Common Errors in Statistics (and How to Avoid Them) (ePub)

PREFACE xi PART I FOUNDATIONS 1 1 Sources of Error 3 Prescription, 4 Fundamental Concepts, 5 Ad Hoc, Post Hoc Hypotheses, 7 To Learn More, 11 2 Hypotheses: The Why of Your Research 13 Prescription, 13 What is a Hypothesis?, 14 Found Data, 16 Null Hypothesis, 16 Neyman-Pearson Theory, 17 Deduction and Induction, 21 Losses, 22 Decisions, 23 To Learn More, 25 3 Collecting Data 27 Preparation, 27 Response Variables, 28 Determining Sample Size, 32 Sequential Sampling, 36 One-Tail or Two?, 37 Fundamental Assumptions, 40 Experimental Design, 41 Four Guidelines, 43 Are Experiments Really Necessary?, 46 To Learn More, 47 PART II STATISTICAL ANALYSIS 49 4 Data Quality Assessment 51 Objectives, 52 Review the Sampling Design, 52 Data Review, 53 The Four-Plot, 55 To Learn More, 55 5 Estimation 57 Prevention, 57 Desirable and Not-So-Desirable Estimators, 57 Interval Estimates, 61 Improved Results, 65 Summary, 66 To Learn More, 66 6 Testing Hypotheses: Choosing a Test Statistic 67 First Steps, 68 Test Assumptions, 70 Binomial Trials, 71 Categorical Data, 72 Time-to-Event Data (Survival Analysis), 73 Comparing the Means of Two Sets of Measurements, 76 Comparing Variances, 85 Comparing the Means of k Samples, 89 Subjective Data, 91 Independence Versus Correlation, 91 Higher-Order Experimental Designs, 92 Inferior Tests, 96 Multiple Tests, 97 Before You Draw Conclusions, 97 Summary, 99 To Learn More, 99 7 Miscellaneous Statistical Procedures 101 Bootstrap, 102 Bayesian Methodology, 103 Meta-Analysis, 110 Permutation Tests, 112 To Learn More, 113 PART III REPORTS 115 8 Reporting Your Results 117 Fundamentals, 117 Descriptive Statistics, 122 Standard Error, 127 p-Values, 130 Confidence Intervals, 131 Recognizing and Reporting Biases, 133 Reporting Power, 135 Drawing Conclusions, 135 Summary, 136 To Learn More, 136 9 Interpreting Reports 139 With a Grain of Salt, 139 The Analysis, 141 Rates and Percentages, 145 Interpreting Computer Printouts, 146 To Learn More, 146 10 Graphics 149 The

PHILLIP I. GOOD, PhD, is Operations Manager of
Statcourse.com, a consulting firm specializing in statistical
solutions for industry. He has published more than thirty scholarly
works, more than six hundred popular articles, and twenty-one
books, including Introduction to Statistics Through Resampling
Methods and R/S-PLUS and Introduction to Statistics Through
Resampling Methods and Microsoft Office Excel, both published
by Wiley.

JAMES W. HARDIN, PhD, is Research Associate Professor and
Director of the Biostatistics Collaborative Unit at the University
of South Carolina.

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