Statistical Rules of Thumb

ISBN-10: 0470144483
ISBN-13: 9780470144480
Edition: 2nd 2008
Authors: Gerald van Belle
List price: $87.00
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Description: Sensibly organized for quick reference, Statistical Rules of Thumb, Second Edition compiles simple rules that are widely applicable, robust, and elegant, and each captures key statistical concepts. This handbook provides a framework for considering  More...

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Book details

List price: $87.00
Edition: 2nd
Copyright year: 2008
Publisher: John Wiley & Sons, Incorporated
Publication date: 9/2/2008
Binding: Paperback
Pages: 304
Size: 6.00" wide x 9.00" long x 0.75" tall
Weight: 1.210
Language: English

Sensibly organized for quick reference, Statistical Rules of Thumb, Second Edition compiles simple rules that are widely applicable, robust, and elegant, and each captures key statistical concepts. This handbook provides a framework for considering statistical questions such as sample size and design of experiments. Explaining the justification for each rule, this book conveys the various possibilities that statisticians must think of when designing and conducting a study or analyzing its data. It provides a framework for considering such aspects of statistical work such as: randomness and statistical models; sample size; covariation; epidemiology; environmental studies; designing, conducting, and analyzing studies; words, tables, and graphs; and consulting. New rules of thumb are included such as: Sample size for ratios of means; Very non-significant P-values are very significant; Dichotomize continuous variables for odds ratio analysis; and Correlations need to be substantial to gain advantage in ANCOVA. Some rules have been revised for the new edition, i.e. sample size for relative risk and sample size for percentage change. In addition, the references have been completed updated and expanded.

Preface to the Second Edition
Preface to the First Edition
Acronyms
The Basics
Four Basic Questions
Observation is Selection
Replicate to Characterize Variability
Variability Occurs at Multiple Levels
Invalid Selection is the Primary Threat to Valid Inference
There is Variation in Strength of Inference
Distinguish Randomized and Observational Studies
Beware of Linear Models
Keep Models As Simple As Possible, But Not More Simple
Understand Omnibus Quantities
Do Not Multiply Probabilities More Than Necessary
Use Two-sided p-Values
p-Values for Sample Size, Confidence Intervals for Results
At Least Twelve Observations for a Confidence Interval
Estimate [plus or minus] Two Standard Errors is Remarkably Robust
Know the Unit of the Variable
Be Flexible About Scale of Measurement Determining Analysis
Be Eclectic and Ecumenical in Inference
Sample Size
Begin with a Basic Formula for Sample Size-Lehr's Equation
Calculating Sample Size Using the Coefficient of Variation
No Finite Population Correction for Survey Sample Size
Standard Deviation and Sample Range
Do Not Formulate a Study Solely in Terms of Effect Size
Overlapping Confidence Intervals Do Not Imply Nonsignificance
Sample Size Calculation for the Poisson Distribution
Sample Size for Poisson With Background Rate
Sample Size Calculation for the Binomial Distribution
When Unequal Sample Sizes Matter; When They Don't
Sample Size With Different Costs for the Two Samples
The Rule of Threes for 95% Upper Bounds When There Are No Events
Sample Size Calculations Are Determined by the Analysis
Observational Studies
The Model for an Observational Study is the Sample Survey
Large Sample Size Does Not Guarantee Validity
Good Observational Studies Are Designed
To Establish Cause Effect Requires Longitudinal Data
Make Theories Elaborate to Establish Cause and Effect
The Hill Guidelines Are a Useful Guide to Show Cause Effect
Sensitivity Analyses Assess Model Uncertainty and Missing Data
Covariation
Assessing and Describing Covariation
Don't Summarize Regression Sampling Schemes with Correlation
Do Not Correlate Rates or Ratios Indiscriminately
Determining Sample Size to Estimate a Correlation
Pairing Data is not Always Good
Go Beyond Correlation in Drawing Conclusions
Agreement As Accuracy, Scale Differential, and Precision
Assess Test Reliability by Means of Agreement
Range of the Predictor Variable and Regression
Measuring Change: Width More Important than Numbers
Environmental Studies
Begin with the Lognormal Distribution in Environmental Studies
Differences Are More Symmetrical
Know the Sample Space for Statements of Risk
Beware of Pseudoreplication
Think Beyond Simple Random Sampling
The Size of the Population and Small Effects
Models of Small Effects Are Sensitive to Assumptions
Distinguish Between Variability and Uncertainty
Description of the Database is As Important as Its Data
Always Assess the Statistical Basis for an Environmental Standard
Measurement of a Standard and Policy
Parametric Analyses Make Maximum Use of the Data
Confidence, Prediction, and Tolerance Intervals
Statistics and Risk Assessment
Exposure Assessment is the Weak Link in Assessing Health Effects of Pollutants
Assess the Errors in Calibration Due to Inverse Regression
Epidemiology
Start with the Poisson to Model Incidence or Prevalence
The Odds Ratio Approximates the Relative Risk Assuming the Disease is Rare
The Number of Events is Crucial in Estimating Sample Sizes
Use a Logarithmic Formulation to Calculate Sample Size
Take No More than Four or Five Controls per Case
Obtain at Least Ten Subjects for Every Variable Investigated
Begin with the Exponential Distribution to Model Time to Event
Begin with Two Exponentials for Comparing Survival Times
Be Wary of Surrogates
Prevalence Dominates in Screening Rare Diseases
Do Not Dichotomize Unless Absolutely Necessary
Additive and Multiplicative Models
Evidence-Based Medicine
Strength of Evidence
Relevance of Information: POEM vs. DOE
Begin with Absolute Risk Reduction, then Follow with Relative Risk
The Number Needed to Treat (NNT) is Clinically Useful
Variability in Response to Treatment Needs to be Considered
Safety is the Weak Component of EBM
Intent to Treat is the Default Analysis
Use Prior Information but not Priors
The Four Key Questions for Meta-analysts
Design, Conduct, and Analysis
Randomization Puts Systematic Effects into the Error Term
Blocking is the Key to Reducing Variability
Factorial Designs and Joint Effects
High-Order Interactions Occur Rarely
Balanced Designs Allow Easy Assessment of Joint Effects
Analysis Follows Design
Independence, Equal Variance, and Normality
Plan to Graph the Results of an Analysis
Distinguish Between Design Structure and Treatment Structure
Make Hierarchical Analyses the Default Analysis
Distinguish Between Nested and Crossed Designs-Not Always Easy
Plan for Missing Data
Address Multiple Comparisons Before Starting the Study
Know Properties Preserved When Transforming Units
Consider Bootstrapping for Complex Relationships
Words, Tables, and Graphs
Use Text for a Few Numbers, Tables for Many Numbers, Graphs for Complex Relationships
Arrange Information in a Table to Drive Home the Message
Always Graph the Data
Always Graph Results of an Analysis of Variance
Never Use a Pie Chart
Bar Graphs Waste Ink; They Don't Illuminate Complex Relationships
Stacked Bar Graphs Are Worse Than Bar Graphs
Three-Dimensional Bar Graphs Constitute Misdirected Artistry
Identify Cross-sectional and Longitudinal Patterns in Longitudinal Data
Use Rendering, Manipulation, and Linking in High-Dimensional Data
Consulting
Session Has Beginning, Middle, and End
Ask Questions
Make Distinctions
Know Yourself, Know the Investigator
Tailor Advice to the Level of the Investigator
Use Units the Investigator is Comfortable With
Agree on Assignment of Responsibilities
Any Basic Statistical Computing Package Will Do
Ethics Precedes, Guides, and Follows Consultation
Be Proactive in Statistical Consulting
Use the Web for Reference, Resource, and Education
Listen to, and Heed the Advice of Experts in the Field
Epilogue
References
Author Index
Topic Index

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