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Common Errors in Statistics, 2nd Edition
Comment from the Stata technical groupThis book contains common-sense, minimally technical advice about how to improve experimental design, analyze data, and present results. It provides 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, and 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. Two articles are reprinted in the two appendices. The first demonstrates that one can find highly significant R2 among totally independent normally distributed random variables; the second provides a partial solution to this conundrum. The second edition also offers new material devoted to unbalanced designs, report interpretation, and alternative modeling methods. Table of contents Preface
Part I Foundations
1 Sources of Error
Prescription
Fundamental Changes Ad Hoc, Post Hoc Hypotheses 2 Hypotheses: The Why of Your Research
Prescription
What Is a Hypothesis? How Precise Must a Hypothesis Be? Found Data Null Hypothesis Neyman–Pearson Theory Deduction and Induction Losses Decisions To Learn More 3 Collecting Data
Preparation
Measuring Devices Determining Sample Size Fundamental Assumptions Experimental Design Four Guidelines Are Experiments Really Necessary? To Learn More Part II Hypothesis Testing and Estimation
4 Estimation
Prevention
Desirable and Not-So-Desirable Estimators Interval Estimates Improved Results Summary To Learn More 5 Testing Hypotheses: Choosing a Test Statistic
Comparing Means of Two Populations
Comparing Variances Comparing the Means of K Samples Higher-Order Experimental Designs Contingency Tables Inferior Tests Multiple Tests Before You Draw Conclusions Summary To Learn More 6 Strengths and Limitations of Some Miscellaneous Statistical Procedures
Bootstrap
Bayesian Methodology Meta-Analysis Permutations Tests To Learn More 7 Reporting Your Results
Fundamentals
Tables Standard Error p-Values Confidence Values Recognizing and Reporting Biases Reporting Power Drawing Conclusions Summary To Learn More 8 Interpreting reports
With a Grain of Salt
Rates and Percentages Interpreting Computer Printouts 9 Graphics
The Soccer Data
Five Rules for Avoiding Bad Graphics One Rule for Correct Usage of Three-Dimensional Graphics The Misunderstood 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 III Building a Model
10 Univariate Regression
Model Selection
Estimating Coefficients Further Considerations Summary To Learn More 11 Alternative Methods of Regression
Linear vs. Nonlinear Regression
Least Absolute Deviation Regression Errors-in-Variables Regression Quantile Regression The Ecological Fallacy Nonsense Regression Summary To Learn More 12 Multivariable Regression
Caveats
Factor Analysis Generalized Linear Models Reporting Your Results A Conjecture Decision Trees Building a Successful Model To Learn More 13 Validation
Methods of Validation
Measures of Predictive Success Long-Term Stability To Learn More Appendix A
Appendix B
Glossary, Grouped by Related but Distinct Terms
Bibliography
Author index
Subject index
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