Presenting Statistical Information

1. Presenting Statistical Information Reports vs Testimony

2. Presenting Statistical Information • Your work as an expert will usually result in a report or a declaration • Declaration is basically a report in legal format, with numbered paragraphs and signature • The report is a foundation for testimony, and might require different information from what will eventually be presented to the trier of fact

3. In a Report • It is important to identify • Source of all information that is relied on • The characteristics of the samples used in statistical analysis and their relationship to the population covered by the suit • The statistical methods used • The criteria used to evaluate results

4. An example • To determine whether the differences in initial passing rates were statistically reliable, I compared the expected and observed passing rates of African Americans vs. Whites. Table 4 displays a comparison of expected and observed outcomes of credit checks as a function of race. • I used both chi squared analysis and binary logistic regression to evaluate the statistical significance and the size of the differences in passing rates. First, this table reports observed and expected frequencies of passing vs. failing initial credit checks. Expected frequencies represent the rates of passing and failing that would be expected if there were no differences between groups. A comparison of expected and observed frequencies can be used to describe the shortfall in initial passing rates associated with group differences.

5. An Example • Finally, this table reports two measures of the size of the difference between groups, the contingency coefficient, the Nagelkerke R squared. The contingency coefficient is a measure of the relationship between group membership and the outcome of credit check. Values of .10 or larger represent effects that are substantively meaningful (Murphy, Myors & Wolach, 2009). The Nagelkerke R squared is an index of the proportion of the variance in the outcomes of credit checks that can be accounted for by race. Values of .01 or higher represent effects that are substantively meaningful (Murphy et al., 2009).

6. An Example • Table 4 • Statistical Tests of African American – White Differences in Outcomes of Initial Credit Checks • PassReview%Pass • African American 175 (223.2)a 352 (303.8) 33.2 • White 238 (189.8) 210 (258.2) 53.1 • c2(1) = 38.34 p < .001 • Fischer’s exact probability < .001 • Contingency coefficient = .197 • Nagelkerke R squared = .049 • a – expected frequencies are shown in parentheses

7. In Testimony • A report needs to be complete • In testimony, KISS (Keep It Simple Stupid!) is the rule of the day • Make presentations simple, clear, visually compelling • The court is often interested in things like shortfalls – how many people are harmed? • E.g., 175 African Americans passed; in a race-neutral system 223.2 would be expected to pass • The shortfall is 48.2

8. Pictures Beat Words • A compelling visual presentation is always best • In a recent case, there was a dispute over whether an Regional Land Manager for an oil company was discriminated against in pay • The Defense argument was that this manager handled a lower-performing territory and was consistently evaluated less favorably than peers

9. Rankings Across Four Sources of Performance Data • Very strong relationship between the supervisor and peer ratings of overall performance – lowest line is Plaintiff • (the correlation is greater than .90)

10. Current Productivity of Regions • Very strong relationship between objective measures of the region productivity and performance ratings. • This result argues strongly against the claim that Plaintiff’s supervisor was biased in his evaluation of her performance.

11. Relationship Between Performance Measures and 2009 Compensation

12. Relationship Between Performance, Area Productivity and 2009 Total Compensation

13. Relationship Between Performance, Area Productivity and 2010 Total Compensation