For disciplines concerned with human well-being, such as medicine, psychology, and law, statistics must be used in accordance with standards for ethical practice. A Statistical Guide for the Ethically Perplexed illustrates the proper use of probabilistic and statistical reasoning in the behavioral, social, and biomedical sciences. Designed to be consulted when learning formal statistical techniques, the text describes common instances of both correct and false statistical and probabilistic reasoning. Lauded for their contributions to statistics, psychology, and psychometrics, the authors make statistical methods relevant to readers' day-to-day lives by including real historical situations that demonstrate the role of statistics in reasoning and decision making. The historical vignettes encompass the English case of Sally Clark, breast cancer screening, risk and gambling, the Federal Rules of Evidence, "high-stakes" testing, regulatory issues in medicine, difficulties with observational studies, ethics in human experiments, health statistics, and much more. In addition to these topics, seven U.S.
Supreme Court decisions reflect the influence of statistical and psychometric reasoning and interpretation/misinterpretation. Exploring the intersection of ethics and statistics, this comprehensive guide assists readers in becoming critical and ethical consumers and producers of statistical reasoning and analyses. It will help them reason correctly and use statistics in an ethical manner.
Lawrence Hubert is the Lyle H. Lanier Professor of Psychology and a professor of statistics and educational psychology at the University of Illinois. He is a fellow of the American Statistical Association, American Psychological Association, Association for Psychological Science, American Association for the Advancement of Science, and American Educational Research Association. Dr. Hubert has been a recipient of honors, including the Jacob Cohen Award for Distinguished Contributions to Teaching and Mentoring from Division 5 of the American Psychological Association. His research focuses on the development of exploratory methods for data representation in the behavioral sciences, emphasizing cluster analysis, spatially oriented multidimensional scaling techniques, and network representation procedures. Howard Wainer is a Distinguished Research Scientist at the National Board of Medical Examiners and adjunct professor of statistics at the Wharton School of the University of Pennsylvania. He is a fellow of the American Statistical Association and American Educational Research Association. Dr. Wainer has been a recipient of several honors, including the Samuel J. Messick Award for Distinguished Scientific Contributions Award from Division 5 of the American Psychological Association and the Career Achievement Award from the National Council on Measurement in Education. His research encompasses the use of graphical methods for data analysis and communication, robust statistical methodology, and the development and application of generalizations of item response theory.
Preamble Introduction The (Questionable) Use of Statistical Models TOOLS FROM PROBABILITY AND STATISTICS Probability Theory: Background and Bayes' Theorem The (Mis)assignment of Probabilities The Probabilistic Generalizations of Logical Fallacies Are No Longer Fallacies Using Bayes' Rule to Assess the Consequences of Screening for Rare Events Bayes' Rule and the Confusion of Conditional Probabilities Bayes' Rule and the Importance of Base Rates Probability Theory: Application Areas Some Probability Considerations in Discrimination and Classification Probability and Litigation Betting, Gaming, and Risk Correlation Illusory Correlation Ecological Correlation Restriction of Range for Correlations Odd Correlations Measures of Nonlinear Association Intraclass Correlation Prediction Regression toward the Mean Actuarial Versus Clinical Prediction Incorporating Reliability Corrections in Prediction Differential Prediction Effects in Selection Interpreting and Making Inferences from Regression Weights The (Un)reliability of Clinical Prediction The Basic Sampling Model and Associated Topics Multivariable Systems Graphical Presentation Problems with Multiple Testing Issues in Repeated-Measures Analyses Matching and Blocking Randomization and Permutation Tests Pitfalls of Software Implementations Sample Size Selection Psychometrics Traditional True Score Theory Concepts of Reliability and Validity Test Fairness Quotidian Psychometric Insights Psychometrics, Eugenics, and Immigration Restriction DATA PRESENTATION AND INTERPRETATION Background: Data Presentation and Interpretation Weight-of-the-Evidence Arguments in the Presentation and Interpretation of Data (Mis)reporting of Data The Social Construction of Statistics Adjustments for Groups Not Comparable on a Variable, Such As Age Inferring Causality Casuistry The Bradford-Hill Criteria for Determining a Causal Connection Some Historical Health and Medical Conceptions of Disease Causality Medical Error as (the) Causative Factor Simpson's Paradox Meta-Analysis Statistical Sleuthing and Explanation Sleuthing Interests and Basic Tools Survival Analysis Statistical Sleuthing and the Imposition of the Death Penalty: McCleskey v. Kemp (1987) EXPERIMENTAL DESIGN AND THE COLLECTION OF DATA Background: Experimental Design and the Collection of Data Observational Studies: Interpretation Observational Studies: Types Observational Studies: Additional Cautions Controlled Studies Controlled Studies: Additional Sources of Bias The Randomized Response Method Ethical Considerations in Data Collection The Nazi Doctor's Trial and the Nuremberg Code The National Research Act of 1974 The Declaration of Helsinki The Federal Rules of Evidence Junk Science The Consequences of Daubert and the Data Quality Act (of 2001) Some Concluding Remarks Bibliography Author Index Subject Index
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