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Common Errors in Statistics (and How to Avoid Them), Fourth Edition

Authors:
Phillip I. Good and James W. Hardin
Publisher: Wiley
Copyright: 2012
ISBN-13: 978-1-118-29439-0
Pages: 336; paperback
Price: $39.75

Comment from the Stata technical group

Common Errors in Statistics (and How to Avoid Them), Fourth Edition, by Phillip I. Good and James W. Hardin, contains a wealth of advice on how to improve experimental design, produce informative tables and graphs, and effectively analyze data. This book is not a treatise on statistical theory. Rather, it provides information on how to best apply that theory to real-world applications and obtain informative results. As the title implies, the book provides many examples of poorly executed analyses and then explains in detail how those examples can be improved.

The authors begin by discussing foundational issues of statistical analysis, including sources of error, data collection, and hypothesis formation. Chapter 2, on hypotheses, has been completely rewritten and now emphasizes the importance of formulating a null hypothesis and all the alternatives, including the conclusions that you would draw based on the outcome you later obtain. The chapter also discusses traditional Neyman–Pearson testing as well as decision making.

The second part of the book focuses on hypothesis testing and parameter estimation. Here the authors examine the statistical evaluation of the data as well as the strengths and limitations of various statistical procedures. Chapter 8, on how to report results, has been updated to reflect the strengths and weaknesses of p-values and confidence intervals as well as to show the important distinction between the statistical significance and the practical significance of results. Chapter 10 discusses how to make effective graphs and includes a list of 11 helpful rules to follow.

The last part of the book shows how to build a model, including linear and nonlinear regression, quantile regression, count models, and panel (longitudinal) data models. The final chapter discusses model validation.

The applied exposition in Common Errors in Statistics (and How to Avoid Them), Fourth Edition will be useful to experienced practitioners, and its many examples and careful explanations make it a helpful supplemental textbook for students of statistics.


Table of contents

PART I: FOUNDATIONS
1. Sources of Error
Prescription
Fundamental Concepts
Surveys and Long-Term Studies
Ad-Hoc, Post-Hoc Hypotheses
To Learn More
2. Hypotheses: The Why of Your Research
Prescription
What Is a Hypothesis?
How Precise Must a Hypothesis Be?
Found Data
Null or Nil Hypothesis
Neyman–Pearson Theory
Deduction and Induction
Losses
Decisions
To Learn More
3. Collecting Data
Preparation
Response Variables
Determining Sample Size
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
To Learn More
5. Estimation
Prevention
Desirable and Not-So-Desirable Estimators
Interval Estimates
Improved Results
Summary
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
Do Not Let Your Software Do Your Thinking For You
Comparing Variances
Comparing the Means of K Samples
Higher-Order Experimental Designs
Inferior Tests
Multiple Tests
Before You Draw Conclusions
Induction
Summary
To Learn More
7. Strengths and Limitations of Some Miscellaneous Statistical Procedures
Nonrandom Samples
Modern Statistical Methods
Bootstrap
Bayesian Methodology
Meta-Analysis
Permutation Tests
To Learn More
8. Reporting Your Results
Fundamentals
Descriptive Statistics
Ordinal Data
Tables
Standard Error
p-Values
Confidence Intervals
Recognizing and Reporting Biases
Reporting Power
Drawing Conclusions
Publishing Statistical Theory
A Slippery Slope
Summary
To Learn More
9. Interpreting Reports
With a Grain of Salt
The Authors
Cost–Benefit Analysis
The Samples
Aggregating Data
Experimental Design
Descriptive Statistics
The Analysis
Correlation and Regression
Graphics
Conclusions
Rates and Percentages
Interpreting Computer Printouts
Summary
To Learn More
10. Graphics
Is a Graph Really Necessary?
KISS
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 Effective Display Elements
Oral Presentations
Summary
To Learn More
PART III: BUILDING A MODEL
11. Univariate Regression
Model Selection
Stratification
Further Considerations
Summary
To Learn More
12. Alternate Methods of Regression
Linear Versus Nonlinear Regression
Least-Absolute-Deviation Regression
Quantile Regression
Survival Analysis
The Ecological Fallacy
Nonsense Regression
Reporting the Results
Summary
To Learn More
13. Multivariable Regression
Caveats
Dynamic Models
Factor Analysis
Reporting Your Results
A Conjecture
Decision Trees
Building a Successful Model
To Learn More
14. Modeling Counts and Correlated Data
Counts
Binomial Outcomes
Common Sources of Error
Panel Data
Fixed- and Random-Effects Models
Population-Averaged Generalized Estimating Equation Models (GEEs)
Subject-Specific or Population-Averaged?
Variance Estimation
Quick Reference for Popular Panel Estimators
To Learn More
15. Validation
Objectives
Methods of Validation
Measures of Predictive Success
To Learn More
Glossary
Bibliography
Author Index
Subject Index
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