Includes bibliographical references (p. 195-205) and indexes.

1. The Basics. 1.1. Distinguish Randomized and Observational Studies. 1.2. Beware of Linear Models. 1.3. Understand Omnibus Quantities. 1.4. Independence, Equal Variance, and Normality. 1.5. Models As Simple As Possible, But Not More Simple. 1.6. Do Not Multiply Probabilities More Than Necessary. 1.7. Know the Sample Space for Statements of Risk. 1.8. Use Two-sided p-Values. 1.9. p-Values for Sample Size, Confidence Intervals for Results. 1.10. Use at Least Twelve Observations in Constructing a Confidence Interval. 1.11. Know the Unit of the Variable. 1.12. Know Properties Preserved When Transforming Units. 1.13. Be Flexible About Scale of Measurement Determining Analysis. 1.14. Be Eclectic and Ecumenical in Inference. 1.15. Consider Bootstrapping for Complex Relationships. 1.16. Standard Error from Sample Range/Sample Size -- 2. Sample Size. 2.1. Begin with a Basic Formula for Sample Size. 2.2. No Finite Population Correction for Survey Sample Size. 2.3. Calculating Sample Size Using the Coefficient of Variation. 2.4. Do Not Formulate a Study Solely in Terms of Effect Size. 2.5. Overlapping Confidence Intervals Do Not Imply Nonsignificance. 2.6. Sample Size Calculation for the Poisson Distribution. 2.7. Sample Size for Poisson With Background Rate. 2.8. Sample Size Calculation for the Binomial Distribution. 2.9. When Unequal Sample Sizes Matter; When They Don't. 2.10. Sample Size With Different Costs for the Two Samples. 2.11. The Rule of Threes for 95% Upper Bounds When There Are No Events. 2.12. Sample Size Calculations Are Determined by the Analysis -- 3. Covariation. 3.1. Assessing and Describing Covariation. 3.2. Don't Summarize Regression Sampling Schemes with Correlation. 3.3. Do Not Correlate Rates or Ratios Indiscriminately. 3.4. Determining Sample Size to Estimate a Correlation. 3.5. Pairing Data is Not Always Good. 3.6. Go Beyond Correlation in Drawing Conclusions. 3.7. Agreement As Accuracy, Scale Differential, and Precision. 3.8. Assess Test Reliability by Means of Agreement. 3.9. Range of the Predictor Variable and Regression. 3.10. Measuring Change: Width More Important than Numbers -- 4. Epidemiology. 4.1. Start with the Poisson to Model Incidence or Prevalence. 4.2. The Odds Ratio Approximates the Relative Risk Assuming the Disease is Rare. 4.3. The Number of Events is Crucial in Estimating Sample Sizes. 4.4. Using a Logarithmic Formulation to Calculate Sample Size. 4.5. Take No More than Four or Five Controls per Case. 4.6. Obtain at Least Ten Subjects for Every Variable Investigated. 4.7. Begin with the Exponential Distribution to Model Time to Event. 4.8. Begin with Two Exponentials for Comparing Survival Times. 4.9. Be Wary of Surrogates. 4.10. Prevalence Dominates in Screening Rare Diseases. 4.11. Do Not Dichotomize Unless Absolutely Necessary. 4.12. Select an Additive or Multiplicative Model on the Basis of Mechanism of Action -- 5. Environmental Studies. 5.1. Think Lognormal. 5.2. Begin with the Lognormal Distribution in Environmental Studies. 5.3. Differences Are More Symmetrical. 5.4. Beware of Pseudoreplication. 5.5. Think Beyond Simple Random Sampling. 5.6. Consider the Size of the Population Affected by Small Effects. 5.7. Statistical Models of Small Effects Are Very Sensitive to Assumptions. 5.8. Distinguish Between Variability and Uncertainty. 5.9. Description of the Database is As Important as Its Data. 5.10. Always Assess the Statistical Basis for an Environmental Standard. 5.11. Measurement of a Standard and Policy. 5.12. Parametric Analyses Make Maximum Use of the Data. 5.13. Distinguish Between Confidence, Prediction, and Tolerance Intervals. 5.14. Statistics Plays a Key Role in Risk Assessment, Less in Risk Management. 5.15. Exposure Assessment is the Weak Link in Assessing Health Effects of Pollutants. 5.16. Assess the Errors in Calibration Due to Inverse Regression -- 6. Design, Conduct, and Analysis. 6.1. Randomization Puts Systematic Effects into the Error Term. 6.2. Blocking is the Key to Reducing Variability. 6.3. Factorial Designs Should be Used to Assess Joint Effects of Variables. 6.4. High-Order Interactions Occur Rarely. 6.5. Balanced Designs Allow Easy Assessment of Joint Effects. 6.6. Analysis Follows Design. 6.7. Plan to Graph the Results of an Analysis. 6.8. Distinguish Between Design Structure and Treatment Structure. 6.9. Make Hierarchical Analyses the Default Analysis. 6.10. Distinguish Between Nested and Crossed Designs - Not Always Easy. 6.11. Plan for Missing Data. 6.12. Address Multiple Comparisons Before Starting the Study -- 7. Words, Tables, and Graphs. 7.1. Use Text for a Few Numbers, Tables for Many Numbers, Graphs for Complex Relationships. 7.2. Arrange Information in a Table to Drive Home the Message. 7.3. Always Graph the Data. 7.4. Never Use a Pie Chart. 7.5. Bargraphs Waste Ink; They Don't Illuminate Complex Relationships. 7.6. Stacked Bargraphs Are Worse Than Bargraphs. 7.7. Three-Dimensional Bargraphs Constitute Misdirected Artistry. 7.8. Identity Cross-sectional and Longitudinal Patterns in Longitudinal Data. 7.9. Use Rendering, Manipulation, and Linking in High Dimensional Data -- 8. Consulting. 8.1. Structure a Consultation Session to Have a Beginning, a Middle, and an End. 8.2. Ask Questions. 8.3. Make Distinctions. 8.4. Know Yourself, Know the Investigator. 8.5. Tailor Advice to the Level of the Investigator. 8.6. Use Units the Investigator is Comfortable With. 8.7. Agree on Assignment of Responsibilities. 8.8. Any Basic Statistical Computing Package Will Do. 8.9. Ethics Precedes, Guides, and Follows Consultation. 8.10. Be Proactive in Statistical Consulting. 8.11. Use the Web for Reference, Resource, and Education. 8.12. Listen to, and Heed the Advice of Experts in the Field.

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