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This publication is directed at both attorneys and statisticians to ensure they will work together successfully on the application of statistics in the law. Attorneys will learn how best to utilize the statistician's talents, while gaining an enriched understanding of the law relevant to audits, jury selection, discrimination, environmental hazards, evidence, and torts as it relates to statistical issues. Statisticians will learn that the law is what judges say it is and to frame their arguments accordingly. This book will increase the effectiveness of both parties in presenting and attacking statistical arguments in the courtroom. Topics covered include sample and survey methods, probability, testing hypotheses, and multiple regression.

Tabla de contenidos provista por Syndetics

• Part I Samples And Populations
• 1 Samples and Populations(p. 3)
• 1.1 Audits(p. 4)
• 1.1.1 Validity of Using Sample Methods(p. 5)
• 1.1.2 Basis for Objection(p. 7)
• 1.1.3 Is the Sample Size Adequate?(p. 8)
• 1.2 Determining the Appropriate Population(p. 8)
• 1.2.1 Jury Panels(p. 9)
• 1.2.2 Criminal Universe(p. 10)
• 1.2.3 Trademarks(p. 10)
• 1.2.4 Discrimination(p. 12)
• 1.2.5 Downsizing(p. 15)
• 1.3 Summary(p. 15)
• 2 Representative Samples and Jury Selection(p. 17)
• 2.1 Concepts(p. 17)
• 2.2 Issues(p. 17)
• 2.2.1 Burden of Proof(p. 18)
• 2.2.2 The Right to be Eligible to Serve(p. 19)
• 2.2.3 Cognizable, Separate, Identifiable Groups(p. 20)
• 2.2.4 Voir Dire Rights of Litigants and Jurors(p. 21)
• 2.3 Composition of the Jury Pool(p. 22)
• 2.3.1 True Cross-Section(p. 22)
• 2.3.2 Snapshot in Time(p. 23)
• 2.3.3 Composition of the Individual Panel(p. 23)
• 2.3.4 Standing(p. 25)
• 2.4 Random Selection(p. 25)
• 2.4.1 Errors in Sampling Methodology(p. 26)
• 2.5 Summary(p. 27)
• 2.6 To Learn More(p. 27)
• 3 Sample and Survey Methodology(p. 29)
• 3.1 Concepts(p. 29)
• 3.2 Sampling Methodology(p. 29)
• 3.2.1 Cluster Sampling(p. 31)
• 3.2.2 The Fight over the Census(p. 32)
• 3.3 Increasing Sample Reliability(p. 34)
• 3.3.1 Designing the Questionnaire(p. 34)
• 3.3.2 Data Integrity(p. 35)
• 3.4 How Much to Tell the Court(p. 36)
• 3.5 Missing Data and Nonresponders(p. 37)
• 3.6 Summary(p. 38)
• 4 Presenting Your Case(p. 41)
• 4.1 Concepts(p. 41)
• 4.2 The Center or Average(p. 41)
• 4.2.1 Extrapolating from the Mean(p. 42)
• 4.2.2 The Geometric Mean(p. 42)
• 4.2.3 The Mode(p. 44)
• 4.3 Measuring the Precision of a Sample Estimate(p. 44)
• 4.3.1 Standard Deviation(p. 45)
• 4.3.2 Bootstrap(p. 46)
• 4.3.3 Coefficient of Variation(p. 47)
• 4.4 Changes in Rates(p. 48)
• 4.4.1 Comparative versus Absolute Disparity(p. 49)
• 4.5 Summary(p. 49)
• Part II Probability
• 5 Probability Concepts(p. 53)
• 5.1 Equally Likely, Equally Frequent(p. 53)
• 5.2 Mutually Exclusive Events(p. 54)
• 5.2.1 Which Population?(p. 54)
• 5.2.2 Putting the Rules in Numeric Form(p. 55)
• 5.3 Conditional Probabilities(p. 55)
• 5.3.1 Negative Evidence(p. 56)
• 5.4 Independence(p. 57)
• 5.4.1 The Product Rule(p. 58)
• 5.4.2 DNA Matching(p. 59)
• 5.4.3 Sampling with and without Replacement(p. 59)
• 5.5 Bayes' Theorem(p. 60)
• 5.6 Summary(p. 61)
• 5.7 To Learn More(p. 61)
• 6 Criminal Law(p. 63)
• 6.1 Facts versus Probabilities(p. 63)
• 6.1.1 Exception to the Rule(p. 67)
• 6.1.2 Bayes' Theorem(p. 67)
• 6.2 Observations versus Guesstimates(p. 69)
• 6.2.1 Inconsistent Application(p. 74)
• 6.2.2 Middle Ground(p. 76)
• 6.3 Probable Cause(p. 77)
• 6.4 Sentencing(p. 77)
• 6.4.1 U.S. v. Shonubi(p. 77)
• 6.4.2 Statistical Arguments(p. 81)
• 6.4.3 Sampling Acceptable(p. 83)
• 6.5 Summary(p. 84)
• 6.6 To Learn More(p. 84)
• 7 Civil Law(p. 85)
• 7.1 The Civil Paradigm(p. 85)
• 7.2 Holding(p. 86)
• 7.2.1 Exception for Joint Negligence(p. 87)
• 7.2.2 Exception for Expert Witnesses(p. 87)
• 7.2.3 Distinguishing Collins(p. 88)
• 7.2.4 Applying Bayes' Theorem(p. 89)
• 7.3 Speculative Gains and Losses(p. 91)
• 7.4 Summary(p. 92)
• 7.5 To Learn More(p. 92)
• 8 Environmental Hazards(p. 93)
• 8.1 Concepts(p. 93)
• 8.2 Is the Evidence Admissible?(p. 94)
• 8.2.1 Daubert(p. 94)
• 8.2.2 Role of the Trial Judge(p. 96)
• 8.3 Is the Evidence Sufficient?(p. 97)
• 8.3.1 SMR Defined(p. 97)
• 8.3.2 Sufficiency Defined(p. 99)
• 8.3.3 Strength and Consistency of Association(p. 100)
• 8.3.4 Dose-Response Relationship(p. 101)
• 8.3.5 Experimental Evidence(p. 102)
• 8.3.6 Plausibility(p. 102)
• 8.3.7 Coherence(p. 102)
• 8.3.8 Other Discussions of Sufficiency(p. 104)
• 8.4 Risk versus Probability(p. 106)
• 8.4.1 Competing Risks(p. 108)
• 8.5 Use of Models(p. 108)
• 8.6 Multiple Defendants(p. 110)
• 8.7 Summary(p. 112)
• Part III Hypothesis Testing And Estimation
• 9 How Large is Large?(p. 115)
• 9.1 Discrimination(p. 116)
• 9.1.1 Eight Is Not Enough(p. 116)
• 9.1.2 Timely Objection(p. 117)
• 9.1.3 Substantial Equivalence(p. 117)
• 9.1.4 Other Related Discrimination Opinions(p. 119)
• 9.2 The 80% Rule(p. 119)
• 9.2.1 Differential Pass and Promotion Rates(p. 120)
• 9.2.2 Sample versus Subsample Size(p. 121)
• 9.3 No Sample Too Small(p. 123)
• 9.3.1 Sensitivity Analysis(p. 123)
• 9.3.2 Statistical Significance(p. 125)
• 9.3.3 Collateral Evidence(p. 126)
• 9.3.4 Supreme Court Division(p. 127)
• 9.4 Summary(p. 129)
• 10 Methods of Analysis(p. 131)
• 10.1 Comparing Two Samples(p. 131)
• 10.1.1 A One-Sample Permutation Test(p. 133)
• 10.1.2 Permutation Tests and Their Assumptions(p. 133)
• 10.2 The Underlying Population(p. 134)
• 10.3 Distribution Theory(p. 137)
• 10.3.1 Binomial Distribution(p. 138)
• 10.3.2 Normal Distribution(p. 138)
• 10.3.3 Poisson Distribution: Events Rare in Time and Space(p. 140)
• 10.3.4 Exponential Distribution(p. 141)
• 10.3.5 Relationships among Distributions(p. 141)
• 10.3.6 Distribution-Free Statistics(p. 142)
• 10.3.7 Bad Choices(p. 142)
• 10.4 Contingency Tables(p. 143)
• 10.4.1 Which Test?(p. 147)
• 10.4.2 One Tail or Two?(p. 150)
• 10.4.3 The Chi-Square Statistic(p. 152)
• 10.5 Summary(p. 154)
• 10.6 To Learn More(p. 155)
• 11 Correlation(p. 157)
• 11.1 Correlation(p. 157)
• 11.1.1 Statistical Significance(p. 158)
• 11.1.2 Practical Significance(p. 159)
• 11.1.3 Absence of Correlation(p. 159)
• 11.1.4 Which Variables?(p. 160)
• 11.1.5 Consistency over the Range(p. 161)
• 11.1.6 Bias(p. 162)
• 11.2 Testing(p. 162)
• 11.2.1 Predictive Validity(p. 162)
• 11.2.2 Validating the Test(p. 165)
• 11.3 Linear Regression(p. 168)
• 11.3.1 Linear Regression Defined(p. 168)
• 11.3.2 Comparing Two Populations(p. 172)
• 11.4 Summary(p. 173)
• 12 Multiple Regression(p. 175)
• 12.1 Lost Earnings(p. 176)
• 12.2 Multiple Applications(p. 177)
• 12.2.1 Construction of the Database(p. 179)
• 12.2.2 Construction of the Equations(p. 180)
• 12.2.3 Application of the Equations(p. 182)
• 12.2.4 Determination of the Limitation(p. 183)
• 12.3 Collinearity and Partial Correlation(p. 184)
• 12.4 Defenses(p. 186)
• 12.4.1 Failure to Include Relevant Factors(p. 187)
• 12.4.2 Negligible Predictive Power(p. 191)
• 12.4.3 Validation(p. 192)
• 12.5 Rebuttal Decisions(p. 192)
• 12.5.1 Collateral Evidence(p. 196)
• 12.5.2 Omitted Variables(p. 196)
• 12.6 Alternate Forms of Regression Analysis(p. 200)
• 12.6.1 Cohort Analysis(p. 201)
• 12.6.2 Linear, Nonlinear, and Logistic Regression(p. 201)
• 12.6.3 Testing for Significance(p. 202)
• 12.7 When Statistics Don't Count(p. 203)
• 12.7.1 Age Discrimination(p. 203)
• 12.7.2 Gender Discrimination(p. 206)
• 12.7.3 Sentencing(p. 206)
• 12.8 Summary(p. 209)
• Part IV Applying Statistics In The Courtroom
• 13 Preventive Statistics(p. 213)
• 13.1 Concepts(p. 213)
• 13.2 Appropriate Controls(p. 213)
• 13.2.1 Breast Implants(p. 213)
• 13.2.2 Basis for Comparison(p. 215)
• 13.2.3 Extent of Damages(p. 216)
• 13.2.4 Placebo Effect(p. 217)
• 13.3 Random Representative Samples(p. 228)
• 13.4 Power of a Test(p. 229)
• 13.4.1 Sample Size(p. 230)
• 13.4.2 Confidence Intervals(p. 232)
• 13.5 Coincidence and the Law of Small Numbers(p. 234)
• 13.5.1 Clustering(p. 235)
• 13.5.2 The Ballot Theorem and the Arc-Sine Law(p. 236)
• 13.6 Coincidence and Ad Hoc-Post Hoc Arguments(p. 237)
• 13.6.1 Reproducibility(p. 237)
• 13.6.2 Painting the Bull's Eye around the Bullet Holes(p. 237)
• 13.6.3 Data Mining or Searching for Significance(p. 238)
• 13.7 Bad Statistics(p. 238)
• 13.7.1 An Example from the National Game(p. 239)
• 13.7.2 Large Sample Approximations(p. 240)
• 13.7.3 Multiple Statistics, Multiple Conclusions(p. 241)
• 13.8 Counterattack(p. 242)
• 14 What Every Statistician Should Know about Courtroom Procedure(p. 243)
• 14.1 Selecting the Case(p. 244)
• 14.2 Prefiling(p. 244)
• 14.3 Discovery(p. 245)
• 14.4 Depositions(p. 245)
• 14.5 Post-Deposition, Pretrial Activities(p. 247)
• 14.6 In the Courtroom(p. 247)
• 14.7 Appeals(p. 248)
• 14.8 Summary(p. 248)
• 15 Making Effective Use of Statistics and Statisticians(p. 249)
• 15.1 Selecting a Statistician(p. 249)
• 15.2 Prefiling Preparation(p. 250)
• 15.3 Discovery(p. 251)
• 15.3.1 Questions(p. 251)
• 15.3.2 Depositions(p. 254)
• 15.4 Presentation of Evidence(p. 255)
• 15.5 Appeals(p. 255)
• 15.6 Summary(p. 256)
• References(p. 257)
• Table of Authorities(p. 263)
• Index(p. 273)

Notas de autor provistas por Syndetics

Phillip I. Good received his Ph.D. in Mathematical Statistics from the University of California at Berkeley