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Common Errors in Statistics (and How to Avoid Them), 4th Edition

Book Description

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.

Table of Contents

  1. Cover
  2. Title page
  3. Copyright page
  4. Preface
  5. Part I: FOUNDATIONS
    1. Chapter 1 Sources of Error
      1. PRESCRIPTION
      2. FUNDAMENTAL CONCEPTS
      3. SURVEYS AND LONG-TERM STUDIES
      4. AD-HOC, POST-HOC HYPOTHESES
      5. TO LEARN MORE
    2. Chapter 2 Hypotheses: The Why of Your Research
      1. PRESCRIPTION
      2. WHAT IS A HYPOTHESIS?
      3. HOW PRECISE MUST A HYPOTHESIS BE?
      4. FOUND DATA
      5. NULL OR NIL HYPOTHESIS
      6. NEYMAN–PEARSON THEORY
      7. DEDUCTION AND INDUCTION
      8. LOSSES
      9. DECISIONS
      10. TO LEARN MORE
    3. Chapter 3 Collecting Data
      1. PREPARATION
      2. RESPONSE VARIABLES
      3. DETERMINING SAMPLE SIZE
      4. FUNDAMENTAL ASSUMPTIONS
      5. EXPERIMENTAL DESIGN
      6. FOUR GUIDELINES
      7. ARE EXPERIMENTS REALLY NECESSARY?
      8. TO LEARN MORE
  6. Part II: STATISTICAL ANALYSIS
    1. Chapter 4 Data Quality Assessment
      1. OBJECTIVES
      2. REVIEW THE SAMPLING DESIGN
      3. DATA REVIEW
      4. TO LEARN MORE
    2. Chapter 5 Estimation
      1. PREVENTION
      2. DESIRABLE AND NOT-SO-DESIRABLE ESTIMATORS
      3. INTERVAL ESTIMATES
      4. IMPROVED RESULTS
      5. SUMMARY
      6. TO LEARN MORE
    3. Chapter 6 Testing Hypotheses: Choosing a Test Statistic
      1. FIRST STEPS
      2. TEST ASSUMPTIONS
      3. BINOMIAL TRIALS
      4. CATEGORICAL DATA
      5. TIME-TO-EVENT DATA (SURVIVAL ANALYSIS)
      6. COMPARING THE MEANS OF TWO SETS OF MEASUREMENTS
      7. DO NOT LET YOUR SOFTWARE DO YOUR THINKING FOR YOU
      8. COMPARING VARIANCES
      9. COMPARING THE MEANS OF K SAMPLES
      10. HIGHER-ORDER EXPERIMENTAL DESIGNS
      11. INFERIOR TESTS
      12. MULTIPLE TESTS
      13. BEFORE YOU DRAW CONCLUSIONS
      14. INDUCTION
      15. SUMMARY
      16. TO LEARN MORE
    4. Chapter 7 Strengths and Limitations of Some Miscellaneous Statistical Procedures
      1. NONRANDOM SAMPLES
      2. MODERN STATISTICAL METHODS
      3. BOOTSTRAP
      4. BAYESIAN METHODOLOGY
      5. META-ANALYSIS
      6. PERMUTATION TESTS
      7. TO LEARN MORE
    5. Chapter 8 Reporting Your Results
      1. FUNDAMENTALS
      2. DESCRIPTIVE STATISTICS
      3. ORDINAL DATA
      4. TABLES
      5. STANDARD ERROR
      6. P-VALUES
      7. CONFIDENCE INTERVALS
      8. RECOGNIZING AND REPORTING BIASES
      9. REPORTING POWER
      10. DRAWING CONCLUSIONS
      11. PUBLISHING STATISTICAL THEORY
      12. A SLIPPERY SLOPE
      13. SUMMARY
      14. TO LEARN MORE
    6. Chapter 9 Interpreting Reports
      1. WITH A GRAIN OF SALT
      2. THE AUTHORS
      3. COST–BENEFIT ANALYSIS
      4. THE SAMPLES
      5. AGGREGATING DATA
      6. EXPERIMENTAL DESIGN
      7. DESCRIPTIVE STATISTICS
      8. THE ANALYSIS
      9. CORRELATION AND REGRESSION
      10. GRAPHICS
      11. CONCLUSIONS
      12. RATES AND PERCENTAGES
      13. INTERPRETING COMPUTER PRINTOUTS
      14. SUMMARY
      15. TO LEARN MORE
    7. Chapter 10 Graphics
      1. IS A GRAPH REALLY NECESSARY?
      2. KISS
      3. THE SOCCER DATA
      4. FIVE RULES FOR AVOIDING BAD GRAPHICS
      5. ONE RULE FOR CORRECT USAGE OF THREE-DIMENSIONAL GRAPHICS
      6. THE MISUNDERSTOOD AND MALIGNED PIE CHART
      7. TWO RULES FOR EFFECTIVE DISPLAY OF SUBGROUP INFORMATION
      8. TWO RULES FOR TEXT ELEMENTS IN GRAPHICS
      9. MULTIDIMENSIONAL DISPLAYS
      10. CHOOSING EFFECTIVE DISPLAY ELEMENTS
      11. ORAL PRESENTATIONS
      12. SUMMARY
      13. TO LEARN MORE
  7. Part III: BUILDING A MODEL
    1. Chapter 11 Univariate Regression
      1. MODEL SELECTION
      2. STRATIFICATION
      3. FURTHER CONSIDERATIONS
      4. SUMMARY
      5. TO LEARN MORE
    2. Chapter 12 Alternate Methods of Regression
      1. LINEAR VERSUS NONLINEAR REGRESSION
      2. LEAST-ABSOLUTE-DEVIATION REGRESSION
      3. QUANTILE REGRESSION
      4. SURVIVAL ANALYSIS
      5. THE ECOLOGICAL FALLACY
      6. NONSENSE REGRESSION
      7. REPORTING THE RESULTS
      8. SUMMARY
      9. TO LEARN MORE
    3. Chapter 13 Multivariable Regression
      1. CAVEATS
      2. DYNAMIC MODELS
      3. FACTOR ANALYSIS
      4. REPORTING YOUR RESULTS
      5. A CONJECTURE
      6. DECISION TREES
      7. BUILDING A SUCCESSFUL MODEL
      8. TO LEARN MORE
    4. Chapter 14 Modeling Counts and Correlated Data
      1. COUNTS
      2. BINOMIAL OUTCOMES
      3. COMMON SOURCES OF ERROR
      4. PANEL DATA
      5. FIXED- AND RANDOM-EFFECTS MODELS
      6. POPULATION-AVERAGED GENERALIZED ESTIMATING EQUATION MODELS (GEEs)
      7. SUBJECT-SPECIFIC OR POPULATION-AVERAGED?
      8. VARIANCE ESTIMATION
      9. QUICK REFERENCE FOR POPULAR PANEL ESTIMATORS
      10. TO LEARN MORE
    5. Chapter 15 Validation
      1. OBJECTIVES
      2. METHODS OF VALIDATION
      3. MEASURES OF PREDICTIVE SUCCESS
      4. TO LEARN MORE
  8. Glossary
  9. Bibliography
  10. Author Index
  11. Subject Index