600 sessions. Among the IMS program highlights were the three Wald Lectures given by Emmanuel Candès, and the Blackwell 2–3 Members’ News: ASA Fellows; ICM speakers; David Allison; Lecture by Martin Wainwright—Xiao-Li Meng writes about how inspirational these Mike Cohen; David Cox lectures (among others) were, on page 10. There were also five Medallion lectures, from Edoardo Airoldi, Emery Brown, Subhashis Ghoshal, Mark Girolami and Judith 4 COPSS Awards winners and nominations Rousseau. Next year’s IMS lectures 6 JSM photos At the IMS Presidential Address and Awards session (you can read Jon Wellner’s 8 Anirban’s Angle: The State of address in the next issue), the IMS lecturers for 2018 were announced. The Wald the World, in a few lines lecturer will be Luc Devroye, the Le Cam lecturer will be Ruth Williams, the Neyman Peter Bühlmann Yuval Peres 10 Obituary: Joseph Hilbe lecture will be given by , and the Schramm lecture by . The Medallion lecturers are: Jean Bertoin, Anthony Davison, Anna De Masi, Svante Student Puzzle Corner; 11 Janson, Davar Khoshnevisan, Thomas Mikosch, Sonia Petrone, Richard Samworth Loève Prize and Ming Yuan. 12 XL-Files: The IMS Style— Next year’s JSM invited sessions Inspirational, Mathematical If you’re feeling inspired by what you heard at JSM, you can help to create the 2018 and Statistical invited program for the meeting in Vancouver (July 28–August 2, 2018).
    [Show full text]
  • You May Be (Stuck) Here! and Here Are Some Potential Reasons Why
    You may be (stuck) here! And here are some potential reasons why. As published in Benchmarks RSS Matters, May 2015 http://web3.unt.edu/benchmarks/issues/2015/05/rss-matters Jon Starkweather, PhD 1 Jon Starkweather, PhD [email protected] Consultant Research and Statistical Support http://www.unt.edu http://www.unt.edu/rss RSS hosts a number of “Short Courses”. A list of them is available at: http://www.unt.edu/rss/Instructional.htm Those interested in learning more about R, or how to use it, can find information here: http://www.unt.edu/rss/class/Jon/R_SC 2 You may be (stuck) here! And here are some potential reasons why. I often read R-bloggers (Galili, 2015) to see new and exciting things users are doing in the wonderful world of R. Recently I came across Norm Matloff’s (2014) blog post with the title “Why are we still teaching t-tests?” To be honest, many RSS personnel have echoed Norm’s sentiments over the years. There do seem to be some fields which are perpetually stuck in decades long past — in terms of the statistical methods they teach and use. Reading Norm’s post got me thinking it might be good to offer some explanations, or at least opinions, on why some fields tend to be stubbornly behind the analytic times. This month’s article will offer some of my own thoughts on the matter. I offer these opinions having been academically raised in one such Rip Van Winkle (Washington, 1819) field and subsequently realized how much of what I was taught has very little practical utility with real world research problems and data.
    [Show full text]
  • Statistics Making an Impact
    John Pullinger J. R. Statist. Soc. A (2013) 176, Part 4, pp. 819–839 Statistics making an impact John Pullinger House of Commons Library, London, UK [The address of the President, delivered to The Royal Statistical Society on Wednesday, June 26th, 2013] Summary. Statistics provides a special kind of understanding that enables well-informed deci- sions. As citizens and consumers we are faced with an array of choices. Statistics can help us to choose well. Our statistical brains need to be nurtured: we can all learn and practise some simple rules of statistical thinking. To understand how statistics can play a bigger part in our lives today we can draw inspiration from the founders of the Royal Statistical Society. Although in today’s world the information landscape is confused, there is an opportunity for statistics that is there to be seized.This calls for us to celebrate the discipline of statistics, to show confidence in our profession, to use statistics in the public interest and to champion statistical education. The Royal Statistical Society has a vital role to play. Keywords: Chartered Statistician; Citizenship; Economic growth; Evidence; ‘getstats’; Justice; Open data; Public good; The state; Wise choices 1. Introduction Dictionaries trace the source of the word statistics from the Latin ‘status’, the state, to the Italian ‘statista’, one skilled in statecraft, and on to the German ‘Statistik’, the science dealing with data about the condition of a state or community. The Oxford English Dictionary brings ‘statistics’ into English in 1787. Florence Nightingale held that ‘the thoughts and purpose of the Deity are only to be discovered by the statistical study of natural phenomena:::the application of the results of such study [is] the religious duty of man’ (Pearson, 1924).
    [Show full text]
  • ISI99 Daily Bulletin 4
    ISI99 Daily Bulletin 4 Thursday 12 August, 1999 Contents First day • ISI Service Awards 2 • Jan Tinbergen Awards 1999 2 • Changes in the programme 3 • Correction 3 • Statistical Education 3 • Making Statistics Come Alive 4 • Behind the scenes and in the frontline, part 3 5 IASS - International Association • Many questions were answered at the Information Desk. of Survey Statisticians 6 • Everyday elegance anyone can afford 6 • Golden era of Finnish art 8 • Ateneum, the ”House for the Arts” 9 • Kiasma, the museum of contemporary art 9 • The spirit of the bottle 10 • Harvest in the Market Square 11 • Kuka hän on? 12 • For people on the move 12 Webmasters Markku (left) and Samppa mastering the Web. ISI99 / Daily Bulletin / Thursday, August 12 / 4 1 ISI Service Awards From time to time, the President awards ISI Service Medals to Adolphe Quetelet (Belgium) persons whose outstanding activities were of particular Henri Willem Methorst (The Netherlands) significance for the Institute’s activities. The Service Medals are Franz von Neumann–Spallart (Austria–Hungary) named after three great statisticians who were instrumental in The ISI Executive Committee considers the following persons furthering international statistical co-operation: as deserving candidates for an ISI Service Medal, to be awarded during the August 13 ISI General Assembly meeting in Helsinki: Adolphe Quetelet Medal David Cox (United Kingdom) Ilkka Mellin (Finland) Mario Palma Rojo (Mexico) Timo Relander (Finland) Vijayan N. Nair (Malaysia) Zoltan Kenessey (in memorial -USA) Willem de Vries (Netherlands) Henri Willem Methorst Medal Constance van Eeden (The Netherlands) Agnes Herzberg (Canada) Raija Lofgren (Finland) Joop Mijnheer (The Netherlands) Jozef L.M.
    [Show full text]
  • Error and Inference: an Outsider Stand on a Frequentist Philosophy
    ERROR AND INFERENCE: AN OUTSIDER STAND ON A FREQUENTIST PHILOSOPHY CHRISTIAN P. ROBERT UNIVERSITE´ PARIS-DAUPHINE CEREMADE, IUF, AND CREST, 75775 PARIS CEDEX 16, FRANCE [email protected] Abstract. This note is an extended review of the book Error and Inference, edited by Deborah Mayo and Aris Spanos, about their frequentist and philosophical perspective on testing of hypothesis and on the criticisms of alternatives like the Bayesian approach. 1. Introduction. “The philosophy of science offer valuable tools for understanding and ad- vancing solutions to the problems of evidence and inference in practice”— D. Mayo and A. Spanos, p.xiv, Error and Inference, 2010 This philosophy book, Error and Inference, whose subtitle is “Recent exchanges on experimental reasoning, reliability, and the objectivity and rationality of Science”, includes contributions by P. Achinstein, A. Chalmers, D. Cox, C. Glymour, L. Laudan, A. Musgrave, and J. Worrall, and by both editors, D. Mayo and A. Spanos. It offers reflections on the nature of testing and the defence of the “Error-Statistical philosophy”, proposed by Mayo (1996). Error and Inference is the edited continuation of a conference held at Virginia Tech in 2006, ERROR 06. Given my layman interest in the philosophy of Science, I find that the book reads rather well, despite my missing references in the field. (Paradoxically, the sections I found the hardest to follow were those most centred on Statistics.) The volume is very carefully edited and thus much more than a collection of papers, thanks to the dedications of the editors. In particular, Deborah Mayo added her comments at the end of the chapters of all contributors (but her own’s and Aris Spanos’).
    [Show full text]
  • Probability, Analysis and Number Theory
    Probability, Analysis and Number Theory. Papers in Honour of N. H. Bingham (ed. C. M. Goldie and A. Mijatovi´c), Advances in Applied Probability Special Volume 48A (2016), 1-14. PROBABILITY UNFOLDING, 1965-2015 N. H. BINGHAM Abstract. We give a personal (and we hope, not too idiosyncratic) view of how our subject of Probability Theory has developed during the last half-century, and the author in tandem with it. 1. Introduction. One of the nice things about Probability Theory is that it is still a young subject. Of course it has ancient roots in the real world, as chance is all around us, and draws on the older fields of Analysis on the one hand and Statistics on the other. We take the conventional view that the modern era begins in 1933 with Kolmogorov's path-breaking Grundbegriffe, and agree with Williams' view of probability pre-Kolmogorov as `a shambles' (Williams (2001), 23)1. The first third of the last century was an interesting transitional period, as Measure Theory, the natural machinery with which to do Proba- bility, already existed. For my thoughts on this period, see [72]2, [104]; for the origins of the Grundbegriffe, see e.g. Shafer & Vovk (2006). Regarding the history of mathematics in general, we have among a wealth of sources the two collections edited by Pier (1994, 2000), covering 1900- 50 and 1950-2000. These contain, by Doob and Meyer respectively (Doob (1994), Meyer (2000)), fine accounts of the development of probability; Meyer (2000) ends with his 12-page selection of a chronological list of key publica- tions during the century.
    [Show full text]
  • International Prize in Statistics Awarded to Sir David Cox for Survival Analysis Model Applied in Medicine, Science, and Engineering
    International Prize in Statistics Awarded to Sir David Cox for Survival Analysis Model Applied in Medicine, Science, and Engineering EMBARGOED until October 19, 2016, at 9 p.m. ET ALEXANDRIA, VA (October 18, 2016) – Prominent British statistician Sir David Cox has been named the inaugural recipient of the International Prize in Statistics. Like the acclaimed Fields Medal, Abel Prize, Turing Award and Nobel Prize, the International Prize in Statistics is considered the highest honor in its field. It will be bestowed every other year to an individual or team for major achievements using statistics to advance science, technology and human welfare. Cox is a giant in the field of statistics, but the International Prize in Statistics Foundation is recognizing him specifically for his 1972 paper in which he developed the proportional hazards model that today bears his name. The Cox Model is widely used in the analysis of survival data and enables researchers to more easily identify the risks of specific factors for mortality or other survival outcomes among groups of patients with disparate characteristics. From disease risk assessment and treatment evaluation to product liability, school dropout, reincarceration and AIDS surveillance systems, the Cox Model has been applied essentially in all fields of science, as well as in engineering. “Professor Cox changed how we analyze and understand the effect of natural or human- induced risk factors on survival outcomes, paving the way for powerful scientific inquiry and discoveries that have impacted
    [Show full text]
  • Curriculum Vitae
    Curriculum Vitae Nancy Margaret Reid O.C. April, 2021 BIOGRAPHICAL INFORMATION Personal University Address: Department of Statistical Sciences University of Toronto 700 University Avenue 9th floor Toronto, Ontario M5S 1X6 Telephone: (416) 978-5046 Degrees 1974 B.Math University of Waterloo 1976 M.Sc. University of British Columbia 1979 Ph.D. Stanford University Thesis: “Influence functions for censored data” Supervisor: R.G. Miller, Jr. 2015 D.Math. (Honoris Causa) University of Waterloo Employment 1-3/2020 Visiting Professor SMRI University of Sydney 1-3/2020 Visiting Professor Monash University Melbourne 10-11/2012 Visiting Professor Statistical Science University College, London 2007-2021 Canada Research Chair Statistics University of Toronto 2003- University Professor Statistics University of Toronto 1988- Professor Statistics University of Toronto 2002-2003 Visiting Professor Mathematics EPF, Lausanne 1997-2002 Chair Statistics University of Toronto 1987- Tenured Statistics University of Toronto 1986-88 Associate Professor Statistics University of Toronto Appointed to School of Graduate Studies 1986/01-06 Visiting Associate Professor Mathematics Univ. of Texas at Austin 1985/07-12 Visiting Associate Professor Biostatistics Harvard School of Public Health 1985-86 Associate Professor Statistics Univ. of British Columbia Tenured Statistics Univ. of British Columbia 1980-85 Assistant Professor Statistics & Mathematics Univ. of British Columbia 1979-80 Nato Postdoctoral Fellow Mathematics Imperial College, London 1 Honours 2020 Inaugural
    [Show full text]
  • 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).
    [Show full text]
  • “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.
    [Show full text]
  • Newsletter December 2012
    Newsletter December 2012 2013 International Year of Statistics Richard Emsley As many readers will know, the BIR is its population, economy and education or other topics for the website at any actively participating in the Interna- system; the health of its citizens; and time by emailing the items to Richard tional Year of Statistics 2013, and similar social, economic, government Emsley and Jeff Myers of the Ameri- there are several ways that members and other relevant topics. can Statistical Association at . are encouraged to contribute to this Around the World in Statistics—A short event. Please join in where you can! The Steering Committee are also seek- 150 to 200 word article about how ing a core group of regular bloggers When the public-focused website of statistics is making life better for the to feature on the new website blog the International Year of Statistics is people of your country and are particu- unveiled later this year it will feature Statistician Job of the Week—A short larly keen for lots of exciting content informing mem- 150 to 250 word article, written by young statisti- bers of the public about the role of any members talking about his/her job cians to contrib- statistics in everyday life. So that this as a statistician and how he/she is ute to this. If content remains fresh and is frequently contributing to improving our global anyone would updated, the Steering Committee is society. like to become a seeking content contributions from par- blogger and ticipating organizations throughout the Statistics in Action—A photo with a contribute to this world in several subject areas.
    [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]