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Statistics in Class Certification and at Trial: Leveraging and Attacking Statistical Evidence in Class Actions

Lessons From Recent Cases on the Use of Statistics to Prove Classwide Liability and Damages

Today’s faculty features:

1pm Eastern | 12pm Central | 11am Mountain | 10am Pacific THURSDAY, MAY 19, 2016

Paul G. Karlsgodt, Partner, Baker Hostetler, Denver Brian A. Troyer, Partner, Thompson Hine, Cleveland

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Statistical Evidence in Class Actions: Where Are We After Tyson Foods?

Brian Troyer

Brian.Troyer@ThompsonHine.com

Agenda

• General Principles
• From Dukes to Tyson Foods
• Other Developments

– What about Duran? – Consumer Value Studies – Price-Fixing Conspiracies – Event Studies (Best Buy)

• Case Examples for Further Study

6

What Is “Representative” Evidence?

• In statistics, a representative sample is one from which population characteristics can be

inferred.

– Representative sample: random selection, sufficient size, population characteristics – Population statistics, including those calculated from samples, support only predictions or estimates about any individual class member or other unit of analysis.

• Under Rule 23, a representative plaintiff must “possess the same interest and share the

same injury.” Wal-Mart Stores Inc. v. Dukes, 131 S. Ct. 2541 (2011).

– In a class action, it must be possible to draw conclusions about individuals. The theory of a class action is that it is fair to draw conclusions because the evidence in the plaintiff’s individual case is the same evidence that would be used in other class member’s cases. Under Rule 23, evidence is representative because it is the same evidence for each class member.

• What evidence would be used in the plaintiff’s individual case (or another’s)?
• Would the evidence in each class members’ case consist of that same evidence?
• Is statistical evidence being offered only to justify class treatment, or would the named plaintiff use that

evidence?

– Aggregate or composite proof is not the same as representative proof, nor is a mere sample of aggregate proof. Under Dukes, the important question is whether there is different evidence, not whether there is any similar evidence.

7

Ecological Fallacy

• This type of fallacy occurs when a conclusion is drawn about an individual from analysis of

group data. Population statistics do not permit such conclusions.

– If seniors at Smart University have a mean LSAT score 5 points above the national average, we cannot conclude that Joe’s score is 5 points above it. Even with additional statistics like distribution and standard deviation calculations, we can only obtain probabilities or estimates about individuals.

• Ecological fallacies frequently occur when statistics are offered as common proof of

conduct, impact, or damages.

– If women on average were less likely to be promoted or given raises, Jane Doe suffered discrimination (or there is a common question of discrimination). – If X% of promotion decisions were discriminatory, Jane Doe suffered X such acts of discrimination. – If employees on average worked 2 hours per week overtime, John Doe was denied overtime pay. – If employees worked on average more than 50% in the office, John Doe worked more than 50% in the office and was non-exempt. – If 75% of widgets are 10% out of tolerance, Joe’s widget is 10% out of tolerance. – If ABC product contained on average more than 50% ingredient A, and it should contain 50% or less, there is a common question of whether ABC product is defective.

8

Statistical Illusions of Commonality

• Statistics often show only an illusory form of commonality. An

inference that 51% of a population is affected by a practice

• disproves rather than supports a finding of commonality;
• leaves unanswered which individuals within the population were affected.
• Statistical probabilities cannot supplant direct, individual proof

and do not establish commonality (differences matter).

– How would we ascertain Joe’s actual LSAT score if it was important to know? Look at Joe’s score, not average scores.

• Practice pointer: plaintiffs’ use of statistics or aggregate

evidence cannot properly deprive a defendant of the right to discover and use individual evidence.

9

A Sampling of Other Common Errors and Abuses

• Erroneous or unsupported assumptions

– All off-label marketing is fraudulent (i.e., off-label use is always ineffective). – All class members were unaware the drug was unapproved.

• Statistical measures without reference to any objective standard

– Standard deviation without reference to magnitude of permissible deviation, or without determining the type of distribution – No objective standards of value similarity or correspondence – No objective definitions of product or performance characteristics or requirements underlying calculation

• Simple misapplication or misuse of statistical measures

– Standard deviations and/or averages used to portray uniformity or consistency

• Averages by their nature conceal variations.
• Standard deviations do not account for all cases.

10

A Sampling of Other Common Errors and Abuses

• Correlations that depend upon arbitrary scales

– Data/variables placed on a scale from 0-100% in arbitrary percentage ranges may exhibit correlations dependent upon how the scale is divided.

• Correlations and trends that are artifacts of arbitrary data aggregation

and averaging, concealing individual variables and confounding effects

– In re Graphics Processing Units Antitrust Litig., 253 F.R.D. 478 (N.D. Cal. July 18, 2008) (false correlations created by averaging price changes across products, purchasers, and channels). – Simpson’s Paradox (trend or correlation reverses with combination or separation

• f data sets)
• Implicit statistical inferences denied to be subject to rules of statistics

– Testing a handful out of millions of specimens and drawing conclusions as to all – Selection of “representative” plaintiffs or cases based upon expert judgment (purposive sampling), or improper extrapolation by “subject matter expert”

11

A Sampling of Other Common Errors and Abuses

• Data collected for different purposes and without accounting for

critical variables

– Were the necessary parameters properly measured or counted? – Double counting, miscounting? – Do the data sources reflect different population subsets or assumptions?

• Sampling from the dependent variable (or no true independent

variable), but extrapolation of inferences to all

– Statistical analyses only of defective product units – Measurement of injury rate only among those known to have actual exposure – Comparison of extent of damage to extent of exposure only in damaged units

12

Wal-Mart Stores Inc. v. Dukes, 131 S. Ct. 2541 (2011)

• Plaintiffs proposed to prove a general policy of sex discrimination, consisting of a “common

mode” of exercising discretion, through:

– Statistical comparison, by region, of eligible and promoted women (Dr. Drogin); and – Statistical comparison to promotions of women by competitors (Dr. Bendlich).

• These analyses failed to bridge the “conceptual gap” between statistical findings and

individual claims and injuries:

– Regional and national disparities would not establish uniform, store-by-store discrimination. – Even if they did, they would not demonstrate discrimination at an individual level.

• Plan for “trial by formula” violated the Rules Enabling Act and Due Process Clause by

depriving Wal-Mart of the right to litigate individual defenses.

– District Court proposed summary proceedings on a sample of class members’ claims, followed by extrapolation of aggregate results to the entire class. – Averaging winners and losers? Extrapolating to injured and uninjured without regard to individual facts? – Limits of “trial by formula” are being tested in lower courts.

13

Comcast Corp. v. Behrend, 133 S. Ct. 24 (2013)

• Econometric analysis of class-wide damages must be consistent with

plaintiffs’ liability and causation theory.

• District Court accepted only one of four theories of antitrust impact

(injury) proffered by plaintiffs’ expert.

• Refused to consider failure of his damage model to distinguish between

impact theories, on grounds that this challenge went to the merits.

• Supreme Court reversed class certification, holding that the lower

courts were required to evaluate whether plaintiffs’ damage model was “consistent with their liability case.”

• Courts must resolve issues necessary to determine whether Rule 23 is

satisfied, regardless of overlap with the merits.

• In the absence of a model “establishing that damages are capable of

measurement on a class-wide basis,” individual damage questions would “overwhelm” the litigation (predominance lacking).

14

Tyson Foods, Inc. v. Bouaphakeo, 136 S. Ct. 1036, 194 L. Ed. 2d 124, 2016 U.S. LEXIS 2134 (2016)

• Tyson did not have records of “donning and doffing” time.
• Expert calculated average donning and doffing times.

– Two departments (killing and cutting/trimming) – Based on sample of 744 observations – Average times of 18 and 21.25 minutes per day

• Second expert added averages to employee weekly hours.
• Calculated $6.7 million unpaid overtime to those class

members who ever exceeded 40 hours.

• Jury awarded $2.9 million.

15

Tyson Foods, Inc. v. Bouaphakeo

• Can sampling and averaging be used this way to prove

liability on a class-wide basis, recognizing that some employees never worked over 40 hours?

• Must there be a way to limit compensation only to those

who worked uncompensated overtime?

• Why did the jury reduce its award from $6.7 million to

$2.9 million?

• Did it implicitly find fewer employees injured? Discount

the calculated averages?

• Significance of no Daubert challenge?

16

Tyson Foods, Inc. v. Bouaphakeo

• The employer has a duty under Fair Labor Standards Act (FLSA) to

keep records of time worked.

• In the absence of such records, an employee may use statistical

averaging (“reasonable inference”) to establish a rebuttable presumption of liability (including injury) and damages.

– Originally borrowed from antitrust law – Tyson argued applies only to fact of injury or damage, not extent

• In the terms of Dukes, this statistical proof was sufficient to bridge

the conceptual gap, because of this special rule of proof.

• The Court deferred the question of whether the process adequately

guarded against recoveries by uninjured employees, remanding for distribution of damages.

17

Key Questions After Tyson Foods

• Can the statistical evidence being offered be used in an

individual case? Would it support an element of the claim?

– Rule 23 is only a procedural device. – No change of substantive law or evidentiary rules to facilitate class treatment is allowed.

• If it can, does it satisfy the commonality requirement (is there

a common question)?

– Even if it is admissible, the statistical evidence probably is not the

• nly admissible evidence.
• If it does, are Rule 23(b) and other requirements satisfied?
• This analytical scheme is unchanged by Tyson Foods.

18

Duran v. U.S. Bank N.A., 325 P.3d 916, 172

• Cal. Rptr. 3d 371(Cal. 2014)
• Plaintiffs claimed unpaid overtime due to misclassification as
• utside salespersons (who customarily and regularly work

more than half their time away from office).

• Exemption is based “first and forest on how employee actually

spends his or her time.”

• The trial court certified a class of 260 and selected its own

nonrandom sample of 20 for trial, excluding all other evidence, and extrapolated liability and damages to the entire class.

• It concluded that entire class was misclassified, without input

from experts, and when plaintiffs’ expert estimated at least 13% of all class members misclassified.

19

Duran v. U.S. Bank N.A., 325 P.3d 916, 172

• Cal. Rptr. 3d 371(Cal. 2014)
• The California Supreme Court reversed, finding that the trial plan improperly extrapolated liability and

damages to all class members.

– Sample too small – Sample not random, in fact consciously biased toward the misclassified – Large margins of error in calculation of averages – The trial plan also failed to allow an opportunity to litigate affirmative defenses or present evidence relevant to individual claims.

• “Statistical methods cannot entirely substitute for common proof ….”
• “Class actions do not create a ‘requirement of common evidence.’ Instead, class litigation may be

appropriate if the circumstances of a particular case demonstrate that there is common evidence.”

• While statistical proof can be useful to prove wrongdoing in pattern and practice, securities, or mass tort

cases, “[t]his rationale for aggregate proof simply has no application in wage and hour litigation alleging misclassification.”

• Statistical methods must be compatible with the nature of the claims and defenses.
• Trial plan may not foreclose litigation of relevant affirmative defenses using individual evidence.

20

Consumer Value Studies

• Saavedra v. Eli Lilly and Co., 2014 U.S. Dist. LEXIS 179088 (C.D. Cal.
• Dec. 18, 2014) (denying class certification of state consumer claims

seeking difference between value of product expected and value received, based on conjoint study)

– Consumer value is subjective and detached from price, and price is not a proxy for value in an inefficient market.

• In re NJOY, Inc. Consumer Class Action Litig., 2016 U.S. Dist. LEXIS

24235 (C.D. Cal. Feb. 2, 2016) (denying class certification of UCL/CLRA claims based on conjoint, direct, and Bayesian hedonic analyses, the latter a combination of hedonic regression and conjoint analysis)

– Conjoint and direct studies measure only subjective consumer value or the demand side, ignoring the supply side and failing to calculate true market prices.

• Some other California cases hold the proper measure is price paid less

value actually received (a truer restitution measure).

21

Price-Fixing Conspiracies

• A number of courts have embraced statistical aggregation and

extrapolation to justify class-wide damage awards to direct purchaser classes, despite individual price negotiation and negotiating power.

– In re Urethane Antitrust Litig., 768 F.3d 1245 (10th Cir. 2014) (affirming treble damage award exceeding $1 billion based on sampling and regression analysis, despite admission that some customers were not overcharged). – In re Polyurethane Foam Antitrust Litig., 2014 U.S. Dist. LEXIS 161020, 2014-2 Trade Cas. (CCH) P78,969 (N.D. Ohio Apr. 9, 2014) (certifying direct and indirect purchaser classes seeking over $9 billion from numerous defendants), pet. appeal denied, Carpenter Co. v. Ace Foam, Inc., No. 14-302 (6th Cir. Sept. 29, 2014), cert. denied, 135 F.3d 1493 (2015).

22

Event Studies in Securities Fraud Cases: Eighth Circuit’s Rejection of “Price Maintenance”

• Facts of IBEW Local 98 Pension Fund v. Best Buy Co., 2016 U.S. App. LEXIS 6616 (8th
• Cir. Apr. 12, 2016)

– Press release followed by analyst call two hours later, alleged corrective disclosure three months later – Claims based on press release dismissed (safe harbor) – Agreement of experts that price increase followed press release and preceded the analyst call

• Holding and Reasoning

– Best Buy’s evidence of no front-end price impact successfully rebutted the Basic presumption of market reliance on statements in the analyst call. “Expert [plaintiffs’] Steinholt attributed the entire September 14 price impact to the non-fraudulent EPS guidance in the press release.” Best Buy thus proved no price impact from the analyst call.

• Dissent

– Best Buy failed to present evidence rebutting a presumption that conference call statements prevented a price decline that otherwise would have occurred. – Evidentiary source of this presumption? Evidence that would rebut? Why presume a decline?

23

McLaughlin v. American Co., 522 F.3d 215 (2d Cir. 2008)

• Plaintiffs alleged implicit representation that light cigarettes are healthier; sought

$800 billion under RICO and certification of a nationwide class of all purchasers.

• Plaintiffs relied upon sixteen experts, including economists who proposed

statistical and econometric analyses, under a “price impact” theory of reliance similar to the fraud-on-the-market theory.

• Second Circuit reversed certification, finding that individual proof of reliance,

loss causation, injury, damages (and limitations) was required:

– Market for light cigarettes is not efficient (econometric method inappropriate). – Individual evidence presented to show lack of reliance by customers. – Expert’s survey evidence regarding consumer reliance “pure speculation.” – Statistical analysis did not prove the relevant facts but actually assumed reliance. – Rejected “fluid recovery” approach of awarding aggregate “class” damages followed by “simplified proof of claim procedure” and cy pres.

24

In re Neurontin Sales & Mkt’g Practices Litig.

• Alleged off-label promotion, RICO and NJCFA claims
• Problem: commonality of question of causation by allegedly

fraudulent marketing

• In the first opinion denying class certification, 244 F.R.D. 89

(D. Mass. 2007), plaintiffs were given the opportunity to show through “statistical proof” that essentially all prescriptions in each prescription category were caused by fraud.

• Second class certification motion also denied, 257 F.R.D. 315

(D. Mass. 2009), vacated and remanded, 712 F.3d 60 (1st Cir. 2013).

– Not an efficient market – Defendant’s right to present evidence defeats predominance

25

In re Neurontin Sales & Mkt’g Practices Litig.

• District Court’s denial of consumer class

– Certification denied for indications with less than substantially all sales caused by fraud.

• Impossible to identify affected sales

– Certification denied for indications allegedly > 99% caused by fraud, because of faulty methodology.

• False assumption that all “detailing” was off-label and fraudulent
• Plaintiffs’ physicians were not even detailed
• Failure/inability to account for factors other than fraud
• District Court’s denial of TPP class

– Certification denied because differences between TPP knowledge and reimbursement decisions required individual inquiries to determine the percentage of payments by each TPP that were fraud-induced.

26

In re Neurontin Sales & Mkt’g Practices Litig.

 After trial of individual TPP claims, the Third Circuit vacated and remanded for consideration whether its holding that statistical proof was sufficient to avoid summary judgment on proximate and but-for causation on an individual claim would change the class certification

• result. Harden Mf’g Corp. v. Pfizer, Inc., 712 F.3d 60 (1st Cir. 2013).

 Held economist’s statistical analysis purporting to show percentages of prescriptions induced by fraud sufficient to show causation.  Credited economist’s opinion that physician testimony about factors in prescribing decisions is unreliable.  Most courts have reached contrary conclusions about whether similar statistical analyses can be used to prove causation and reliance in pharmaceutical marketing cases.

27

UFCW Local 1776 & Participating Health & Welfare Fund v. Eli Lilly & Co., 620 F.3d 121 (2d Cir. 2010) (In re Zyprexa)

• “Excess price” analysis could not provide common proof of

– but-for (transactional) causation, because drug pricing is inelastic; – proximate (direct) causation,

• faulty assumption that physicians prescribe based on TPP negotiated prices
• variations in TPP price negotiations showed the causal chain was incomplete.
• “Excess sales” theory could not provide common proof of causation

because, e.g.,

– it assumed away all other factors affecting prescriptions; – it ignored individualized evidence of non-reliance; – TPPs likely paid for different percentages of off-label prescriptions; and, – it ignored alternative prescriptions and costs, some of which could even have cost more than Zyprexa.

28

Rhodes v. E.I. Du Pont de Nemours and Co., 253 F.R.D. 365 (S.D.W.V. 2008)

• The court denied certification of medical monitoring claims based on

contamination of drinking water.

• Selected deficiencies in toxicologist’s and epidemiologist’s quantitative opinions
• ffered to establish common proof:

– Significant Exposure

• The background exposure level remained unknown (no reference standard).
• Analysis instead was focused on risk level (conflating legal elements).

– Significantly Increased Risk of Disease

• They relied on data showing a conservative, safe level of exposure rather than a level causing

significantly increased risk.

• They could not show that any individual suffered a significantly increased risk, only some

higher population risk.

• They relied upon preliminary research data and data collected for regulatory purposes.
• They failed to rule out other variables affecting the claimed health effects.

29

STATISTICAL EVIDENCE IN CLASS ACTIONS: PRACTICE STRATEGIES

AND TACTICS

Paul G. Karlsgodt, Partner Denver, CO

Part 1: Justifying or Challenging the Applicability of Statistical Evidence to Support or Defend Class Certification

31

How Are Statistics Used to Support Class Certification?

• The existence of a common practice.
• A relationship between the defendant’s conduct and

some injury to class members.

• The total damages or other impact caused by a

practice.

• The average or typical individual impact of a

practice.

• The percentage of people impacted by a practice.
• Given a set of characteristics, the probability that a

person was impacted by a practice.

• Common reliance:

– Uniform common reliance, e.g. “fraud on the market.” – Reliance by “most” of the class.

32

Evaluating Common Impact

• Is there truly a common impact?

– Recurring theme: Aggregate impact is mistaken for uniform impact.

• Dukes: Fewer promotions doesn’t mean that all women suffered discrimination.
• McLaughlin: Individual facts (descriptive statistics) presented to show non-

reliance by customers.

• Rhodes: Did not provide any common proof that any given individual suffered a

significantly increased risk of the exposure.

– But see Tyson Foods – Statistical evidence was relevant to each employee’s individual claim of impact. Notably, actual time records were not available.

• Did the challenged practice cause the common impact?

– Recurring Theme: Inability to specify root causes and predictors that are common to all class members and rule out other potential causes.

• Dukes: Conclusions don’t go further than showing that disparities exist
• Rhodes: Did not address the question of the relationship between exposure and a

significantly increased risk of health problems

• Comcast: Analysis took into account the combined value of four different potential

impacts, only one of which the trial court had found could be a common impact.

33

Common Impact Fact Patterns

• Trial by Formula (Duran, Dukes)

– Statistics used to estimate percentage of class members to whom the defendant may be liable –

• Creates the practical problem of not being able to identify which particular class

members suffered injury.

• Winners and losers – Some class members may be better off as a result of a

practice.

– Statistics used to apportion damages

• Might be acceptable if all class members suffered some damages.
• May violate due process rights of absent class members if the formula does not

fairly apportion.

• Root Cause Analysis (Comcast, Dukes)

– Have to be able to rule out causes not consistent with liability. – Has to be able to establish cause as opposed to mere correlation. – Theory of damages has to align with theory of common impact.

• Common Evidence of Individual Impact (Tyson Foods)

– If evidence is admissible for each, it may be admissible for all.

34

Part 2: Using or Challenging Expert Witnesses in Class Certification Proceedings

35

36

Tips for Dealing With Experts

36

Draw Inferences (optional) Analyze Collect Data Considerations



How collected? Trusted source?



Is the method / measurement process reliable (consistent performance with repetition)?



Valid?



Recorded properly?



Categories appropriate?



What is the non-response rate (survey)? Why?



Has the methodology been peer reviewed? Discredited?



Can the results be generalized?



How are charts/graphs presented?



What method is used to select the units (or scale)?



Do analyses reach different

• pinions?



What variables were omitted?



Did the expert answer the right question?



How do I estimate whatever is missing?



Is the sample size big enough to be predictive?



How accurate are the predictions?



Does the analysis prove a fact to be true, or does it assume the fact is true?

Ask, “What is missing? Who would know?”

Challenging Expert Credentials

• Does the statistics expert have expertise in the subject area s/he is analyzing?

– Or is the expert simply a statistician with no particular understanding of the subject at issue? If so, is there a relevant supporting expert?

• In what ways should the statistician’s testimony be supplemented by:

– Other experts with subject area expertise. – Data quality knowledge?

• What are the strengths and weaknesses of the foundational philosophies and

historical tendencies of this expert’s approach?

– Consider an expert’s subfield emphasis—e.g. econometrics, biostats, product testing. – Consider the expert’s attention to the entire lifecycle of a statistic—e.g. initial data profiling, study design, collection method.

• What evidence of error exists in the expert’s published materials? Commentary

by other statisticians?

– What evidence of error consideration is given in the expert’s own published materials? Are none, some, or all potential deficiencies noted by the expert? – How extensive and meaningful were peer reviews for published materials, assuming they exist at all?

37

Evaluating Expert Methodologies

• Take a hard look at the statistician’s methodology. It can have a big

impact on the case outcome. – Dukes: Trial by formula not allowed. – McLaughlin: The expert’s survey methodology deemed “pure speculation.” – Neurontin: Inability to ID root cause 1) Where expert’s opinion was that less than substantially all (>99%) of prescriptions were caused by fraud, individual inquiry required; 2) Where expert’s opinion was that substantially all prescriptions were caused by fraud, the expert analysis was flawed. – Zyprexa: Root cause not pinpointed by expert. – Rhodes: 1) Preliminary and insufficient data was used. 2) Failed to rule out other variables. – Duran: 1) Methodology was flawed because sample was arbitrary; 2) Sampling would have been improper even if used to calculate damages due to the high margin of error. – Facebook: there was no way to conduct this type of highly specialized and individualized analysis for each of the thousands of advertisers in the proposed class.

• Tyson Foods: No Daubert motion filed!

38

Understanding kinds of statistics

“Statistics is the science and art of describing data and drawing inferences from them”*

39

*(Finkelstein and Levin, p. 1)

Describes relationships, correlations, events Pay for women in company X is 15% lower than it is for men.

Statistics Inferential Statistics Descriptive Statistics

Makes inferences, generalizations, estimates, predictions Company X has a discriminatory pay policy.

Terminology of “Statistics”?

• Probability

– How likely is something to be true?

• Regression analysis

– Discussed in Wal-Mart Stores, Inc. v. Dukes and Comcast – Examines the relationship between variables.

• Surveys

– Of X population, Y are likely to respond this way.

• Econometrics

– E.g., “but for the misrepresentation, the price would have been X dollars lower”

• Compilations of Data

– Not “statistics” per se, but may raise some of the same issues.

• Studies

– Discussed in Tyson Foods. – Based on observations of a sample population, we estimate the

• utcome to be X.

40

41

Visualizing Root Causes

• Is there a common “answer” for all class members—i.e. did the same set of

circumstances apply to each class member; “Yes” in Halliburton; “No” in Dukes

• Is there some other possible explanation (other than gender bias)?
• Root Cause (Ishikawa) Diagram

41

Class Definition “’[A]ll women [w]ho have been or may be subjected to Wal-Mart’s challenged pay and management track promotions policies and practices.” Personal Traits Employment Status Management Behaviors Policies & Procedures

Gender Personal decisions Age Family Situation Full-/part-time Tenure Role Previous job Performance Biased Discretion Non-biased discretion Mobility Workload Expectations

What Else?

Paraphrasing: While disparity may exist, the underlying root causes are likely to be different among class members

Region

Dept Store

Challenging Validity of Data

– Is there an actively-engaged, senior executive-sponsored data governance body in place? – What evidence is available to demonstrate that a robust data quality management program is used? – Are data stewardship roles & responsibilities well-defined? – Is a standards-based data management process and procedure framework in place? – Does the company have day-to-day reliance on purpose-built data governance tools and performance metrics? – Does a robust and active master data management program exist? – Is a financial information management program in place? – How well and how often are errors identified, analyzed for root cause, and corrected? – How well-managed is the quality of data coming into the system, both manually and in an automated fashion? – What data in the system can’t be considered the source of truth?

42

For Further Study

• David H. Kaye & David A. Freedman, Reference Guide on Statistics, Reference

Manual on Scientific Evidence 3d Ed. (Federal Judicial Center 2011) (http://www.fjc.gov/public/pdf.nsf/lookup/SciMan3D01.pdf/$file/SciMan3D01.pdf)

• Robert Ambrogi, Statistics Surge as Evidence in Trials, IMS Newsletter, BullsEye:

August 2009, (http://www.ims-expertservices.com/newsletters/aug/statistics-surge- as-evidence-in-trials-081409.asp)

• Edward K. Cheng, A Practical Solution to the Reference Class Problem, 109 Colum.
• L. Rev. 2081 (2009) http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1357682

(the article is also available on JSTOR, which requires a login http://www.jstor.org/stable/40380407?seq=1#page_scan_tab_contents)

• Denise Martin, Stephanie Plancich, and Mary Elizabeth Stern, Class Certification in

Wage and Hour Litigation: What Can We Learn from Statistics? (Nera Economic Consulting 2009) http://www.nera.com/content/dam/nera/publications/archive1/PUB_Wage_Hour_Litig ation_1109_final.pdf

• Dukes, plaintiff’s Expert Dr. Richard Drogin’s Statistical Report

(http://www.walmartclass.com/all_reports.html)

• Michael O. Finkelstein and Bruce Levin, Statistics for Lawyers: Third Edition

(Springer, 2015)

• Finkelstein, Michael O., Basic Concepts of Probability and Statistics in the Law

(Springer, 2009)

• Olive Jean Dunn and Virginia A. Clark, Applied Statistics: Analysis of Variance and

Regression, Second Edition (John Wiley & Sons, 1987)

43

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These materials have been prepared by Baker & Hostetler LLP for informational purposes only and are not legal advice. The information is not intended to create, and receipt of it does not constitute, a lawyer-client relationship. Readers should not act upon this information without seeking professional counsel. You should consult a lawyer for individual advice regarding your own situation.