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Common Errors In Statistics And How To Avoid Them

Author: Phillip I. Good
Publisher: John Wiley & Sons
ISBN: 1118360117
Size: 40.77 MB
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Praise for Common Errors in Statistics (and How to Avoid Them) "A very engaging and valuable book for all who use statistics in any setting." —CHOICE "Addresses popular mistakes often made in data collection and provides an indispensable guide to accurate statistical analysis and reporting. The authors' emphasis on careful practice, combined with a focus on the development of solutions, reveals the true value of statistics when applied correctly in any area of research." —MAA Reviews Common Errors in Statistics (and How to Avoid Them), Fourth Edition provides a mathematically rigorous, yet readily accessible foundation in statistics for experienced readers as well as students learning to design and complete experiments, surveys, and clinical trials. Providing a consistent level of coherency throughout, the highly readable Fourth Edition focuses on debunking popular myths, analyzing common mistakes, and instructing readers on how to choose the appropriate statistical technique to address their specific task. The authors begin with an introduction to the main sources of error and provide techniques for avoiding them. Subsequent chapters outline key methods and practices for accurate analysis, reporting, and model building. The Fourth Edition features newly added topics, including: Baseline data Detecting fraud Linear regression versus linear behavior Case control studies Minimum reporting requirements Non-random samples The book concludes with a glossary that outlines key terms, and an extensive bibliography with several hundred citations directing readers to resources for further study. Presented in an easy-to-follow style, Common Errors in Statistics, Fourth Edition is an excellent book for students and professionals in industry, government, medicine, and the social sciences.

Common Errors In Statistics And How To Avoid Them

Author: Phillip I. Good
Publisher: John Wiley & Sons
ISBN: 0471998516
Size: 16.93 MB
Format: PDF, Kindle
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Praise for the First Edition of Common Errors in Statistics " . . . let me recommend Common Errors to all those who interact with statistics, whatever their level of statistical understanding . . . " --Stats 40 " . . . written . . . for the people who define good practice rather than seek to emulate it." --Journal of Biopharmaceutical Statistics " . . . highly informative, enjoyable to read, and of potential use to a broad audience. It is a book that should be on the reference shelf of many statisticians and researchers." --The American Statistician " . . . I found this book the most easily readable statistics book ever. The credit for this certainly goes to Phillip Good." --E-STREAMS A tried-and-true guide to the proper application of statistics Now in a second edition, the highly readable Common Errors in Statistics (and How to Avoid Them) lays a mathematically rigorous and readily accessible foundation for understanding statistical procedures, problems, and solutions. This handy field guide analyzes common mistakes, debunks popular myths, and helps readers to choose the best and most effective statistical technique for each of their tasks. Written for both the newly minted academic and the professional who uses statistics in their work, the book covers creating a research plan, formulating a hypothesis, specifying sample size, checking assumptions, interpreting p-values and confidence intervals, building a model, data mining, Bayes' Theorem, the bootstrap, and many other topics. The Second Edition has been extensively revised to include: * Additional charts and graphs * Two new chapters, Interpreting Reports and Which Regression Method? * New sections on practical versus statistical significance and nonuniqueness in multivariate regression * Added material from the authors' online courses at statistics.com * New material on unbalanced designs, report interpretation, and alternative modeling methods With a final emphasis on both finding solutions and the great value of statistics when applied in the proper context, this book is eminently useful to students and professionals in the fields of research, industry, medicine, and government.

Common Errors In Statistics And How To Avoid Them

Author: Phillip I. Good
Publisher: John Wiley & Sons
ISBN: 1118360133
Size: 32.49 MB
Format: PDF, ePub
View: 6665
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Praise for Common Errors in Statistics (and How to Avoid Them) "A very engaging and valuable book for all who use statistics in any setting." —CHOICE "Addresses popular mistakes often made in data collection and provides an indispensable guide to accurate statistical analysis and reporting. The authors' emphasis on careful practice, combined with a focus on the development of solutions, reveals the true value of statistics when applied correctly in any area of research." —MAA Reviews Common Errors in Statistics (and How to Avoid Them), Fourth Edition provides a mathematically rigorous, yet readily accessible foundation in statistics for experienced readers as well as students learning to design and complete experiments, surveys, and clinical trials. Providing a consistent level of coherency throughout, the highly readable Fourth Edition focuses on debunking popular myths, analyzing common mistakes, and instructing readers on how to choose the appropriate statistical technique to address their specific task. The authors begin with an introduction to the main sources of error and provide techniques for avoiding them. Subsequent chapters outline key methods and practices for accurate analysis, reporting, and model building. The Fourth Edition features newly added topics, including: Baseline data Detecting fraud Linear regression versus linear behavior Case control studies Minimum reporting requirements Non-random samples The book concludes with a glossary that outlines key terms, and an extensive bibliography with several hundred citations directing readers to resources for further study. Presented in an easy-to-follow style, Common Errors in Statistics, Fourth Edition is an excellent book for students and professionals in industry, government, medicine, and the social sciences.

Common Errors In Statistics And How To Avoid Them I Good Hardin 2003

Author: John Wiley & Sons, Inc
Publisher: Bukupedia
ISBN:
Size: 37.76 MB
Format: PDF
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ONE OF THE VERY FIRST STATISTICAL APPLICATIONS ON which Dr. Good worked was an analysis of leukemia cases in Hiroshima, Japan following World War II; on August 7, 1945 this city was the target site of the first atomic bomb dropped by the United States. Was the high incidence of leukemia cases among survivors the result of exposure to radiation from the atomic bomb? Was there a relationship between the number of leukemia cases and the number of survivors at certain distances from the atomic bomb’s epicenter? To assist in the analysis, Dr. Good had an electric (not an electronic) calculator, reams of paper on which to write down intermediate results, and a prepublication copy of Scheffe’s Analysis of Variance. The work took several months and the results were somewhat inconclusive, mainly because he could never seem to get the same answer twice—a consequence of errors in transcription rather than the absence of any actual relationship between radiation and leukemia. Today, of course, we have high-speed computers and prepackaged statistical routines to perform the necessary calculations. Yet, statistical software will no more make one a statistician than would a scalpel turn one into a neurosurgeon. Allowing these tools to do our thinking for us is a sure recipe for disaster. Pressed by management or the need for funding, too many research workers have no choice but to go forward with data analysis regardless of the extent of their statistical training. Alas, while a semester or two of undergraduate statistics may suffice to develop familiarity with the names of some statistical methods, it is not enough to be aware of all the circumstances under which these methods may be applicable. The purpose of the present text is to provide a mathematically rigorous but readily understandable foundation for statistical procedures. Here for the second time are such basic concepts in statistics as null and alternative Preface hypotheses, p value, significance level, and power. Assisted by reprints from the statistical literature, we reexamine sample selection, linear regression, the analysis of variance, maximum likelihood, Bayes’ Theorem, metaanalysis, and the bootstrap. Now the good news: Dr. Good’s articles on women’s sports have appeared in the San Francisco Examiner, Sports Now, and Volleyball Monthly. So, if you can read the sports page, you’ll find this text easy to read and to follow. Lest the statisticians among you believe this book is too introductory, we point out the existence of hundreds of citations in statistical literature calling for the comprehensive treatment we have provided. Regardless of past training or current specialization, this book will serve as a useful reference; you will find applications for the information contained herein whether you are a practicing statistician or a well-trained scientist who just happens to apply statistics in the pursuit of other science. The primary objective of the opening chapter is to describe the main sources of error and provide a preliminary prescription for avoiding them. The hypothesis formulation—data gathering—hypothesis testing and estimate cycle is introduced, and the rationale for gathering additional data before attempting to test after-the-fact hypotheses is detailed. Chapter 2 places our work in the context of decision theory. We emphasize the importance of providing an interpretation of each and every potential outcome in advance of consideration of actual data. Chapter 3 focuses on study design and data collection for failure at the planning stage can render all further efforts valueless. The work of Vance Berger and his colleagues on selection bias is given particular emphasis. Desirable features of point and interval estimates are detailed in Chapter 4 along with procedures for deriving estimates in a variety of practical situations. This chapter also serves to debunk several myths surrounding estimation procedures. Chapter 5 reexamines the assumptions underlying testing hypotheses. We review the impacts of violations of assumptions, and we detail the procedures to follow when making two- and k-sample comparisons. In addition, we cover the procedures for analyzing contingency tables and two-way experimental designs if standard assumptions are violated. Chapter 6 is devoted to the value and limitations of Bayes’ Theorem, meta-analysis, and resampling methods. Chapter 7 lists the essentials of any report that will utilize statistics, debunks the myth of the “standard” error, and describes the value and limitations of p values and confidence intervals for reporting results. Practical significance is distinguished from statistical significance, and induction is distinguished from deduction. x PREFACE Twelve rules for more effective graphic presentations are given in Chapter 8 along with numerous examples of the right and wrong ways to maintain reader interest while communicating essential statistical information. Chapters 9 through 11 are devoted to model building and to the assumptions and limitations of standard regression methods and data mining techniques. A distinction is drawn between goodness of fit and prediction, and the importance of model validation is emphasized. Seminal articles by David Freedman and Gail Gong are reprinted. Finally, for the further convenience of readers, we provide a glossary grouped by related but contrasting terms, a bibliography, and subject and author indexes. Our thanks to William Anderson, Leonardo Auslender, Vance Berger, Peter Bruce, Bernard Choi, Tony DuSoir, Cliff Lunneborg, Mona Hardin, Gunter Hartel, Fortunato Pesarin, Henrik Schmiediche, Marjorie Stinespring, and Peter A. Wright for their critical reviews of portions of this text. Doug Altman, Mark Hearnden, Elaine Hand, and David Parkhurst gave us a running start with their bibliographies. We hope you soon put this textbook to practical use. Phillip Good Huntington Beach, CA [email protected] James Hardin College Station, TX [email protected] PREFACE

Statistics Done Wrong

Author: Alex Reinhart
Publisher: No Starch Press
ISBN: 1593276206
Size: 63.84 MB
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Statistics Done Wrong describes how researchers often go wrong and teaches you the best practices for avoiding their mistakes.

Investment Mistakes Even Smart Investors Make And How To Avoid Them

Author: Larry Swedroe
Publisher: McGraw Hill Professional
ISBN: 007178683X
Size: 34.25 MB
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CBS MoneyWatch columnist Larry Swedroe’s bedrock principles for investing success Investment Mistakes Even Smart Investors Make and How to Avoid Them helps anyone from the novice investor to the professional money manager become a more informed investor—and ignore the kind of pervasive “conventional wisdom” that so often leads to financial loss. Swedroe describes how behavioral mistakes and overconfidence can lead you to stray from proven investment principles, and he explains how to reverse these temptations and make the right investing decisions when it counts most. Larry Swedroe is Principal and Director of Research at Buckingham Asset Management. He writes the popular blog “Wise Investing” at CBS MoneyWatch.com.

Translating Statistics To Make Decisions

Author: Victoria Cox
Publisher: Apress
ISBN: 1484222563
Size: 50.67 MB
Format: PDF
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Examine and solve the common misconceptions and fallacies that non-statisticians bring to their interpretation of statistical results. Explore the many pitfalls that non-statisticians—and also statisticians who present statistical reports to non-statisticians—must avoid if statistical results are to be correctly used for evidence-based business decision making. Victoria Cox, senior statistician at the United Kingdom’s Defence Science and Technology Laboratory (Dstl), distills the lessons of her long experience presenting the actionable results of complex statistical studies to users of widely varying statistical sophistication across many disciplines: from scientists, engineers, analysts, and information technologists to executives, military personnel, project managers, and officials across UK government departments, industry, academia, and international partners. The author shows how faulty statistical reasoning often undermines the utility of statistical results even among those with advanced technical training. Translating Statistics teaches statistically naive readers enough about statistical questions, methods, models, assumptions, and statements that they will be able to extract the practical message from statistical reports and better constrain what conclusions cannot be made from the results. To non-statisticians with some statistical training, this book offers brush-ups, reminders, and tips for the proper use of statistics and solutions to common errors. To fellow statisticians, the author demonstrates how to present statistical output to non-statisticians to ensure that the statistical results are correctly understood and properly applied to real-world tasks and decisions. The book avoids algebra and proofs, but it does supply code written in R for those readers who are motivated to work out examples. Pointing along the way to instructive examples of statistics gone awry, Translating Statistics walks readers through the typical course of a statistical study, progressing from the experimental design stage through the data collection process, exploratory data analysis, descriptive statistics, uncertainty, hypothesis testing, statistical modelling and multivariate methods, to graphs suitable for final presentation. The steady focus throughout the book is on how to turn the mathematical artefacts and specialist jargon that are second nature to statisticians into plain English for corporate customers and stakeholders. The final chapter neatly summarizes the book’s lessons and insights for accurately communicating statistical reports to the non-statisticians who commission and act on them. What You'll Learn Recognize and avoid common errors and misconceptions that cause statistical studies to be misinterpreted and misused by non-statisticians in organizational settings Gain a practical understanding of the methods, processes, capabilities, and caveats of statistical studies to improve the application of statistical data to business decisions See how to code statistical solutions in R Who This Book Is For Non-statisticians—including both those with and without an introductory statistics course under their belts—who consume statistical reports in organizational settings, and statisticians who seek guidance for reporting statistical studies to non-statisticians in ways that will be accurately understood and will inform sound business and technical decisions

Avoiding Common Anesthesia Errors

Author: Catherine Marcucci
Publisher: Lippincott Williams & Wilkins
ISBN: 1451178697
Size: 56.23 MB
Format: PDF, ePub, Docs
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This pocket book succinctly describes 215 common, serious errors made by attendings, residents, fellows, CRNAs, and practicing anesthesiologists in the practice of anesthesia and offers practical, easy-to-remember tips for avoiding these errors. The book can easily be read immediately before the start of a rotation or used for quick reference. Each error is described in a quick-reading one-page entry that includes a brief clinical scenario, a short review of the relevant physiology and/or pharmacology, and tips on how to avoid or resolve the problem. Illustrations are included where appropriate. The book also includes important chapters on human factors, legal issues, CPT coding, and how to select a practice.

What Is A P Value Anyway

Author: Andrew Vickers
Publisher: Addison-Wesley Longman
ISBN:
Size: 37.94 MB
Format: PDF
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What is a p-value Anyway? offers a fun introduction to the fundamental principles of statistics, presenting the essential concepts in thirty-four brief, enjoyable stories. Drawing on his experience as a medical researcher, Vickers blends insightful explanations and humor, with minimal math, to help readers understand and interpret the statistics they read every day. Describing data; Data distributions; Variation of study results: confidence intervals; Hypothesis testing; Regression and decision making; Some common statistical errors, and what they teach us For all readers interested in statistics.

Common Errors In English Usage Paul Brians 2008

Author: Paul Brians
Publisher: Bukupedia
ISBN:
Size: 55.86 MB
Format: PDF, Kindle
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What is an error in English? The concept of language errors is a fuzzy one. I'll leave to linguists the technical definitions. Here we're concerned only with deviations from the standard use of English as judged by sophisticated users such as professional writers, editors, teachers, and literate executives and personnel officers. The aim of this site is to help you avoid low grades, lost employment opportunities, lost business, and titters of amusement at the way you write or speak. But isn't one person's mistake another's standard usage? Often enough, but if your standard usage causes other people to consider you stupid or ignorant, you may want to consider changing it. You have the right to express yourself in any manner you please, but if you wish to communicate effectively you should use nonstandard English only when you intend to, rather than fall into it because you don't know any better. I'm learning English as a second language. Will this site help me improve my English? Very likely, though it's really aimed at the most common errors of native speakers. The errors others make in English differ according to the characteristics of their first languages. Speakers of other languages tend to make some specific errors that are uncommon among native speakers, so you may also want to consult sites dealing specifically with English as a second language (see http://www.cln.org/subjects/esl_cur.html and http://esl.about.com/education/adulted/esl/). There is also a Help Desk for ESL students at Washington State University at http://www.wsu.edu/~gordonl/ESL/. An outstanding book you may want to order is Ann Raimes' Keys for Writers. This is not a questionandanswer site for ESL. Aren't some of these points awfully picky? This is a relative matter. One person's gaffe is another's peccadillo. Some common complaints about usage strike me as too persnickety, but I'm just covering mistakes in English that happen to bother me. Feel free to create your own page listing your own pet peeves, but I welcome suggestions for additions to these pages.