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Common Errors in Statistics and How to Avoid Them (Good)

 
 Author(s)  Phillip Good, James Hardin
 Title  Common Errors in Statistics (and How to Avoid Them)
 Edition  
 Year  2003
 Publisher  John Wiley and Sons, Inc.
 ISBN  0-471-46068-0
 Website  www.wiley.com
 http://www.wiley.com/WileyCDA/WileyTitle/productCd-0470457988.html
 




Table of Contents

PREFACE.

PART I FOUNDATIONS.

1 Sources of Error.

  • Prescription. 
  • Fundamental Concepts. 
  • Ad Hoc, Post Hoc Hypotheses.


2 Hypotheses: The Why of Your Research.

  • Prescription. 
  • What is a Hypothesis? 
  • Found Data. 
  • Null Hypothesis. 
  • Neyman–Pearson Theory. 
  • Deduction and Induction. 
  • Losses. 
  • Decisions.


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?


PART II STATISTICAL ANALYSIS.

4 Data Quality Assessment.

  • Objectives. 
  • Review the Sampling Design. 
  • Data Review. 
  • The Four-Plot.


5 Estimation.

  • Prevention. 
  • Desirable and Not-So-Desirable Estimators. 
  • Interval Estimates. 
  • Improved Results.


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.


7 Miscellaneous Statistical Procedures.

  • Bootstrap. 
  • Bayesian Methodology. 
  • Meta-Analysis. 
  • Permutation Tests.


PART III REPORTS.

8 Reporting Your Results.

  • Fundamentals. 
  • Descriptive Statistics. 
  • Standard Error.
  • p-Values. 
  • Confidence Intervals. 
  • Recognizing and Reporting Biases.
  • Reporting Power. 
  • Drawing Conclusions.


9 Interpreting Reports.

  • With a Grain of Salt. 
  • The Analysis. 
  • Rates and Percentages. 
  • Interpreting Computer Printouts.


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.

 

PART IV BUILDING A MODEL.

11 Univariate Regression.

  • Model Selection. 
  • Stratification. 
  • Estimating Coefficients. 
  • Further Considerations.


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.


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.


14 Modeling Correlated Data.

  • Common Sources of Error. 
  • Panel Data.
  • Fixed- and Random-Effects Models.
  • Population-Averaged GEEs.
  • Quick Reference for Popular Panel Estimators.


15 Validation.

  • Objectives. 
  • Methods of Validation. 
  • Measures of Predictive Success.
  • Long-Term Stability.


GLOSSARY, GROUPED BY RELATED BUT DISTINCT TERMS

BIBLIOGRAPHY

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