Inferential Statistics the Controlled Experiment, Hypothesis Testing, and the Z Distribution
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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. -
The Political Methodologist Newsletter of the Political Methodology Section American Political Science Association Volume 10, Number 2, Spring, 2002
The Political Methodologist Newsletter of the Political Methodology Section American Political Science Association Volume 10, Number 2, Spring, 2002 Editor: Suzanna De Boef, Pennsylvania State University [email protected] Contents Summer 2002 at ICPSR . 27 Notes on the 35th Essex Summer Program . 29 Notes from the Editor 1 EITM Summer Training Institute Announcement 29 Note from the Editor of PA . 31 Teaching and Learning Graduate Methods 2 Michael Herron: Teaching Introductory Proba- Notes From the Editor bility Theory . 2 Eric Plutzer: First Things First . 4 This issue of TPM features articles on teaching the first Lawrence J. Grossback: Reflections from a Small course in the graduate methods sequence and on testing and Diverse Program . 6 theory with empirical methods. The first course presents Charles Tien: A Stealth Approach . 8 unique challenges: what to expect of students and where to begin. The contributions suggest that the first gradu- Christopher H. Achen: Advice for Students . 10 ate methods course varies greatly with respect to goals, content, and rigor across programs. Whatever the na- Testing Theory 12 ture of the course and whether you teach or are taking John Londregan: Political Theory and Political your first methods course, Chris Achen’s advice for stu- Reality . 12 dents beginning the methods sequence will be excellent reading. Rebecca Morton: EITM*: Experimental Impli- cations of Theoretical Models . 14 With the support of the National Science Foun- dation, the empirical implications of theoretical models (EITM) are the subject of increased attention. Given the Articles 16 empirical nature of the endeavor, EITM should inspire us. Andrew D. Martin: LATEX For the Rest of Us . -
1 History of Statistics 8. Analysis of Variance and the Design Of
History of Statistics 8. Analysis of Variance and the Design of Experiments. R. A. Fisher (1890-1962) In the first decades of the twentieth century, the Englishman, Ronald Aylmer Fisher, who always published as R. A. Fisher, radically changed the use of statistics in research. He invented the technique called Analysis of Variance and founded the entire modern field called Design of Experiments. During his active years he was acknowledged as the greatest statistician in the world. He was knighted by Queen Elizabeth in 1952. Fisher’s graduate work in mathematics focused on statistics in the physical sciences, including astronomy. That’s not as surprising as it might seem. Almost all of modern statistical theory is based on fundamentals originally developed for use in astronomy. The progression from astronomy to the life sciences is a major thread in the history of statistics. Two major areas that were integral to the widening application of statistics were agriculture and genetics, and Fisher made major contributions to both. The same basic issue appears in all these areas of research: how to account for the variability in a set of observations. In astronomy the variability is caused mainly by measurement erro r, as when the position of a planet is obtained from different observers using different instruments of unequal reliability. It is assumed, for example, that a planet has a specific well-defined orbit; it’s just that our observations are “off” for various reasons. In biology the variability in a set of observations is not the result of measurement difficulty but rather by R. -
The Economist
Statisticians in World War II: They also served | The Economist http://www.economist.com/news/christmas-specials/21636589-how-statis... More from The Economist My Subscription Subscribe Log in or register World politics Business & finance Economics Science & technology Culture Blogs Debate Multimedia Print edition Statisticians in World War II Comment (3) Timekeeper reading list They also served E-mail Reprints & permissions Print How statisticians changed the war, and the war changed statistics Dec 20th 2014 | From the print edition Like 3.6k Tweet 337 Advertisement “I BECAME a statistician because I was put in prison,” says Claus Moser. Aged 92, he can look back on a distinguished career in academia and civil service: he was head of the British government statistical service in 1967-78 and made a life peer in 2001. But Follow The Economist statistics had never been his plan. He had dreamed of being a pianist and when he realised that was unrealistic, accepted his father’s advice that he should study commerce and manage hotels. (“He thought I would like the music in the lobby.”) War threw these plans into disarray. In 1936, aged 13, he fled Germany with his family; four years later the prime minister, Winston Churchill, decided that because some of the refugees might be spies, he would “collar the lot”. Lord Moser was interned in Huyton, Latest updates » near Liverpool (pictured above). “If you lock up 5,000 Jews we will find something to do,” The Economist explains : Where Islamic he says now. Someone set up a café; there were lectures and concerts—and a statistical State gets its money office run by a mathematician named Landau. -
LADY TASTING TEA Between the Plot Based on Data and the Plot Generated by a Specific Set of Equations
96 THE LADY TASTING TEA between the plot based on data and the plot generated by a specific set of equations. The proof that the proposed generator is correct is based on asking the reader to look at two similar graphs. This eye- ball test has proved to be a fallible one in statistical analysis. Those things that seem to the eye to be similar or very close to the same are often drastically different when examined carefully with statis- tical tools developed for this purpose. PEARSON'S GOODNESS OF FIT TEST This problem was one that Karl Pearson recognized early in his career. One of Pearson's great achievements was the creation of the first "goodness of fit test." By comparing the observed to the predicted values, Pearson was able to produce a statistic that tested the goodness of the fit. He called his test statistic a "chi square goodness of fit test." He used the Greek letter chi (x), since the dis- tribution of this test statistic belonged to a group of his skew distri- butions that he had designated the chi family. Actually, the test statistic behaved like the square of a chi, thus the name "chi squared." Since this is a statistic in Fisher's sense, it has a prob- ability distribution. Pearson proved that the chi square goodness of fit test has a distribution that is the same, regardless of the type of data used. That is, he could tabulate the probability distribution of this statistic and use that same set of tables for every test. -
Lady Tasting Tea: How Milk
book reviews US space programme was grievously mis- conceived from the start and that all the projects designed to assert the nation’s technological virility — putting a few people into low orbits or retrieving fragments of Mars — were pointless extravagances, which did nothing to enlarge human knowledge or LIBRARY BRIDGEMAN ART the limits of useful technology. White makes some curious judgements. He accepts without reservation the (to me) unconvincing evidence that Werner Heisen- berg held back the German atomic bomb project for reasons of conscience; he does not reveal Heisenberg’s famous mistake in esti- mating the critical mass of fissile material. And he exculpates Wernher von Braun, whose activities led to the deaths of some 3,000 Londoners and many more labourers at the Peenemünde missile site; von Braun, he tells us, was “a scientist first and a weapons designer second”, was no overt supporter of the Nazi party and was only doing his humble best to help his country win the war. (So that’s all right, then.) There is no dearth of mistakes, small and not so small. Max Perutz did not “unravel haemoglobin” in the year in which the DNA structure was revealed; Aaron Klug did not win his Nobel prize for genetics; Maurice Statistical conundrum: did the tea go into the cup before or after the milk? Wilkins was not an X-ray crystallographer, nor did he scorn model building; Francis (1935). In this chapter Fisher illustrates the Crick never worked on “the effect of magnet- principles of experimental design by dis- ic fields on development of cells”; the Allies Still waiting for cussing the hypothetical problem of how to did not use the Enigma machine to send mis- test the claim that it is possible to tell whether leading signals to the Germans; Martin Ryle the revolution the tea is put into the cup before or after the was not the originator of the Big Bang theory The Lady Tasting Tea: How milk. -
Cautionary Tales in Designed Experiments
The correct bibliographic citation for this manual is as follows: Salsburg, David S. 2020. Cautionary Tales in Designed Experiments. Cary, NC: SAS Institute Inc. Cautionary Tales in Designed Experiments Copyright © 2020, SAS Institute Inc., Cary, NC, USA ISBN 978-1-952363-24-5 (Paperback) ISBN 978-1-952363-23-8 (Web PDF) All Rights Reserved. Produced in the United States of America. For a hard copy book: No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, or otherwise, without the prior written permission of the publisher, SAS Institute Inc. For a web download or e-book: Your use of this publication shall be governed by the terms established by the vendor at the time you acquire this publication. The scanning, uploading, and distribution of this book via the Internet or any other means without the permission of the publisher is illegal and punishable by law. Please purchase only authorized electronic editions and do not participate in or encourage electronic piracy of copyrighted materials. Your support of others’ rights is appreciated. U.S. Government License Rights; Restricted Rights: The Software and its documentation is commercial computer software developed at private expense and is provided with RESTRICTED RIGHTS to the United States Government. Use, duplication, or disclosure of the Software by the United States Government is subject to the license terms of this Agreement pursuant to, as applicable, FAR 12.212, DFAR 227.7202-1(a), DFAR 227.7202-3(a), and DFAR 227.7202-4, and, to the extent required under U.S. -
AMSTATNEWS the Membership Magazine of the American Statistical Association •
June 2018 • Issue #492 AMSTATNEWS The Membership Magazine of the American Statistical Association • http://magazine.amstat.org See You in CINCINNATI! ALSO: Deans Offer Advice to Statistics Departments Boost Your Career in Washington Special Offer for all ASA Journal Subscribers Save 20% on books from the ASA-CRC Series on Statistical Reasoning in Science and Society with code PMA18, and review our latest journal issues Visualizing Baseball Jim Albert, Bowling Green State University, Ohio, USA ISBN: 9781498782753 _ $__2_9_._9_5__ _ $23.96 A collection of graphs will be used to explore the game of baseball. Graphical displays will be used to show how measures of batting and pitching performance have changed over time, to explore the career trajectories of players, to understand the importance of the pitch count, and to see the patterns of speed, movement, and location of different types of pitches. Errors, Blunders, and Lies How to Tell the Difference David S. Salsburg, Yale University, New Haven, CT, USA ISBN: 9781498795784 _ _$_2_9__.9__5_ _ $23.96 In this follow-up to the author's bestselling classic, "The Lady Tasting Tea", David Salsburg takes a fresh and insightful look at the history of statistical development by examining errors, blunders and outright lies in many different models taken from a variety of fields. S L A N R U O J Statistics and Public The American CHANCE Journal of the American Policy Statistician Statistical Association VOL 5, 2018 VOL 72, 2018 VOL 31, 2018 VOL 112, 2017 Attending JSM18? Visit us at booths 337, 436 -
Why the Resistance to Statistical Innovations? Bridging the Communication Gap
Psychological Methods © 2013 American Psychological Association 2013, Vol. 18, No. 4, 572–582 1082-989X/13/$12.00 DOI: 10.1037/a0034177 Why the Resistance to Statistical Innovations? Bridging the Communication Gap Donald Sharpe University of Regina While quantitative methodologists advance statistical theory and refine statistical methods, substantive researchers resist adopting many of these statistical innovations. Traditional explanations for this resistance are reviewed, specifically a lack of awareness of statistical developments, the failure of journal editors to mandate change, publish or perish pressures, the unavailability of user friendly software, inadequate education in statistics, and psychological factors. Resistance is reconsidered in light of the complexity of modern statistical methods and a communication gap between substantive researchers and quantitative methodologists. The concept of a Maven is introduced as a means to bridge the communi- cation gap. On the basis of this review and reconsideration, recommendations are made to improve communication of statistical innovations. Keywords: quantitative methods, resistance, knowledge transfer David Salsburg (2001) began the conclusion of his book, The quantitative methodologists. Instead, I argue there is much discon- Lady Tasting Tea: How Statistics Revolutionized Science in the tent. Twentieth Century, by formally acknowledging the statistical rev- A persistent irritation for some quantitative methodologists is olution and the dominance of statistics: “As we enter the twenty- substantive researchers’ overreliance on null hypothesis signifi- first century, the statistical revolution in science stands triumphant. cance testing (NHST). NHST is the confusing matrix of null and . It has become so widely used that its underlying assumptions alternative hypotheses, reject and fail to reject decision making, have become part of the unspoken popular culture of the Western and ubiquitous p values. -
Why the Father of Modern Statistics Didn't Believe Smoking Caused Cancer
Why the Father of Modern Statistics Didn’t Believe Smoking Caused Cancer By Ben Christopher · 49,340 views · More stats Ronald A. Fisher, father of modern statistics, enjoying his pipe. *** In the summer of 1957, Ronald Fisher, one of the fathers of modern statistics, sat down to write a strongly worded letter in defense of tobacco. The letter was addressed to the British Medical Journal, which, just a few weeks earlier, had taken the editorial position that smoking cigarettes causes lung cancer. According to the journal's editorial board, the time for amassing evidence and analyzing data was over. Now, they wrote, “all the modern devices of publicity” should be used to inform the public about the perils of tobacco. According to Fisher, this was nothing short of statistically illiterate fear mongering. Surely the danger posed to the smoking masses was “not the mild and soothing weed,” he wrote, “but the organized creation of states of frantic alarm.” 2016-Christopher-Fisher-Smoking-Cancer.docx Page 1 Fisher was a well-known hothead (and an inveterate pipe smoker), but the letter and the resulting debate, which lasted until his death in 1962, was taken as a serious critique by the scientific community. After all, Ronald A. Fisher had spent much of his career devising ways to mathematically evaluate causal claims—claims exactly like the one that the British Medical Journal was making about smoking and cancer. Along the way, he had revolutionized the way that biological scientists conduct experiments and analyze data. And yet we know how this debate ends. On one of the most significant public health questions of the 20th century, Fisher got it wrong. -
Research Methodology and Statistical Methods.Pmd
Research Methodology and Statistical Methods Research Methodology and Statistical Methods Morgan Shields www.edtechpress.co.uk Published by ED-Tech Press, 54 Sun Street, Waltham Abbey Essex, United Kingdom, EN9 1EJ © 2019 by ED-Tech Press Reprinted2020 Research Methodology and Statistical Methods Morgan Shields Includes bibliographical references and index. ISBN 978-1-78882-100-1 All rights reserved. No part of this publication may be reproduced, stored in retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without either the prior written permission of the publisher or a license permitting restricted copying in the United Kingdom issued by the Copyright Licensing Agency Ltd., Saffron House, 6-10 Kirby Street, London EC1N 8TS. Trademark Notice: All trademarks used herein are the property of their respective owners. The use of any trademark in this text does not vest in the author or publisher any trademark ownership rights in such trademarks, nor does the use of such trademarks imply any affiliation with or endorsement of this book by such owners. Unless otherwise indicated herein, any third-party trademarks that may appear in this work are the property of their respective owners and any references to third-party trademarks, logos or other trade dress are for demonstrative or descriptive purposes only. Such references are not intended to imply any sponsorship, endorsement, authorization, or promotion of ED-Tech products by the owners of such marks, or any relationship between the owner and ED-Tech Press or its affiliates, authors, licensees or distributors. British Library Cataloguing in Publication Data. -
Confounding Bias.Asdiscussedinchapter2, Observation Is Fallible: We Sometimes Think We See What Is Not in Fact There
A Clinician’s Guide to Statistics and Epidemiology in Mental Health A Clinician’s Guide to Statistics and Epidemiology in Mental Health Measuring Truth and Uncertainty S. Nassir Ghaemi MD MPH Professor of Psychiatry, Tufts University School of Medicine Director, Mood Disorders Program, Tufts Medical Center Boston, Massachusetts CAMBRIDGE UNIVERSITY PRESS Cambridge, New York, Melbourne, Madrid, Cape Town, Singapore, São Paulo, Delhi, Dubai, Tokyo Cambridge University Press The Edinburgh Building, Cambridge CB2 8RU, UK Published in the United States of America by Cambridge University Press, New York www.cambridge.org Information on this title: www.cambridge.org/9780521709583 © S. N. Ghaemi 2009 This publication is in copyright. Subject to statutory exception and to the provision of relevant collective licensing agreements, no reproduction of any part may take place without the written permission of Cambridge University Press. First published in print format 2009 ISBN-13 978-0-511-58093-2 eBook (NetLibrary) ISBN-13 978-0-521-70958-3 Paperback Cambridge University Press has no responsibility for the persistence or accuracy of urls for external or third-party internet websites referred to in this publication, and does not guarantee that any content on such websites is, or will remain, accurate or appropriate. Every effort has been made in preparing this publication to provide accurate and up-to-date information which is in accord with accepted standards and practice at the time of publication. Although case histories are drawn from actual cases, every effort has been made to disguise the identities of the individuals involved. Nevertheless, the authors, editors and publishers can make no warranties that the information contained herein is totally free from error, not least because clinical standards are constantly changing through research and regulation.