Preface.
PART I FOUNDATIONS.
1. Sources of Error.
Prescription.
Fundamental Concepts.
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 NotSoDesirable 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.
HigherOrder 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.
MetaAnalysis.
Permutation Tests.
To Learn More.
7. Reporting Your Results.
Fundamentals.
Tables.
Standard Error.
pValues.
Confidence Intervals.
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 ThreeDimensional 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. Alternate Methods of Regression.
Linear vs. Nonlinear Regression.
Least Absolute Deviation Regression.
ErrorsinVariables Regression.
Quantile Regression.
The Ecological Fallacy.
Nonsense Regression.
Summary.
To Learn More.
12. Multivariable Regression.
Caveats.
Factor Analysis.
General Linearized Models.
Reporting Your Results.
A Conjecture.
Decision Trees.
Building a Successful Model.
To Learn More.
13. Validation.
Methods of Validation.
Measures of Predictive Success.
LongTerm Stability.
To Learn More.
Appendix A.
Appendix B.
Glossary, Grouped by Related but Distinct Terms.
Bibliography.
Author Index.
Subject Index.
