Applying statistics in the courtroom : a new approach for attorneys and expert witnesses / Phillip I. Good.

Format:

Book

Author/Creator:

Good, Phillip I.

Language:

English

Subjects (All):

Forensic statistics--United States.

Forensic statistics.

United States.

Physical Description:

xviii, 267 pages : illustrations ; 24 cm

Place of Publication:

Boca Raton, Fla. : Chapman & Hall/CRC, [2001]

Summary:

This publication is directed at both the attorney and the statistician to ensure they will successfully apply statistics in the law. The attorney will learn how best to utilize statistics while gaining an enriched understanding of the law on 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. Applying Statistics in the Courtroom: A New Approach for Attorneys and Expert Witnesses will increase the effectiveness of both the attorney and the statistician in presenting and attacking statistical arguments in the courtroom.

Contents:

Part I Samples And Populations

1.1 Audits 4

1.1.1 Validity of Using Sample Methods 5

1.1.2 Basis for Objection 7

1.1.3 Is the Sample Size Adequate? 8

1.2 Determining the Appropriate Population 8

1.2.1 Jury Panels 9

1.2.2 Criminal Universe 10

1.2.3 Trademarks 10

1.2.4 Discrimination 12

1.2.5 Downsizing 15

2 Representative Samples and Jury Selection 17

2.2.1 Burden of Proof 18

2.2.2 The Right to be Eligible to Serve 19

2.2.3 Cognizable, Separate, Identifiable Groups 20

2.2.4 Voir Dire Rights of Litigants and Jurors 21

2.3 Composition of the Jury Pool 22

2.3.1 True Cross-Section 22

2.3.2 Snapshot in Time 23

2.3.3 Composition of the Individual Panel 23

2.3.4 Standing 25

2.4 Random Selection 25

2.4.1 Errors in Sampling Methodology 26

3 Sample and Survey Methodology 29

3.2.1 Cluster Sampling 31

3.2.2 The Fight over the Census 32

3.3 Increasing Sample Reliability 34

3.3.1 Designing the Questionnaire 34

3.3.2 Data Integrity 35

3.4 How Much to Tell the Court 36

3.5 Missing Data and Nonresponders 37

4 Presenting Your Case 41

4.2 The Center or Average 41

4.2.1 Extrapolating from the Mean 42

4.2.2 The Geometric Mean 42

4.2.3 The Mode 44

4.3 Measuring the Precision of a Sample Estimate 44

4.3.1 Standard Deviation 45

4.3.2 Bootstrap 46

4.3.3 Coefficient of Variation 47

4.4 Changes in Rates 48

4.4.1 Comparative versus Absolute Disparity 49

Part II Probability

5.1 Equally Likely, Equally Frequent 53

5.2 Mutually Exclusive Events 54

5.2.1 Which Population? 54

5.2.2 Putting the Rules in Numeric Form 55

5.3 Conditional Probabilities 55

5.3.1 Negative Evidence 56

5.4 Independence 57

5.4.1 The Product Rule 58

5.4.2 DNA Matching 59

5.4.3 Sampling with and without Replacement 59

5.5 Bayes' Theorem 60

6 Criminal Law 63

6.1 Facts versus Probabilities 63

6.1.1 Exception to the Rule 67

6.1.2 Bayes' Theorem 67

6.2 Observations versus Guesstimates 69

6.2.1 Inconsistent Application 74

6.2.2 Middle Ground 76

6.3 Probable Cause 77

6.4 Sentencing 77

6.4.1 U.S. v. Shonubi 77

6.4.2 Statistical Arguments 81

6.4.3 Sampling Acceptable 83

7 Civil Law 85

7.1 The Civil Paradigm 85

7.2 Holding 86

7.2.1 Exception for Joint Negligence 87

7.2.2 Exception for Expert Witnesses 87

7.2.3 Distinguishing Collins 88

7.2.4 Applying Bayes' Theorem 89

7.3 Speculative Gains and Losses 91

8 Environmental Hazards 93

8.2 Is the Evidence Admissible? 94

8.2.1 Daubert 94

8.2.2 Role of the Trial Judge 96

8.3 Is the Evidence Sufficient? 97

8.3.1 SMR Defined 97

8.3.2 Sufficiency Defined 99

8.3.3 Strength and Consistency of Association 100

8.3.4 Dose-Response Relationship 101

8.3.5 Experimental Evidence 102

8.3.6 Plausibility 102

8.3.7 Coherence 102

8.3.8 Other Discussions of Sufficiency 104

8.4 Risk versus Probability 106

8.4.1 Competing Risks 108

8.5 Use of Models 108

8.6 Multiple Defendants 110

Part III Hypothesis Testing And Estimation

9 How Large is Large? 115

9.1 Discrimination 116

9.1.1 Eight Is Not Enough 116

9.1.2 Timely Objection 117

9.1.3 Substantial Equivalence 117

9.1.4 Other Related Discrimination Opinions 119

9.2 The 80% Rule 119

9.2.1 Differential Pass and Promotion Rates 120

9.2.2 Sample versus Subsample Size 121

9.3 No Sample Too Small 123

9.3.1 Sensitivity Analysis 123

9.3.2 Statistical Significance 125

9.3.3 Collateral Evidence 126

9.3.4 Supreme Court Division 127

10 Methods of Analysis 131

10.1 Comparing Two Samples 131

10.1.1 A One-Sample Permutation Test 133

10.1.2 Permutation Tests and Their Assumptions 133

10.2 The Underlying Population 134

10.3 Distribution Theory 137

10.3.1 Binomial Distribution 138

10.3.2 Normal Distribution 138

10.3.3 Poisson Distribution: Events Rare in Time and Space 140

10.3.4 Exponential Distribution 141

10.3.5 Relationships among Distributions 141

10.3.6 Distribution-Free Statistics 142

10.3.7 Bad Choices 142

10.4 Contingency Tables 143

10.4.1 Which Test? 147

10.4.2 One Tail or Two? 150

10.4.3 The Chi-Square Statistic 152

11 Correlation 157

11.1 Correlation 157

11.1.1 Statistical Significance 158

11.1.2 Practical Significance 159

11.1.3 Absence of Correlation 159

11.1.4 Which Variables? 160

11.1.5 Consistency over the Range 161

11.1.6 Bias 162

11.2 Testing 162

11.2.1 Predictive Validity 162

11.2.2 Validating the Test 165

11.3 Linear Regression 168

11.3.1 Linear Regression Defined 168

11.3.2 Comparing Two Populations 172

12 Multiple Regression 175

12.1 Lost Earnings 176

12.2 Multiple Applications 177

12.2.1 Construction of the Database 179

12.2.2 Construction of the Equations 180

12.2.3 Application of the Equations 182

12.2.4 Determination of the Limitation 183

12.3 Collinearity and Partial Correlation 184

12.4 Defenses 186

12.4.1 Failure to Include Relevant Factors 187

12.4.2 Negligible Predictive Power 191

12.4.3 Validation 192

12.5 Rebuttal Decisions 192

12.5.1 Collateral Evidence 196

12.5.2 Omitted Variables 196

12.6 Alternate Forms of Regression Analysis 200

12.6.1 Cohort Analysis 201

12.6.2 Linear, Nonlinear, and Logistic Regression 201

12.6.3 Testing for Significance 202

12.7 When Statistics Don't Count 203

12.7.1 Age Discrimination 203

12.7.2 Gender Discrimination 206

12.7.3 Sentencing 206

Part IV Applying Statistics In The Courtroom

13 Preventive Statistics 213

13.2 Appropriate Controls 213

13.2.1 Breast Implants 213

13.2.2 Basis for Comparison 215

13.2.3 Extent of Damages 216

13.2.4 Placebo Effect 217

13.3 Random Representative Samples 228

13.4 Power of a Test 229

13.4.1 Sample Size 230

13.4.2 Confidence Intervals 232

13.5 Coincidence and the Law of Small Numbers 234

13.5.1 Clustering 235

13.5.2 The Ballot Theorem and the Arc-Sine Law 236

13.6 Coincidence and Ad Hoc-Post Hoc Arguments 237

13.6.1 Reproducibility 237

13.6.2 Painting the Bull's Eye around the Bullet Holes 237

13.6.3 Data Mining or Searching for Significance 238

13.7 Bad Statistics 238

13.7.1 An Example from the National Game 239

13.7.2 Large Sample Approximations 240

13.7.3 Multiple Statistics, Multiple Conclusions 241

13.8 Counterattack 242

14 What Every Statistician Should Know about Courtroom Procedure 243

14.1 Selecting the Case 244

14.2 Prefiling 244

14.3 Discovery 245

14.4 Depositions 245

14.5 Post-Deposition, Pretrial Activities 247

14.6 In the Courtroom 247

14.7 Appeals 248

15 Making Effective Use of Statistics and Statisticians 249

15.1 Selecting a Statistician 249

15.2 Prefiling Preparation 250

15.3 Discovery 251

15.3.2 Depositions 254

15.4 Presentation of Evidence 255

15.5 Appeals 255.

Notes:

Includes bibliographical references (pages 257-261) and index.

ISBN:

1584882719

OCLC:

46685360