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Summary

Praise for the Second Edition:"All statistics students and teachers will find in this book a friendly and intelligentguide to . . . applied statistics in practice." -Journal of Applied Statistics". . . a very engaging and valuable book for all who use statistics in any setting." -CHOICE". . . a concise guide to the basics of statistics, replete with examples . . . a valuablereference for more advanced statisticians as well." -MAA ReviewsNow in its Third Edition, the highly readable Common Errors in Statistics (and How to Avoid Them) continues to serve as a thorough and straightforward discussion of basic statistical methods, presentations, approaches, and modeling techniques. Further enriched with new examples and counterexamples from the latest research as well as added coverage of relevant topics, this new edition of the benchmark book addresses popular mistakes often made in data collection and provides an indispensable guide to accurate statistical analysis and reporting. The authors' emphasis on careful practice, combined with a focus on the development of solutions, reveals the true value of statistics when applied correctly in any area of research.The Third Edition has been considerably expanded and revised to include:A new chapter on data quality assessmentA new chapter on correlated dataAn expanded chapter on data analysis covering categorical and ordinal data, continuous measurements, and time-to-event data, including sections on factorial and crossover designsRevamped exercises with a stronger emphasis on solutionsAn extended chapter on report preparationNew sections on factor analysis as well as Poisson and negative binomial regressionProviding valuable, up-to-date information in the same user-friendly format as its predecessor, Common Errors in Statistics (and How to Avoid Them), Third Edition is an excellent book for students and professionals in industry, government, medicine, and the social sciences.

Preface
Foundations
Sources of Error
Prescription
Fundamental Concepts
Ad-hoc, post-hoc hypotheses
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
Collecting Data
Preparation
Response Variables
Determining Sample Size
Fundamental Assumptions
Experimental Design
Four Guidelines
Are Experiments Really Necessary?
To Learn More
Statistical Analysis
Data Quality Assessment
GIGO
Objectives
Design Review
Data Review
Estimation
Prevention
Desirable and Not-so-desirable estimators
Interval Estimates
Improved Results
Summary
To Learn More
Testing Hypotheses: Choosing a Test Statistic
First Steps
Test Assumptions
Binomial Trials
Categorical Data
Time to Event Data (survival analysis)
Comparing Means of Two Sets of Measurements
Multivariate comparisons
Options
Testing equivalence
Unequal variances
Dependent observations
Comparing Variances
Comparing the Means of K Samples
Subjective Data
Independence vs. Correlation
Higher Order Experimental Designs
Errors in interpretation
Multi-factor designs
Cross-over designs
Factorial designs
Unbalanced designs
Inferior Tests
Multiple Tests
Summary
To Learn More
Miscellaneous Statistical Procedures
Bootstrap
Bayesian Methodology
Meta-Analysis
Permutation Tests
To Learn More
Reports
Reporting Results
Fundamentals
Treatment Allocation
Adequacy of Blinding
Missing Data
Descriptive Statistics
Binomial trials
Categorical data
Rare events
Measurements
Which mean?
Ordinal data
Tables
Dispersion, precision, and accuracy
Standard Error
p-values
Confidence Intervals
Recognizing and Reporting Biases
Reporting Power
Drawing Conclusions
Summary
To Learn More
Interpreting Reports
With A Grain of Salt
Rates and Percentages
Interpreting Computer Printouts
Graphics
Five Rules for Avoiding Bad Graphics
Displaying the 3rd Dimension
The Misunderstood Pie Chart
Effective Display of Subgroup Information
Two Rules for Text Elements
Choosing The Right Display
To Learn More
Building A Model
Univariate Regression
Model Selection
Scope
Ambiguous relationships
Confounding variables
Estimating Coefficients
Further Considerations
Bad data
Practical v. statistical significance
Goodness-of-fit v. prediction
Indicator variables
Transformations
When a straight line won't do
Curve fitting and Magic Beans
Summary
Checklist
To Learn More
Alternate Modeling Methods
LAD Regression
Demming or EIV Regression
Quantile Regression
The Ecological Fallacy
Poisson and Negative Binomial Regression
Nonsense Regression
Summary
To Learn More
Multivariate 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
Modeling Correlated Data
Common Sources of Error
Panel Data
Fixed and Random Effects Models
GEE's
Subject-specific or population averaged?
Variance estimation
Quick Reference for Popular Panel Estimators
To Learn More
Validation
Objectives
Methods of Validation
Independent verification
Split sample
Resampling
Measures of Predictive Success
Long Term Stability
To Learn More



Glossary
Bibliography
Author Index
Subject Index
Table of Contents provided by Publisher. All Rights Reserved.

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