DOCSLIB.ORG

Rothamsted in the Making of Sir Ronald Fisher Sc.D., F.R.S

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Rothamsted in the Making of Sir Ronald Fisher Sc.D., F.R.S Rothamsted in the Making of Sir Ronald Fisher Sc.D., F.R.S. John Aldrich Economics Department University of Southampton Southampton SO17 1BJ UK e-mail: [email protected] Abstract In 1919 the agricultural station at Rothamsted hired Ronald Fisher (1890-1962) to analyse historic data on crop yields. For him it was the beginning of a spectacular career and for Rothamsted the beginning of a Statistics Department which became a force in world statistics. Fisher arrived with publications in mathematical statistics and genetics. These were established subjects but at Rothamsted between 1919 and -33 he created the new career of agricultural research statistician. The paper considers how Rothamsted, in the person of its Director Sir John Russell, nurtured this development while supporting Fisher’s continuing work in mathematical statistics and genetics. It considers too how people associated with Fisher at Rothamsted, including assistants Wishart, Irwin and Yates and voluntary workers Tippett, Hoblyn and Hotelling contributed to establishing Fisherian statistics. September 2019 1 Introduction In 1919 a 29 year-old Cambridge mathematics graduate with no established profession started at Rothamsted Experimental Station; he died in 1962 “the most famous statistician and mathematical biologist in the world”—thus Irwin et al. (1963: 159). Sir Ronald Fisher Sc.D., F.R.S. was Balfour Professor at Cambridge when he was knighted and had been Galton Professor at University College but the higher doctorate and Fellowship of the Royal Society came when he was at Rothamsted. Ronald Aylmer Fisher would be another Galton and Balfour Professor but at Rothamsted he was the first Chief Statistician or even statistician. Between 1919 and 1933 he made a career for himself by creating the career of agricultural research statistician. What he did is sketched by Irwin et al. and detailed in Box’s Life (1978) and in numerous technical studies—see Aldrich (2003/19)—but here I look behind the what to consider how Fisher managed—and was helped—to do it. Rothamsted stimulated him to do the work in agriculture and, more surprisingly, encouraged the work in mathematical statistics and genetics that brought him fame; it also helped create a following to bestow that fame. Fisher at Rothamsted sits in larger histories—of mathematical statistics, of mathematicians in British statistics, of statistics in agriculture and of agricultural science in Britain—and I have drawn on these literatures.1 I also glance at parallel lives and institutions and at Fisher’s experience after Rothamsted, developing Box’s (1978: 96) observation that he “was never happier professionally than during the years he spent there.” But I start with the ideas, people and institutions that together provided a launch-pad for the career of agricultural research statistician. 1 Thus Parolini (2015a) tells much of the present story from another angle. 2 1 A platform for the agricultural research statistician In 1948, when Frank Yates Fisher’s successor at Rothamsted was made FRS, he told Sir John Russell that the award “must be personally gratifying to you also in that you are as it were the god-father of agricultural research statistics.”2 That god-paternity went back to the time when, as Director, Edward John Russell (1872-1965; FRS 1917), contemplated the “mass of valuable data” from the Rothamsted experiments and looked forward to a time when “conditions become more normal.”3 Russell (1917: v) mused, “I cannot help thinking that the application to them of modern statistical methods would yield information of high value both to the man of science and to the practical agriculturist.” Men of science were already using statistical methods and I recall their work as it prepared the way for the office of agricultural research statistician and treated problems—especially in field experiments and agricultural meteorology—later taken up by Fisher. I start with the research setting and the sources of statistical expertise before proceeding to the scene at the close of the Great War. The pre-war agricultural research community was small and, though dispersed across experimental stations, agricultural colleges, universities and even businesses, strongly inter-connected with findings disseminated through academic, trade and official publications. The leading professional forces were Rothamsted Experimental Station, the Cambridge University School of Agriculture and the Journal of Agricultural Science—of this last Russell (1966: 286) wrote, “for some forty years it published almost all the chief papers in agricultural science in Great Britain.” Rothamsted, the life work of John Lawes and Henry Gilbert, started in 1843 while the 2 Yates was replying to Russell’s letter of congratulation. 3 For Russell see his Autobiography (1956) and Thornton’s (1966) memoir. 3 School and journal dated from 1899 and 1905 respectively. Public support for agriculture and agricultural research—much increased after 1910—was through the Board of Agriculture and Fisheries, renamed Ministry in 1919.4 The Board had a branch responsible for agricultural statistics, i.e., the statistics of the agricultural sector—the numbers for agricultural economics.5 The numbers also interested the statisticians of the Royal Statistical Society and the close link between Board and Society is illustrated by the award of the Society’s highest honour, the Guy Medal in gold, to the Board’s P. G. Craigie (1843-1930) for “extraordinary services to statistical science in connection with the development of agricultural statistics.” 6 The Society’s domain was economic, demographic and social statistics with interest in statistical theory confined to a few, notably Edgeworth, Bowley and Yule.7 In 1869 the Journal had published a “statistical description” of some Rothamsted experiments—by Frederick Purdy, a compiler of Poor Law statistics—but this was an isolated venture and agricultural experiments only became the Society’s statistics in the 1930s; see §7 below. The Society’s statistics did not stretch to matters like ascertaining “the conditions under which the plant grows and the soil supplies it with nutriment”—the purpose of 4 Russell (1966: ch. VII-VIII) and Vernon (1997) survey the entire scene. For Rothamsted, see Russell (1917), for the School, Wood (1922), and for the JAS, Bell (1980). 5 “On the Home Produce, Imports, Consumption, and Price of Wheat etc.” (1880a) the one paper written by Lawes and Gilbert for statisticians illustrates the point; Gower (1988: 181-2) comments on them as statisticians. 6 Craigie headed the Board’s Statistical, Intelligence, and Educational Branch; see Anon (1930). 7 Aldrich (2010a) examines the place of theory in the Society. 4 the Rothamsted experiments, as stated by Hall (1906: vi). However, in 1907 the Journal published a piece on agricultural meteorology which was agricultural science—cf. Lawes and Gilbert (1880b). The author of “Correlation of the weather and crops” was R. H. Hooker (1867-1944), a younger Society/Board figure abreast of modern biometric techniques like correlation.8 Hooker was developing a theme from “Seasons in the British Isles” by Napier Shaw of the Meteorological Office—the Society maintained a watching brief for all official statistics. Hooker’s paper did not realise Craigie’s expectation that it would be “the beginning of a series of papers in which the practical knowledge of agriculturalists and the theoretical methods of arithmetical expression might be brought together” and Hooker’s research into crops and weather proved of more interest to meteorologists.9 Agricultural researchers looking for statistical methods turned to the theory of errors or to biometrics.10 Error theory/least squares theory had been developed by mathematicians in the early nineteenth century for measurement problems in astronomy and geodesy and was widely taught and disseminated in textbooks of different levels while biometrics was newer and less accessible. The leader of the biometricians, Karl Pearson (1857-1936) of University College, was a great proselytiser but he was not directly involved with agriculture and his “journal for the statistical study of biological problems”—Biometrika—published its first specifically 8 See Aldrich (1995: 370ff.) for the time series correlation analysis developed by Hooker and his friend Yule. Hooker’s life is recalled by Yule (1944). For Yule see below. 9 The first reference to Hooker in the JAS was Fisher (1921). More immediately Gosset responded to a point raised in the discussion with his “Probable error of a correlation coefficient” (1908b). Hooker eventually became President of the Meteorological Society. 10 For the theory of errors and biometry see Stigler (1986). 5 agricultural article in 1923. However, Gosset and Yule—who may be thought of as Fisher’s forerunners—had been Pearson’s students and two of his American followers, the zoologist Raymond Pearl and botanist J. Arthur Harris, worked in experimental stations and took an interest in agricultural experiments.11 Pearson’s core interest was in biology, including eugenics, and he advocated the use of statistics in medicine though his influence there was chiefly through his disciple Major Greenwood who was at the Lister Institute from 1910. 12 A harbinger of the post-war development of agricultural statistics as the statistics of agricultural experiments was a session on “error in agriculture experiment” jointly organised by the Agricultural and the Statistical Sections for the 1910 British Association conference.13 The papers reflected thinking on how the theory of errors could be used in agricultural research and, in particular, the inferential possibilities of the probable error—how it could make precise statements like this from Hall (1909b: 362), “Plot 12 is probably better than Plot 3 by more than 8.1, and less than 11.9 per cent.” The prime movers were not marginal or junior figures but A. Daniel Hall (1864-1942; FRS 1909), Russell’s predecessor as director of Rothamsted and reviver of its fortunes after the deaths of the founders, and Thomas Wood (1869-1929; FRS 1919), professor of agriculture at Cambridge, both founding editors of the JAS.
Load more

Recommended publications
  • F:\RSS\Me\Society's Mathemarica
    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.
    [Show full text]
  • Introduction to Social Statistics
    SOCY 3400: INTRODUCTION TO SOCIAL STATISTICS MWF 11-12:00; Lab PGH 492 (sec. 13748) M 2-4 or (sec. 13749)W 2-4 Professor: Jarron M. Saint Onge, Ph.D. Office: PGH 489 Phone: (713) 743-3962 Email: [email protected] Office Hours: MW 10-11 (Please email) or by appointment Teaching Assistant: TA Email: Office Hours: TTh 10-12 or by appointment Required Text: McLendon, M. K. 2004. Statistical Analysis in the Social Sciences. Additional materials will be distributed through WebCT COURSE DESCRIPTION: Sociological research relies on experience with both qualitative (e.g. interviews, participant observation) and quantitative methods (e.g., statistical analyses) to investigate social phenomena. This class focuses on learning quantitative methods for furthering our knowledge about the world around us. This class will help students in the social sciences to gain a basic understanding of statistics, whether to understand, critique, or conduct social research. The course is divided into three main sections: (1) Descriptive Statistics; (2) Inferential Statistics; and (3) Applied Techniques. Descriptive statistics will allow you to summarize and describe data. Inferential Statistics will allow you to make estimates about a population (e.g., this entire class) based on a sample (e.g., 10 or 12 students in the class). The third section of the course will help you understand and interpret commonly used social science techniques that will help you to understand sociological research. In this class, you will learn concepts associated with social statistics. You will learn to understand and grasp the concepts, rather than only focusing on getting the correct answers.
    [Show full text]
  • Bayesian Inference for Heterogeneous Event Counts
    Bayesian Inference for Heterogeneous Event Counts ANDREW D. MARTIN Washington University This article presents an integrated set of Bayesian tools one can use to model hetero- geneous event counts. While models for event count cross sections are now widely used, little has been written about how to model counts when contextual factors introduce heterogeneity. The author begins with a discussion of Bayesian cross-sectional count models and discusses an alternative model for counts with overdispersion. To illustrate the Bayesian framework, the author fits the model to the number of women’s rights cosponsorships for each member of the 83rd to 102nd House of Representatives. The model is generalized to allow for contextual heterogeneity. The hierarchical model allows one to explicitly model contextual factors and test alternative contextual explana- tions, even with a small number of contextual units. The author compares the estimates from this model with traditional approaches and discusses software one can use to easily implement these Bayesian models with little start-up cost. Keywords: event count; Markov chain Monte Carlo; hierarchical Bayes; multilevel models; BUGS INTRODUCTION Heterogeneity is what makes society and, for that matter, statistics interesting. However, until recently, little attention has been paidto modeling count data observed in heterogeneous clusters (a notable exception is Sampson, Raudenbush, and Earls 1997). Explanations AUTHOR’S NOTE: This research is supported by the National Science Foundation Law and Social Sciences and Methodology, Measurement, and Statistics Sections, Grant SES-0135855 to Washing- ton University. I would like to thank Dave Peterson, Kevin Quinn, and Kyle Saunders for their helpful comments. I am particularly indebted to Christina Wolbrecht for sharing her valuable data set with me.
    [Show full text]
  • THE HISTORY and DEVELOPMENT of STATISTICS in BELGIUM by Dr
    THE HISTORY AND DEVELOPMENT OF STATISTICS IN BELGIUM By Dr. Armand Julin Director-General of the Belgian Labor Bureau, Member of the International Statistical Institute Chapter I. Historical Survey A vigorous interest in statistical researches has been both created and facilitated in Belgium by her restricted terri- tory, very dense population, prosperous agriculture, and the variety and vitality of her manufacturing interests. Nor need it surprise us that the successive governments of Bel- gium have given statistics a prominent place in their affairs. Baron de Reiffenberg, who published a bibliography of the ancient statistics of Belgium,* has given a long list of docu- ments relating to the population, agriculture, industry, commerce, transportation facilities, finance, army, etc. It was, however, chiefly the Austrian government which in- creased the number of such investigations and reports. The royal archives are filled to overflowing with documents from that period of our history and their very over-abun- dance forms even for the historian a most diflScult task.f With the French domination (1794-1814), the interest for statistics did not diminish. Lucien Bonaparte, Minister of the Interior from 1799-1800, organized in France the first Bureau of Statistics, while his successor, Chaptal, undertook to compile the statistics of the departments. As far as Belgium is concerned, there were published in Paris seven statistical memoirs prepared under the direction of the prefects. An eighth issue was not finished and a ninth one * Nouveaux mimoires de I'Acadimie royale des sciences et belles lettres de Bruxelles, t. VII. t The Archives of the kingdom and the catalogue of the van Hulthem library, preserved in the Biblioth^que Royale at Brussells, offer valuable information on this head.
    [Show full text]
  • Use of Genomic Tools to Discover the Cause of Champagne Dilution Coat Color in Horses and to Map the Genetic Cause of Extreme Lordosis in American Saddlebred Horses
    University of Kentucky UKnowledge Theses and Dissertations--Veterinary Science Veterinary Science 2014 USE OF GENOMIC TOOLS TO DISCOVER THE CAUSE OF CHAMPAGNE DILUTION COAT COLOR IN HORSES AND TO MAP THE GENETIC CAUSE OF EXTREME LORDOSIS IN AMERICAN SADDLEBRED HORSES Deborah G. Cook University of Kentucky, [email protected] Right click to open a feedback form in a new tab to let us know how this document benefits ou.y Recommended Citation Cook, Deborah G., "USE OF GENOMIC TOOLS TO DISCOVER THE CAUSE OF CHAMPAGNE DILUTION COAT COLOR IN HORSES AND TO MAP THE GENETIC CAUSE OF EXTREME LORDOSIS IN AMERICAN SADDLEBRED HORSES" (2014). Theses and Dissertations--Veterinary Science. 15. https://uknowledge.uky.edu/gluck_etds/15 This Doctoral Dissertation is brought to you for free and open access by the Veterinary Science at UKnowledge. It has been accepted for inclusion in Theses and Dissertations--Veterinary Science by an authorized administrator of UKnowledge. For more information, please contact [email protected]. STUDENT AGREEMENT: I represent that my thesis or dissertation and abstract are my original work. Proper attribution has been given to all outside sources. I understand that I am solely responsible for obtaining any needed copyright permissions. I have obtained needed written permission statement(s) from the owner(s) of each third-party copyrighted matter to be included in my work, allowing electronic distribution (if such use is not permitted by the fair use doctrine) which will be submitted to UKnowledge as Additional File. I hereby grant to The University of Kentucky and its agents the irrevocable, non-exclusive, and royalty-free license to archive and make accessible my work in whole or in part in all forms of media, now or hereafter known.
    [Show full text]
  • Tropical Meteorology
    Tropical Meteorology Chapter 01B Topics ¾ El Niño – Southern Oscillation (ENSO) ¾ The Madden-Julian Oscillation ¾ Westerly wind bursts 1 The Southern Oscillation ¾ There is considerable interannual variability in the scale and intensity of the Walker Circulation, which is manifest in the so-called Southern Oscillation (SO). ¾ The SO is associated with fluctuations in sea level pressure in the tropics, monsoon rainfall, and wintertime circulation over the Pacific Ocean and also over North America and other parts of the extratropics. ¾ It is the single most prominent signal in year-to-year climate variability in the atmosphere. ¾ It was first described in a series of papers in the 1920s by Sir Gilbert Walker and a review and references are contained in a paper by Julian and Chervin (1978). The Southern Oscillation ¾ Julian and Chervin (1978) use Walker´s own words to summarize the phenomenon. "By the southern oscillation is implied the tendency of (surface) pressure at stations in the Pacific (San Francisco, Tokyo, Honolulu, Samoa and South America), and of rainfall in India and Java... to increase, while pressure in the region of the Indian Ocean (Cairo, N.W. India, Darwin, Mauritius, S.E. Australia and the Cape) decreases...“ and "We can perhaps best sum up the situation by saying that there is a swaying of pressure on a big scale backwards and forwards between the Pacific and Indian Oceans...". 2 Fig 1.17 The Southern Oscillation ¾ Bjerknes (1969) first pointed to an association between the SO and the Walker Circulation, but the seeds for this association were present in the studies by Troup (1965).
    [Show full text]
  • Strength in Numbers: the Rising of Academic Statistics Departments In
    Agresti · Meng Agresti Eds. Alan Agresti · Xiao-Li Meng Editors Strength in Numbers: The Rising of Academic Statistics DepartmentsStatistics in the U.S. Rising of Academic The in Numbers: Strength Statistics Departments in the U.S. Strength in Numbers: The Rising of Academic Statistics Departments in the U.S. Alan Agresti • Xiao-Li Meng Editors Strength in Numbers: The Rising of Academic Statistics Departments in the U.S. 123 Editors Alan Agresti Xiao-Li Meng Department of Statistics Department of Statistics University of Florida Harvard University Gainesville, FL Cambridge, MA USA USA ISBN 978-1-4614-3648-5 ISBN 978-1-4614-3649-2 (eBook) DOI 10.1007/978-1-4614-3649-2 Springer New York Heidelberg Dordrecht London Library of Congress Control Number: 2012942702 Ó Springer Science+Business Media New York 2013 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Exempted from this legal reservation are brief excerpts in connection with reviews or scholarly analysis or material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work. Duplication of this publication or parts thereof is permitted only under the provisions of the Copyright Law of the Publisher’s location, in its current version, and permission for use must always be obtained from Springer.
    [Show full text]
  • Assortative Mating (4 P.)
    Evolutionary Dynamics: Population Genetics and Ecological Models WS 2019/2020 course number: MA4453-KP05 Assignment 1 due: October 28 Mendel's experiments & Punnett squares (4 P.) In the lecture, we discussed Mendel's experiments with garden peas in which he considered one character (e.g. ower color or seed shape). In a second step, he considered two characters simultaneously. He started with two pure lines. One had yellow (dominant allele Y ), wrinkled (recessive allele r) seeds. The other had green (recessive allele y), round (dominant allele R) seeds. I.e. the genotypes of these pure lines were Y Y rr and yyRR. He crossed these two lines to obtain the F1 generation. He then obtained the F2 generation through selng of individuals from the F1 generation. Draw the corresponding Punnett square to determine the genotypes and phenotypes that are generated in this way. What are the proportions of the observed phenotypes if you determine proportions by counting the respective cells in the Punnett square? Mendel indeed observed these proportions. However, in 1905, William Bateson, Edith Re- becca Saunders, and Reginald Punnett performed a similar experiment in sweet pea plants. They simultaneously considered ower color and pollen shape and found dierent proportions than Mendel. How can this discrepancy be explained? You can read Mendel's original publication here (in German!): http: // www. deutschestextarchiv. de/ book/ view/ mendel_ pflanzenhybriden_ 1866? p= 14 Assortative mating (4 P.) In the lecture, we studied randomly mating individuals. However, in reality, mating is often non-random. E.g. individuals may prefer to mate with individuals that are similar to them with respect to some trait.
    [Show full text]
  • Rothamsted in the Making of Sir Ronald Fisher Scd FRS
    Rothamsted in the Making of Sir Ronald Fisher ScD FRS John Aldrich University of Southampton RSS September 2019 1 Finish 1962 “the most famous statistician and mathematical biologist in the world” dies Start 1919 29 year-old Cambridge BA in maths with no prospects takes temporary job at Rothamsted Experimental Station In between 1919-33 at Rothamsted he makes a career for himself by creating a career 2 Rothamsted helped make Fisher by establishing and elevating the office of agricultural statistician—a position in which Fisher was unsurpassed by letting him do other things—including mathematical statistics, genetics and eugenics 3 Before Fisher … (9 slides) The problems were already there viz. o the relationship between harvest and weather o design of experimental trials and the analysis of their results Leading figures in agricultural science believed the problems could be treated statistically 4 Established in 1843 by Lawes and Gilbert to investigate the effective- ness of fertilisers Sir John Bennet Lawes Sir Joseph Henry Gilbert (1814-1900) land-owner, fertiliser (1817-1901) professional magnate and amateur scientist scientist (chemist) In 1902 tired Rothamsted gets make-over when Daniel Hall becomes Director— public money feeds growth 5 Crops and weather and experimental trials Crops & the weather o examined by agriculturalists—e.g. Lawes & Gilbert 1880 o subsequently treated more by meteorologists Experimental trials a hotter topic o treated in Journal of Agricultural Science o by leading figures Wood of Cambridge and Hall
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • Basic Statistics
    Statistics Kristof Reid Assistant Professor Medical University of South Carolina Core Curriculum V5 Financial Disclosures • None Core Curriculum V5 Further Disclosures • I am not a statistician • I do like making clinical decisions based on appropriately interpreted data Core Curriculum V5 Learning Objectives • Understand why knowing statistics is important • Understand the basic principles and statistical test • Understand common statistical errors in the medical literature Core Curriculum V5 Why should I care? Indications for statistics Core Curriculum V5 47% unable to determine study design 31% did not understand p values 38% did not understand sensitivity and specificity 83% could not use odds ratios Araoye et al, JBJS 2020;102(5):e19 Core Curriculum V5 50% (or more!) of clinical research publications have at least one statistical error Thiese et al, Journal of thoracic disease, 2016-08, Vol.8 (8), p.E726-E730 Core Curriculum V5 17% of conclusions not justified by results 39% of studies used the wrong analysis Parsons et al, J Bone Joint Surg Br. 2011;93-B(9):1154-1159. Core Curriculum V5 Are these two columns different? Column A Column B You have been asked by your insurance carrier 19 10 12 11 to prove that your total hip patient outcomes 10 9 20 19 are not statistically different than your competitor 10 17 14 10 next door. 21 19 24 13 Column A is the patient reported score for you 12 18 and column B for your competitor 17 8 12 17 17 10 9 9 How would you prove you are 24 10 21 9 18 18 different or better? 13 4 18 8 12 14 15 15 14 17 Core Curriculum V5 Are they still the same? Column A: Mean=15 Column B: Mean=12 SD=4 Core Curriculum V5 We want to know if we're making a difference Core Curriculum V5 What is statistics? a.
    [Show full text]