Common Errors in Statistics (and How to Avoid Them), 3rd Edition
Authors:
|
Phillip I. Good and James W. Hardin |
| Publisher: |
Wiley |
| Copyright: |
2009 |
| ISBN-13: |
978-0-470-45798-6 |
| Pages: |
273; paperback |
| Price: |
$45.00 |
|
|
|
|
Comment from the Stata technical group
Common 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 contents
PART 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
|
){kind=link}
){kind=link}