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[New York] : John Wiley & Sons, c2002

xviii,221 s. : il.

ISBN 0-471-40227-3 (brož.)

Wiley series in probability and statistics

Obsahuje ilustrace, tabulky, grafy, předmluvu, rejstříky, údaje o autorovi

Bibliografie: s. 195-205

Statistika matematická - pojednání

000101288

Contents // Preface xiii // Acronyms xvii // 1 The Basics 1 // 1.1 Distinguish Randomized and Observational Studies 2 // 1.2 Beware of Linear Models 3 // 1.3 Understand Omnibus Quantities 6 // 1.4 Independence, Equal Variance, and Normality 7 // 1.5 Models As Simple As Possible, But Not More Simple 11 // 1.6 Do Not Multiply Probabilities More Than Necessary 12 // 1.7 Know the Sample Space for Statements of Risk 13 // 1.8 Use Two-sided p-Values 14 // 1.9 p-Values for Sample Size, Confidence Intervals for // Results 16 // 1.10 Use at Least Twelve Observations in Constructing a // Confidence Interval 18 // 1.11 Know the Unit of the Variable 19 // 1.12 Know Properties Preserved When Transforming Units 20 // vii // ѴІІІ CONTENTS // 1.13 Be Flexible About Scale of Measurement Determining // Analysis 23 // 1.14 Be Eclectic and Ecumenical in Inference 24 // 1.15 Consider Bootstrapping for Complex Relationships 25 // 1.16 Standard Error from Sample Range/Sample Size 26 // 2 Sample Size 29 // 2.1 Begin with a Basic Formula for Sample Size 31 // 2.2 No Finite Population Correction for Survey Sample // Size 33 // 2.3 Calculating Sample Size Using the Coefficient of // Variation 35 // 2.4 Do Not Formulate a Study Solely in Terms of Effect // Size 38 // 2.5 Overlapping Confidence Intervals Do Not Imply // Nonsignificance 39 // 2.6 Sample Size Calculation for the Poisson Distribution 40 // 2.7 Sample Size for Poisson With Background Rate 41 // 2.8 Sample Size Calculation for the

Binomial Distribution 43 // 2.9 When Unequal Sample Sizes Matter; When They // Don’t 45 // 2.10 Sample Size With Different Costs for the Two Samples 47 // 2.11 The Rule of Threes for 95% Upper Bounds When // There Are No Events 49 // 2.12 Sample Size Calculations Are Determined by the // Analysis 50 // 3 Covariation 53 // 3.1 Assessing and Describing Covariation 55 // 3.2 Don’t Summarize Regression Sampling Schemes with // Correlation 56 // 3.3 Do Not Correlate Rates or Ratios Indiscriminately 58 // 3.4 Determining Sample Size to Estimate a Correlation 59 // 3.5 Pairing Data is not Always Good 61 // 3.6 Go Beyond Correlation in Drawing Conclusions 63 // CONTENTS ІХ // 3.7 Agreement As Accuracy, Scale Differential, and Precision 65 // 3.8 Assess Test Reliability by Means of Agreement 68 // 3.9 Range of the Predictor Variable and Regression 70 // 3.10 Measuring Change: Width More Important than // Numbers 72 // 4 Epidemiology 75 // 4.1 Start with the Poisson to Model Incidence or // Prevalence 76 // 4.2 The Odds Ratio Approximates the Relative Risk // Assuming the Disease is Rare 77 // 4.3 The Number of Events is Crucial in Estimating // Sample Sizes 82 // 4.4 Using a Logarithmic Formulation to Calculate Sample // Size 84 // 4.5 Take No More than Four or Five Controls per Case 86 // 4.6 Obtain at Least Ten Subjects for Every Variable // Investigated 87 // 4.7 Begin with the Exponential Distribution to Model // Time to Event 89 // 4.8 Begin with Two Exponentials

for Comparing Survival // Times 91 // 4.9 Be Wary of Surrogates 92 // 4.10 Prevalence Dominates in Screening Rare Diseases 95 // 4.11 Do Not Dichotomize Unless Absolutely Necessary 99 // 4.12 Select an Additive or Multiplicative Model on the // Basis of Mechanism of Action 100 // 5 Environmental Studies 103 // 5.1 Think Lognormal 103 // 5.2 Begin with the Lognormal Distribution in // Environmental Studies 104 // 5.3 Differences Are More Symmetrical 106 // 5.4 Beware of Pseudoreplication 108 // CONTENTS // 5.5 Think Beyond Simple Random Sampling 109 // 5.6 Consider the Size of the Population Affected by Small // Effects 111 // 5.7 Statistical Models of Small Effects Are Very Sensitive // to Assumptions 112 // 5.8 Distinguish Between Variability and Uncertainty 113 // 5.9 Description of the Database is As Important as Its Data 115 // 5.10 Always Assess the Statistical Basis for an // Environmental Standard 116 // 5.11 Measurement of a Standard and Policy 117 // 5.12 Parametric Analyses Make Maximum Use of the Data 119 // 5.13 Distinguish Between Confidence, Prediction, and // Tolerance Intervals 120 // 5.14 Statistics Plays a Key Role in Risk Assessment, Less // in Risk Management 122 // 5.15 Exposure Assessment is the Weak Link in Assessing // Health Effects of Pollutants 124 // 5.16 Assess the Errors in Calibration Due to Inverse // Regression 125 // Design, Conduct, and Analysis 129 // 6.1 Randomization Puts Systematic Effects into the Error // Term 129 // 6.2 Blocking is the

Key to Reducing Variability 131 // 6.3 Factorial Designs Should be Used to Assess Joint // Effects of Variables 132 // 6.4 High-Order Interactions Occur Rarely 134 // 6.5 Balanced Designs Allow Easy Assessment of Joint // Effects 136 // 6.6 Analysis Follows Design 137 // 6.7 Plan to Graph the Results of an Analysis 139 // 6.8 Distinguish Between Design Structure and Treatment // Structure 142 // 6.9 Make Hierarchical Analyses the Default Analysis 143 // CONTENTS xi // 6.10 Distinguish Between Nested and Crossed Designs- // Not Always Easy 145 // 6.11 Plan for Missing Data 146 // 6.12 Address Multiple Comparisons Before Starting the // Study 149 // Words, Tables, and Graphs 153 // 7.1 Use Text for a Few Numbers, Tables for Many // Numbers, Graphs for Complex Relationships 153 // 7.2 Arrange Information in a Table to Drive Home the // Message 155 // 7.3 Always Graph the Data 158 // 7.4 Never Use a Pie Chart 160 // 7.5 Bargraphs Waste Ink; They Don’t Illuminate Complex // Relationships 162 // 7.6 Stacked Bargraphs Are Worse Than Bargraphs 163 // 7.7 Three-Dimensional Bargraphs Constitute Misdirected // Artistry 166 // 7.8 Identify Cross-sectional and Longitudinal Patterns in // Longitudinal Data 167 // 7.9 Use Rendering, Manipulation, and Linking in High // Dimensional Data 170 // Consulting 175 // 8.1 Structure a Consultation Session to Have a Beginning, // a Middle, and an End 176 // 8.2 Ask Questions 177 // 8.3 Make Distinctions 178 // 8.4 Know Yourself, Know the Investigator

180 // 8.5 Tailor Advice to the Level of the Investigator 181 // 8.6 Use Units the Investigator is Comfortable With 182 // 8.7 Agree on Assignment of Responsibilities 184 // 8.8 Any Basic Statistical Computing Package Will Do 185 // 8.9 Ethics Precedes, Guides, and Follows Consultation 186 // 8.10 Be Proactive in Statistical Consulting 187 // xii CONTENTS // 8.11 Use the Web for Reference, Resource, and Education 189 // 8.12 Listen to, and Heed the Advice of Experts in the Field 190 // Epilogue 193 // References 195 // Author Index 207 // Topic Index // 211