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| Preface to the Second Edition | |
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| Preface to the First Edition | |
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| Acronyms | |
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| The Basics | |
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| Four Basic Questions | |
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| Observation is Selection | |
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| Replicate to Characterize Variability | |
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| Variability Occurs at Multiple Levels | |
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| Invalid Selection is the Primary Threat to Valid Inference | |
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| There is Variation in Strength of Inference | |
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| Distinguish Randomized and Observational Studies | |
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| Beware of Linear Models | |
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| Keep Models As Simple As Possible, But Not More Simple | |
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| Understand Omnibus Quantities | |
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| Do Not Multiply Probabilities More Than Necessary | |
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| Use Two-sided p-Values | |
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| p-Values for Sample Size, Confidence Intervals for Results | |
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| At Least Twelve Observations for a Confidence Interval | |
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| Estimate [plus or minus] Two Standard Errors is Remarkably Robust | |
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| Know the Unit of the Variable | |
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| Be Flexible About Scale of Measurement Determining Analysis | |
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| Be Eclectic and Ecumenical in Inference | |
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| Sample Size | |
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| Begin with a Basic Formula for Sample Size-Lehr's Equation | |
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| Calculating Sample Size Using the Coefficient of Variation | |
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| No Finite Population Correction for Survey Sample Size | |
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| Standard Deviation and Sample Range | |
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| Do Not Formulate a Study Solely in Terms of Effect Size | |
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| Overlapping Confidence Intervals Do Not Imply Nonsignificance | |
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| Sample Size Calculation for the Poisson Distribution | |
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| Sample Size for Poisson With Background Rate | |
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| Sample Size Calculation for the Binomial Distribution | |
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| When Unequal Sample Sizes Matter; When They Don't | |
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| Sample Size With Different Costs for the Two Samples | |
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| The Rule of Threes for 95% Upper Bounds When There Are No Events | |
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| Sample Size Calculations Are Determined by the Analysis | |
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| Observational Studies | |
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| The Model for an Observational Study is the Sample Survey | |
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| Large Sample Size Does Not Guarantee Validity | |
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| Good Observational Studies Are Designed | |
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| To Establish Cause Effect Requires Longitudinal Data | |
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| Make Theories Elaborate to Establish Cause and Effect | |
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| The Hill Guidelines Are a Useful Guide to Show Cause Effect | |
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| Sensitivity Analyses Assess Model Uncertainty and Missing Data | |
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| Covariation | |
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| Assessing and Describing Covariation | |
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| Don't Summarize Regression Sampling Schemes with Correlation | |
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| Do Not Correlate Rates or Ratios Indiscriminately | |
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| Determining Sample Size to Estimate a Correlation | |
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| Pairing Data is not Always Good | |
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| Go Beyond Correlation in Drawing Conclusions | |
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| Agreement As Accuracy, Scale Differential, and Precision | |
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| Assess Test Reliability by Means of Agreement | |
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| Range of the Predictor Variable and Regression | |
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| Measuring Change: Width More Important than Numbers | |
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| Environmental Studies | |
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| Begin with the Lognormal Distribution in Environmental Studies | |
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| Differences Are More Symmetrical | |
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| Know the Sample Space for Statements of Risk | |
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| Beware of Pseudoreplication | |
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| Think Beyond Simple Random Sampling | |
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| The Size of the Population and Small Effects | |
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| Models of Small Effects Are Sensitive to Assumptions | |
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| Distinguish Between Variability and Uncertainty | |
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| Description of the Database is As Important as Its Data | |
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| Always Assess the Statistical Basis for an Environmental Standard | |
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| Measurement of a Standard and Policy | |
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| Parametric Analyses Make Maximum Use of the Data | |
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| Confidence, Prediction, and Tolerance Intervals | |
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| Statistics and Risk Assessment | |
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| Exposure Assessment is the Weak Link in Assessing Health Effects of Pollutants | |
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| Assess the Errors in Calibration Due to Inverse Regression | |
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| Epidemiology | |
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| Start with the Poisson to Model Incidence or Prevalence | |
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| The Odds Ratio Approximates the Relative Risk Assuming the Disease is Rare | |
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| The Number of Events is Crucial in Estimating Sample Sizes | |
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| Use a Logarithmic Formulation to Calculate Sample Size | |
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| Take No More than Four or Five Controls per Case | |
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| Obtain at Least Ten Subjects for Every Variable Investigated | |
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| Begin with the Exponential Distribution to Model Time to Event | |
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| Begin with Two Exponentials for Comparing Survival Times | |
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| Be Wary of Surrogates | |
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| Prevalence Dominates in Screening Rare Diseases | |
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| Do Not Dichotomize Unless Absolutely Necessary | |
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| Additive and Multiplicative Models | |
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| Evidence-Based Medicine | |
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| Strength of Evidence | |
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| Relevance of Information: POEM vs. DOE | |
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| Begin with Absolute Risk Reduction, then Follow with Relative Risk | |
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| The Number Needed to Treat (NNT) is Clinically Useful | |
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| Variability in Response to Treatment Needs to be Considered | |
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| Safety is the Weak Component of EBM | |
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| Intent to Treat is the Default Analysis | |
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| Use Prior Information but not Priors | |
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| The Four Key Questions for Meta-analysts | |
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| Design, Conduct, and Analysis | |
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| Randomization Puts Systematic Effects into the Error Term | |
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| Blocking is the Key to Reducing Variability | |
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| Factorial Designs and Joint Effects | |
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| High-Order Interactions Occur Rarely | |
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| Balanced Designs Allow Easy Assessment of Joint Effects | |
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| Analysis Follows Design | |
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| Independence, Equal Variance, and Normality | |
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| Plan to Graph the Results of an Analysis | |
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| Distinguish Between Design Structure and Treatment Structure | |
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| Make Hierarchical Analyses the Default Analysis | |
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| Distinguish Between Nested and Crossed Designs-Not Always Easy | |
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| Plan for Missing Data | |
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| Address Multiple Comparisons Before Starting the Study | |
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| Know Properties Preserved When Transforming Units | |
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| Consider Bootstrapping for Complex Relationships | |
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| Words, Tables, and Graphs | |
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| Use Text for a Few Numbers, Tables for Many Numbers, Graphs for Complex Relationships | |
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| Arrange Information in a Table to Drive Home the Message | |
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| Always Graph the Data | |
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| Always Graph Results of an Analysis of Variance | |
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| Never Use a Pie Chart | |
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| Bar Graphs Waste Ink; They Don't Illuminate Complex Relationships | |
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| Stacked Bar Graphs Are Worse Than Bar Graphs | |
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| Three-Dimensional Bar Graphs Constitute Misdirected Artistry | |
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| Identify Cross-sectional and Longitudinal Patterns in Longitudinal Data | |
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| Use Rendering, Manipulation, and Linking in High-Dimensional Data | |
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| Consulting | |
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| Session Has Beginning, Middle, and End | |
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| Ask Questions | |
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| Make Distinctions | |
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| Know Yourself, Know the Investigator | |
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| Tailor Advice to the Level of the Investigator | |
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| Use Units the Investigator is Comfortable With | |
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| Agree on Assignment of Responsibilities | |
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| Any Basic Statistical Computing Package Will Do | |
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| Ethics Precedes, Guides, and Follows Consultation | |
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| Be Proactive in Statistical Consulting | |
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| Use the Web for Reference, Resource, and Education | |
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| Listen to, and Heed the Advice of Experts in the Field | |
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| Epilogue | |
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| References | |
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| Author Index | |
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| Topic Index | |