Statistical Rules of Thumb,9780471402275

Statistical Rules of Thumb

by
Edition: 32nd
ISBN13: 9780471402275
ISBN10: 0471402273
Format: Paperback
Pub. Date: 2002-03-01
Publisher(s): Wiley-Interscience
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List Price: $83.95

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Summary

This book provides simple statistical rules of thumb that are widely applicable, robust, and elegant, and that capture key statistical concepts. This useful handbook provides a framework for considering statistical questions such as sample size and design of experiments.

Author Biography

Gerald van Belle is a professor in the departments of Biostatistics and Environmental Health at the University of Washington in Seattle, WA. He is coauthor with Lloyd Fisher of Biostatistics: A Methodology for the Health Sciences (also from Wiley).

Table of Contents

Preface xiii
Acronyms xvii
The Basics
1(28)
Distinguish Randomized and Observational Studies
2(1)
Beware of Linear Models
3(3)
Understand Omnibus Quantities
6(1)
Independence, Equal Variance, and Normality
7(4)
Models As Simple As Possible, But Not More Simple
11(1)
Do Not Multiply Probabilities More Than Necessary
12(1)
Know the Sample Space for Statements of Risk
13(1)
Use Two-sided p-Values
14(2)
p-Values for Sample Size, Confidence Intervals for Results
16(2)
Use at Least Twelve Observations in Constructing a Confidence Interval
18(1)
Know the Unit of the Variable
19(1)
Know Properties Preserved When Transforming Units
20(3)
Be Flexible About Scale of Measurement Determining Analysis
23(1)
Be Eclectic and Ecumenical in Inference
24(1)
Consider Bootstrapping for Complex Relationships
25(1)
Standard Error from Sample Range/Sample Size
26(3)
Sample Size
29(24)
Begin with a Basic Formula for Sample Size
31(2)
No Finite Population Correction for Survey Sample Size
33(2)
Calculating Sample Size Using the Coefficient of Variation
35(3)
Do Not Formulate a Study Solely in Terms of Effect Size
38(1)
Overlapping Confidence Intervals Do Not Imply Nonsignificance
39(1)
Sample Size Calculation for the Poisson Distribution
40(1)
Sample Size for Poisson With Background Rate
41(2)
Sample Size Calculation for the Binomial Distribution
43(2)
When Unequal Sample Sizes Matter; When They Don't
45(2)
Sample Size With Different Costs for the Two Samples
47(2)
The Rule of Threes for 95% Upper Bounds When There Are No Events
49(1)
Sample Size Calculations Are Determined by the Analysis
50(3)
Covariation
53(22)
Assessing and Describing Covariation
55(1)
Don't Summarize Regression Sampling Schemes with Correlation
56(2)
Do Not Correlate Rates or Ratios Indiscriminately
58(1)
Determining Sample Size to Estimate a Correlation
59(2)
Pairing Data is not Always Good
61(2)
Go Beyond Correlation in Drawing Conclusions
63(2)
Agreement As Accuracy, Scale. Differential, and Precision
65(3)
Assess Test Reliability by Means of Agreement
68(2)
Range of the Predictor Variable and Regression
70(2)
Measuring Change: Width More Important than Numbers
72(3)
Epidemiology
75(28)
Start with the Poisson to Model Incidence or Prevalence
76(1)
The Odds Ratio Approximates the Relative Risk Assuming the Disease is Rare
77(5)
The Number of Events is Crucial in Estimating Sample Sizes
82(2)
Using a Logarithmic Formulation to Calculate Sample Size
84(2)
Take No More than Four or Five Controls per Case
86(1)
Obtain at Least Ten Subjects for Every Variable Investigated
87(2)
Begin with the Exponential Distribution to Model Time to Event
89(2)
Begin with Two Exponentials for Comparing Survival Times
91(1)
Be Wary of Surrogates
92(3)
Prevalence Dominates in Screening Rare Diseases
95(4)
I Do Not Dichotomize Unless Absolutely Necessary
99(1)
Select an Additive or Multiplicative Model on the Basis of Mechanism of Action
100(3)
Environmental Studies
103(26)
Think Lognormal
103(1)
Begin with the Lognormal Distribution in Environmental Studies
104(2)
Differences Are More Symmetrical
106(2)
Beware of Pseudoreplication
108(1)
Think Beyond Simple Random Sampling
109(2)
Consider the Size of the Population Affected by Small Effects
111(1)
Statistical Models of Small Effects Are Very Sensitive to Assumptions
112(1)
Distinguish Between Variability and Uncertainty
113(2)
Description of the Database is As Important as Its Data
115(1)
Always Assess the Statistical Basis for an Environmental Standard
116(1)
Measurement of a Standard and Policy
117(2)
Parametric Analyses Make Maximum Use of the Data
119(1)
Distinguish Between Confidence, Prediction, and Tolerance Intervals
120(2)
Statistics Plays a Key Role in Risk Assessment, Less in Risk Management
122(2)
Exposure Assessment is the Weak Link in Assessing Health Effects of Pollutants
124(1)
Assess the Errors in Calibration Due to Inverse Regression
125(4)
Design, Conduct, and Analysis
129(24)
Randomization Puts Systematic Effects into the Error Term
129(2)
Blocking is the Key to Reducing Variability
131(1)
Factorial Designs Should be Used to Assess Joint Effects of Variables
132(2)
High-Order Interactions Occur Rarely
134(2)
Balanced Designs Allow Easy Assessment of Joint Effects
136(1)
Analysis Follows Design
137(2)
Plan to Graph the Results of an Analysis
139(3)
Distinguish Between Design Structure and Treatment Structure
142(1)
Make Hierarchical Analyses the Default Analysis
143(2)
Distinguish Between Nested and Crossed Designs-Not Always Easy
145(1)
Plan for Missing Data
146(3)
Address Multiple Comparisons Before Starting the Study
149(4)
Words, Tables, and Graphs
153(22)
Use Text for a Few Numbers, Tables for Many Numbers, Graphs for Complex Relationships
153(2)
Arrange Information in a Table to Drive Home the Message
155(3)
Always Graph the Data
158(2)
Never Use a Pie Chart
160(2)
Bargraphs Waste Ink; They Don't Illuminate Complex Relationships
162(1)
Stacked Bargraphs Are Worse Than Bargraphs
163(3)
Three-Dimensional Bargraphs Constitute Misdirected Artistry
166(1)
Identify Cross-sectional and Longitudinal Patterns in Longitudinal Data
167(3)
Use Rendering, Manipulation, and Linking in High Dimensional Data
170(5)
Consulting
175(18)
Structure a Consultation Session to Have a Beginning a Middle, and an End
176(1)
Ask Questions
177(1)
Make Distinctions
178(2)
Know Yourself, Know the Investigator
180(1)
Tailor Advice to the Level of the Investigator
181(1)
Use Units the Investigator is Comfortable With
182(2)
Agree on Assignment of Responsibilities
184(1)
Any Basic Statistical Computing Package Will Do
185(1)
Ethics Precedes, Guides, and Follows Consultation
186(1)
Be Proactive in Statistical Consulting
187(2)
Use the Web for Reference, Resource, and Education
189(1)
Listen to, and Heed the Advice of Experts in the Field
190(3)
Epilogue 193(2)
References 195(12)
Author Index 207(4)
Topic Index 211

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