{"product_id":"common-errors-in-statistics-and-how-to-avoid-them-isbn-9781118294390","title":"Common Errors in Statistics (and How to Avoid Them)","description":"\u003cp\u003ePraise for \u003ci\u003eCommon Errors in Statistics (and How to Avoid Them)\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\"A very engaging and valuable book for all who use statistics in any setting.\"\u003cbr\u003e \u003ci\u003e—CHOICE\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\"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.\"\u003cbr\u003e \u003ci\u003e—MAA Reviews\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eCommon Errors in Statistics (and How to Avoid Them), Fourth Edition\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003eProviding a consistent level of coherency throughout, the highly readable \u003ci\u003eFourth Edition\u003c\/i\u003e 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 \u003ci\u003eFourth Edition\u003c\/i\u003e features newly added topics, including:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eBaseline data\u003c\/li\u003e \u003cli\u003eDetecting fraud\u003c\/li\u003e \u003cli\u003eLinear regression versus linear behavior\u003c\/li\u003e \u003cli\u003eCase control studies\u003c\/li\u003e \u003cli\u003eMinimum reporting requirements\u003c\/li\u003e \u003cli\u003eNon-random samples\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThe book concludes with a glossary that outlines key terms, and an extensive bibliography with several hundred citations directing readers to resources for further study.\u003c\/p\u003e \u003cp\u003ePresented in an easy-to-follow style, \u003ci\u003eCommon Errors in Statistics, Fourth Edition\u003c\/i\u003e is an excellent book for students and professionals in industry, government, medicine, and the social sciences.\u003c\/p\u003e  \u003cp\u003ePreface xi\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART I FOUNDATIONS 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1. Sources of Error 3\u003c\/b\u003e\u003cbr\u003e Prescription 4\u003cbr\u003e Fundamental Concepts 5\u003cbr\u003e Surveys and Long-Term Studies 9\u003cbr\u003e Ad-Hoc, Post-Hoc Hypotheses 9\u003cbr\u003e To Learn More 13\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2. Hypotheses: The Why of Your Research 15\u003c\/b\u003e\u003cbr\u003e Prescription 15\u003cbr\u003e What Is a Hypothesis? 16\u003cbr\u003e How Precise Must a Hypothesis Be? 17\u003cbr\u003e Found Data 18\u003cbr\u003e Null or Nil Hypothesis 19\u003cbr\u003e Neyman–Pearson Theory 20\u003cbr\u003e Deduction and Induction 25\u003cbr\u003e Losses 26\u003cbr\u003e Decisions 27\u003cbr\u003e To Learn More 28\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3. Collecting Data 31\u003c\/b\u003e\u003cbr\u003e Preparation 31\u003cbr\u003e Response Variables 32\u003cbr\u003e Determining Sample Size 37\u003cbr\u003e Fundamental Assumptions 46\u003cbr\u003e Experimental Design 47\u003cbr\u003e Four Guidelines 49\u003cbr\u003e Are Experiments Really Necessary? 53\u003cbr\u003e To Learn More 54\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART II STATISTICAL ANALYSIS 57\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4. Data Quality Assessment 59\u003c\/b\u003e\u003cbr\u003e Objectives 60\u003cbr\u003e Review the Sampling Design 60\u003cbr\u003e Data Review 62\u003cbr\u003e To Learn More 63\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5. Estimation 65\u003c\/b\u003e\u003cbr\u003e Prevention 65\u003cbr\u003e Desirable and Not-So-Desirable Estimators 68\u003cbr\u003e Interval Estimates 72\u003cbr\u003e Improved Results 77\u003cbr\u003e Summary 78\u003cbr\u003e To Learn More 78\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6. Testing Hypotheses: Choosing a Test Statistic 79\u003c\/b\u003e\u003cbr\u003e First Steps 80\u003cbr\u003e Test Assumptions 82\u003cbr\u003e Binomial Trials 84\u003cbr\u003e Categorical Data 85\u003cbr\u003e Time-To-Event Data (Survival Analysis) 86\u003cbr\u003e Comparing the Means of Two Sets of Measurements 90\u003cbr\u003e Do Not Let Your Software Do Your Thinking For You 99\u003cbr\u003e Comparing Variances 100\u003cbr\u003e Comparing the Means of K Samples 105\u003cbr\u003e Higher-Order Experimental Designs 108\u003cbr\u003e Inferior Tests 113\u003cbr\u003e Multiple Tests 114\u003cbr\u003e Before You Draw Conclusions 115\u003cbr\u003e Induction 116\u003cbr\u003e Summary 117\u003cbr\u003e To Learn More 117\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7. Strengths and Limitations of Some Miscellaneous Statistical Procedures 119\u003c\/b\u003e\u003cbr\u003e Nonrandom Samples 119\u003cbr\u003e Modern Statistical Methods 120\u003cbr\u003e Bootstrap 121\u003cbr\u003e Bayesian Methodology 123\u003cbr\u003e Meta-Analysis 131\u003cbr\u003e Permutation Tests 135\u003cbr\u003e To Learn More 137\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8. Reporting Your Results 139\u003c\/b\u003e\u003cbr\u003e Fundamentals 139\u003cbr\u003e Descriptive Statistics 144\u003cbr\u003e Ordinal Data 149\u003cbr\u003e Tables 149\u003cbr\u003e Standard Error 151\u003cbr\u003e \u003ci\u003ep\u003c\/i\u003e-Values 155\u003cbr\u003e Confidence Intervals 156\u003cbr\u003e Recognizing and Reporting Biases 158\u003cbr\u003e Reporting Power 160\u003cbr\u003e Drawing Conclusions 160\u003cbr\u003e Publishing Statistical Theory 162\u003cbr\u003e A Slippery Slope 162\u003cbr\u003e Summary 163\u003cbr\u003e To Learn More 163\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9. Interpreting Reports 165\u003c\/b\u003e\u003cbr\u003e With a Grain of Salt 165\u003cbr\u003e The Authors 166\u003cbr\u003e Cost–Benefit Analysis 167\u003cbr\u003e The Samples 167\u003cbr\u003e Aggregating Data 168\u003cbr\u003e Experimental Design 168\u003cbr\u003e Descriptive Statistics 169\u003cbr\u003e The Analysis 169\u003cbr\u003e Correlation and Regression 171\u003cbr\u003e Graphics 171\u003cbr\u003e Conclusions 172\u003cbr\u003e Rates and Percentages 174\u003cbr\u003e Interpreting Computer Printouts 175\u003cbr\u003e Summary 178\u003cbr\u003e To Learn More 178\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10. Graphics 181\u003c\/b\u003e\u003cbr\u003e Is a Graph Really Necessary? 182\u003cbr\u003e KISS 182\u003cbr\u003e The Soccer Data 182\u003cbr\u003e Five Rules for Avoiding Bad Graphics 183\u003cbr\u003e One Rule for Correct Usage of Three-Dimensional Graphics 194\u003cbr\u003e The Misunderstood and Maligned Pie Chart 196\u003cbr\u003e Two Rules for Effective Display of Subgroup Information 198\u003cbr\u003e Two Rules for Text Elements in Graphics 201\u003cbr\u003e Multidimensional Displays 203\u003cbr\u003e Choosing Effective Display Elements 209\u003cbr\u003e Oral Presentations 209\u003cbr\u003e Summary 210\u003cbr\u003e To Learn More 211\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePART III BUILDING A MODEL 213\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11. Univariate Regression 215\u003c\/b\u003e\u003cbr\u003e Model Selection 215\u003cbr\u003e Stratification 222\u003cbr\u003e Further Considerations 226\u003cbr\u003e Summary 233\u003cbr\u003e To Learn More 234\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12. Alternate Methods of Regression 237\u003c\/b\u003e\u003cbr\u003e Linear Versus Nonlinear Regression 238\u003cbr\u003e Least-Absolute-Deviation Regression 238\u003cbr\u003e Quantile Regression 243\u003cbr\u003e Survival Analysis 245\u003cbr\u003e The Ecological Fallacy 246\u003cbr\u003e Nonsense Regression 248\u003cbr\u003e Reporting the Results 248\u003cbr\u003e Summary 248\u003cbr\u003e To Learn More 249\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13. Multivariable Regression 251\u003c\/b\u003e\u003cbr\u003e Caveats 251\u003cbr\u003e Dynamic Models 256\u003cbr\u003e Factor Analysis 256\u003cbr\u003e Reporting Your Results 258\u003cbr\u003e A Conjecture 260\u003cbr\u003e Decision Trees 261\u003cbr\u003e Building a Successful Model 264\u003cbr\u003e To Learn More 265\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14. Modeling Counts and Correlated Data 267\u003c\/b\u003e\u003cbr\u003e Counts 268\u003cbr\u003e Binomial Outcomes 268\u003cbr\u003e Common Sources of Error 269\u003cbr\u003e Panel Data 270\u003cbr\u003e Fixed- and Random-Effects Models 270\u003cbr\u003e Population-Averaged Generalized Estimating Equation Models (GEEs) 271\u003cbr\u003e Subject-Specific or Population-Averaged? 272\u003cbr\u003e Variance Estimation 272\u003cbr\u003e Quick Reference for Popular Panel Estimators 273\u003cbr\u003e To Learn More 275\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15. Validation 277\u003c\/b\u003e\u003cbr\u003e Objectives 277\u003cbr\u003e Methods of Validation 278\u003cbr\u003e Measures of Predictive Success 283\u003cbr\u003e To Learn More 285\u003c\/p\u003e \u003cp\u003e\u003cb\u003eGlossary 287\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eBibliography 291\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAuthor Index 319\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eSubject Index 329\u003c\/b\u003e\u003c\/p\u003e  \u003cp\u003e“Presented in an easy-to-follow style, this textbook is thought for students and professionals in industry, government, medicine, and the social sciences.”  (\u003ci\u003eZentralblatt MATH\u003c\/i\u003e, 1 December 2013)\u003c\/p\u003e  \u003cp\u003e\u003cb\u003ePHILLIP I. GOOD, PhD,\u003c\/b\u003e is Operations Manager at Information Research, a consulting firm specializing in statistical solutions for private and public organizations. He has published more than thirty scholarly works and more than 600 popular articles. Dr. Good is the author of \u003ci\u003eIntroduction to Statistics Through Resampling Methods and R\/S-PLUS®, Introduction to Statistics Through Resampling Methods and Microsoft Office Excel®,\u003c\/i\u003e and \u003ci\u003eAnalyzing the Large Number of Variables in Biomedical and Satellite Imagery,\u003c\/i\u003e all published by Wiley.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eJAMES W. HARDIN, PhD,\u003c\/b\u003e is Associate Professor and Biostatistics Division Director of the Department of Epidemiology and Biostatistics at the University of South Carolina. Dr. Hardin has published extensively in his areas of research interest, which include generalized linear models, generalized estimating equations, survival models, and computational statistics. He is also an affiliate faculty member of the Institute for Families in Society at the University of South Carolina.\u003c\/p\u003e  \u003cp\u003ePraise for \u003ci\u003eCommon Errors in Statistics (and How to Avoid Them)\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\"A very engaging and valuable book for all who use statistics in any setting.\"\u003cbr\u003e \u003ci\u003e—CHOICE\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\"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.\"\u003cbr\u003e \u003ci\u003e—MAA Reviews\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003ci\u003eCommon Errors in Statistics (and How to Avoid Them), Fourth Edition\u003c\/i\u003e 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.\u003c\/p\u003e \u003cp\u003eProviding a consistent level of coherency throughout, the highly readable \u003ci\u003eFourth Edition\u003c\/i\u003e 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 \u003ci\u003eFourth Edition\u003c\/i\u003e features newly added topics, including:\u003c\/p\u003e \u003cul\u003e \u003cli\u003eBaseline data\u003c\/li\u003e \u003cli\u003eDetecting fraud\u003c\/li\u003e \u003cli\u003eLinear regression versus linear behavior\u003c\/li\u003e \u003cli\u003eCase control studies\u003c\/li\u003e \u003cli\u003eMinimum reporting requirements\u003c\/li\u003e \u003cli\u003eNon-random samples\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eThe book concludes with a glossary that outlines key terms, and an extensive bibliography with several hundred citations directing readers to resources for further study.\u003c\/p\u003e \u003cp\u003ePresented in an easy-to-follow style, \u003ci\u003eCommon Errors in Statistics, Fourth Edition\u003c\/i\u003e is an excellent book for students and professionals in industry, government, medicine, and the social sciences.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47988950368485,"sku":"NP9781118294390","price":70.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118294390.jpg?v=1761782176","url":"https:\/\/k12savings.com\/products\/common-errors-in-statistics-and-how-to-avoid-them-isbn-9781118294390","provider":"K12savings","version":"1.0","type":"link"}