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Common Errors in Statistics (and How to Avoid Them), 3rd Edition
Comment from the Stata technical groupCommon Errors in Statistics (and How to Avoid Them), Third Edition, by Phillip I. Good and James W. Hardin, contains common sense, minimally technical advice about how to improve experimental design, analyze data, and present results. Good and Hardin provide a guide for experienced scientists, as well as for students learning to design and complete experiments and statistical analysis. The text begins with a discussion of foundations that covers sources of error, hypotheses, and data collection. The second section, on hypothesis testing and parameter estimation, takes a harder look at statistical evaluation of the data, including the strengths and limitations of various statistical procedures. The third section provides guidelines for reporting results, from what information to include to how to create an informative and easy-to-understand graph. The final section covers building a model, with topics on univariate and multivariable regression, as well as validation of the model chosen. Table of contentsPART I: FOUNDATIONS
1. Sources of Error
Prescription
Fundamental Concepts Ad Hoc, Post Hoc Hypotheses To Learn More 2. Hypotheses: The Why of Your Research
Prescription
What is a Hypothesis? Found Data Null Hypothesis Neyman–Pearson Theory Deduction and Induction Losses Decisions To Learn More 3. Collecting Data
Preparation
Response Variables Determining Sample Size Sequential Sampling One-Tail or Two? Fundamental Assumptions Experimental Design Four Guidelines Are Experiments Really Necessary? To Learn More PART II: STATISTICAL ANALYSIS
4. Data Quality Assessment
Objectives
Review the Sampling Design Data Review The Four-Plot To Learn More 5. Estimation
1. Prevention
2. Desirable and Not-So-Desirable Estimators 3. Interval Estimates 4. Improved Results 5. Summary 6. To Learn More 6. Testing Hypotheses: Choosing a Test Statistic
First Steps
Test Assumptions Binomial Trials Categorical Data Time-to-Event Data (Survival Analysis) Comparing the Means of Two Sets of Measurements Comparing Variances Comparing the Means of k Samples Subjective Data Independence Versus Correlation Higher-Order Experimental Designs Inferior Tests Multiple Tests Before You Draw Conclusions Summary To Learn More 7. Miscellaneous Statistical Procedures
Bootstrap
Bayesian Methodology Meta-Analysis Permutation Tests To Learn More PART III: REPORTS
8. Reporting Results
Fundamentals
Descriptive Statistics Standard Error p-Values Confidence Intervals Recognizing and Reporting Biases Reporting Power Drawing Conclusions Summary To Learn More 9. Interpreting Reports
With a Grain of Salt
The Analysis Rates and Percentages Interpreting Computer Printouts To Learn More 10. Graphics
The Soccer Data
Five Rules for Avoiding Bad Graphics One Rule for Correct Usage of Three-Dimensional Graphics The Misunderstood and Maligned Pie Chart Two Rules for Effective Display of Subgroup Information Two Rules for Text Elements in Graphics Multidimensional Displays Choosing Graphical Displays Summary To Learn More PART IV: BUILDING A MODEL
11. Univariate Regression
Model Selection
Stratification Estimating Coefficients Further Considerations Summary To Learn More 12. Alternate Methods of Regression
Linear Versus Non-Linear Regression
Least Absolute Deviation Regression Errors-in-Variables Regression Quantile Regression The Ecological Fallacy Nonsense Regression Summary To Learn More 13. Multivariable Regression
Caveats
Correcting for Confounding Variables Keep It Simple Dynamic Models Factor Analysis Reporting Your Results A Conjecture Decision Trees Building a Successful Model To Learn More 14. Modeling Correlated Data
Common Sources of Error
Panel Data Fixed- and Random-Effects Models Population-Averaged GEEs Quick Reference for Popular Panel Estimators To Learn More 15. Validation
Objectives
Methods of Validation Measures of Predictive Success Long-Term Stability To Learn More Glossary, Grouped by Related but Distinct Terms
Bibliography
Author Index
Subject Index
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