Statistics in Class Action Litigation: Admissibility and the Impact of Wal-Mart v. DukesCrafting Provisions to Allocate Risk, Avoid Common Pitfalls, and Minimize Liability

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THURSDAY, OCTOBER 6, 2011

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Paul G. Karlsgodt, Partner, Baker Hostetler, Denver

Brian A. Troyer, Partner, Thompson Hine, Cleveland

Justin Hopson, Director, Hitachi Consulting, Denver

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Paul Karlsgodt, Baker Hostetlerpkarlsgodt@bakerlaw.com

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Brian Troyer, Thompson Hinebrian.troyer@thompsonhine.com

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Justin Hopson, Hitachi Consultingjhopson@hitachiconsulting.com

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Statistics in Class Certification Proceedings

What they’re good for, and how to discredit them

5

Copyright 2011Paul KarlsgodtBrian TroyerJustin HopsonAll rights reserved

Agenda

Part I – Introduction (~15 min.) Why is this topic important? What do we mean by “statistics”? How are statistics used in class certification?

Part II – Case law on the use of statistics in class certification (~40 min.)

Part III – Practical tips on presenting and challenging statistics (~20 min.)

Question and Answer (~15 min.)

6

Part I – Introduction

7

Why is this topic important?

Dukes, In re IPO, In re Hydrogen Peroxide create a more demanding standard for class certification

Both sides are likely to attempt to create a more well-developed factual record

Statistics often provide an appealing way to illustrate how aggregate or common proof is possible.

Data is more available and accessible than ever before.

8

General overview“Statistics is the science and art of describing data and

drawing inferences from them”*

*(Finkelstein and Levin, p. 1)

Describes relationships, correlations, events

Example: batting average, transit daily ridership, # of callsCommon Terms: counts, summaries, %, average (mean, median, mode), variation, rangeUse and Limitations: Describes observations. Easily audited

StatisticsStatistics

Inferential StatisticsInferential Statistics

DescriptiveStatistics

DescriptiveStatistics

Uses data to make inferences, generalizations, estimates, predictions, or decisions about a process, outcome, or population

Example: 95% confident tomorrow’s transit ridership will be between 65K and 72KCommon Terms: random sample, hypothesis testing, confidence interval, significanceUse and Limitations: Estimate “how likely?” based on what you’ve observed. Regression analysis subject to estimation error

9

Terminology of “statistics”?

Descriptive statistics Used to explain an event or course of events.

Inferential Statistics From the data showing Y, you can infer that X is true.

Probability How likely is something to be true?

Regression analysis Discussed in Wal-Mart Stores, Inc. v. Dukes. 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.

10

Where do we see statistics?Employment and sociologicalEconomic and financialEpidemiologicalPollution and toxic exposureSales and prescriptionsProduct failures

11

Common Uses of Statistics in Law

Most commonly presented to prove commonality (Rule 23(a)(2)), predominance and Superiority (Rule 23(b)(3)), and cohesiveness (Rule 23(b)(2))As proof of a common policy or practiceAs proof of a common relationship between the

defendant’s conduct and some injury to class members (e.g. reliance, causation, injury)

As common proof of aggregate or class-wide damages, restitution

Less commonly presented to prove other factorsE.g., In re Initial Public Offering Securities Litig.,

471 F.3d 24 (2d Cir. 2006) (numerosity).

12

How are statistics used to support Class Certification

The existence of a common practiceA relationship between the defendant’s conduct

and some injury to class membersThe total damages or other impact caused by a

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

person was impacted by a practice.Common reliance

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

13

Part II – Case law on the use of statistics in class certification

14

Common Practice/Policy: Wal-Mart Stores, Inc. v. Dukes

Title VII sex discrimination claimsPlaintiffs are required to prove a pattern or policy of

discrimination.Ninth Circuit affirmed certification of a class of 1.5 million

current and former female employees, arguing that all female employees were subject to a discriminatory policy.

Dukes reaffirmed: that Rule 23 is not a mere pleading standard, but that the

proponent must prove that the requirements are satisfied.that a court must conduct a “rigorous analysis.”that “[f]requently that ‘rigorous analysis’ will entail some

overlap with the merits of the plaintiff’s underlying claim. That cannot be helped.” 131 S.Ct. at 2551.

The broad question, then, is what rigorous analysis of statistical evidence looks like.

15

Dukes: Proof of Common Injury

Two ways to bridge gap between the individual’s claim and the existence of a class who suffered the same injuryBiased testing procedure (not at issue)Significant proof of a general policy of discrimination

Plaintiffs offered a “social framework” analysis by sociologist Dr. Bielby claiming to show that Wal-Mart’s corporate culture made it vulnerable to gender bias, buthe could not determine with any specificity how regularly

stereotypes played a meaningful role, andcould not say whether 0.5% or 95% of decisions were

discriminatory. “If Bielby admittedly has no answer to that question, we can

safely disregard what he has to say.” 131 S.Ct. at 2554.

16

Dukes: Proof of the Existence of a Common Policy

The only allegedly discriminatory general policy identified was of allowing supervisors discretion. “just the opposite of a uniform employment practice ….” 131 S.Ct. at

2554. “In such a company, demonstrating the invalidity of one manager’s use

of discretion will do nothing to demonstrate the invalidity of another’s.” Plaintiffs attempted to show a “common mode” of exercising

discretion through statistical and anecdotal evidence.Dr. Drogin (statistician) compared, by region, the number of women

promoted with percentage of women in pool of hourly workers.Dr. Bendich (labor economist) compared work-force data of Wal-Mart

and competitors, concluding Wal-Mart promoted lower percentage of women.

17

Dukes: Proof of the Existence of a Common Policy, cont’d

These statistical analyses failed to bridge the conceptual gap to show that the existence of a general policy or practice of discrimination was a question common to all class members for two reasons.

First, regional and national disparities failed to provide a basis to infer a “uniform, store-by-store disparity” and thus a company-wide policy. described as a “failure of inference” design characteristic of the study caused it to fail to meet its stated purpose mismatch between statistical method and the legal requirement for certification

Second, even if the studies established a disparity in every store from regional or nationwide data, this would not demonstrate commonality. Disparate impact of discretion insufficient. “Other than the bare existence of delegated discretion, respondents have

identified no ‘specific employment practice’—much less one that ties all their 1.5 million claims together. Merely showing that Wal-Mart’s policy of discretion has produced an overall sex-based disparity does not suffice.” 131 S.Ct. at 2555-56.

Note the dissent’s charge that the majority misunderstood the methods used.

18

Dukes: Rejection of Trial by Formula The Court also rejected class certification based on “Trial by

Formula.” 131 S.Ct. at 2561. A sample set of class members’ claims would be tried. The percentage of valid claims and the average backpay award to

determine a “class recovery” to be distributed without further individual proceedings.

“We disapprove that novel project.” This scheme would deprive Wal-Mart of the right to litigate defenses

to individual claims, and would violate the Rules Enabling Act. This holding is similar to the one in McLaughlin.

Is it a class if some members would win and some would lose? Would the losers recover a share of the award?

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Dukes in Summary Does not change the landscape regarding statistics and class

certification but confirms necessity of rigorous scrutiny. Gives a strong hint in favor of Daubert, but does not answer the

question. The Court examined the statistical analyses and found inferential

gaps between the policy that statistics were claimed to show and what they actually showed. Court evaluated the merits/substance of the statistics.

Illustrates and confirms inherent limitations of statistical and aggregate proof.

Confirms that, validity of statistics aside, conceptual gaps are critical. Even if statistics showed the claimed pattern, that pattern would not

establish commonality. Whether any individual decision was discriminatory would still require

individual proof. Keep in mind that the issue was whether statistical evidence could

be used as representative proof on behalf of all women at once, not whether it could be used at all by individual plaintiffs.

20

Statistical Concepts in Dukes

21

Descriptive statistics (¶14)Average salary male>female 2001

(Table 8) Why the average? What is the distribution?Why 2001?

Inferential statistics (¶36)Promotion analysis controls for feeder job,

store, and move year (¶54). Valid given Bielby’s assertion that relocation across stores “creates a greater burden for women”?1

Break-out sub-set (Sam’s Club ¶62) for further analysisWas it responsible for the overall

differences ?What is left in the set of observations for,

“Wal-Mart, not Sam’s Club?” 1 Class Cert p. 25

21

Statistical Concepts in Dukes (cont’d)

22

• Recall that regression analysis is used to describe the relationship between phenomena

• Plaintiffs in Dukes . . . • Tried to predict salary using job held, store where

person worked, promotions/transfers, full-/part-time, salaried/hourly

• Outcome: Using gender in the equation made it a better predictor of observed salary. • So gender was in fact significant. Earnings between men and

women are disparate• But it was not determined to be caused by an active policy to

discriminate against women. So the difference is not “impact”• Just because there is a difference, doesn’t make

it actionable

22

Visualizing The Issues

23

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 perhaps some other explanation (other than gender)? Root Cause (Ishikawa) Diagram

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

EmploymentStatus

ManagementBehaviors

Policies &Procedures

Gender Personal decisions Age Family Situation

Full-/part-time Tenure Role Previous jobPerformance

Discretion

Mobility Workload Expectations

What Else?

Paraphrasing: While disparity may exist, the underlying root causes are

likely to be different among class members

RegionDept Store

23

Post-Dukes In re Wells Fargo Residential Mortgage Lending

Discrimination Litig., slip op., No. 3:08-md-01931-MMC (Sept. 6, 2011).Denied certification of claims under Fair Housing Act and

Equal Credit Opportunity Act.Found regression analysis allegedly showing disparate impact

of discretionary policy insufficient.Daubert motion denied for purposes of decision.

24

Common Impacts: Borrowing “Fraud on the Market”

“[W]here materially misleading statements have been disseminated into an impersonal, well-developed market for securities, the reliance of individual plaintiffs on the integrity of the market price may be presumed.” Basic Inc. v. Levinson, 485 U.S. 224, 247, 108 S.Ct. 978, 991, 99 L.Ed. 194 (1988).

Based on the efficient-market hypothesis: the market price of a security reflects all information known to the market.

An efficient market is one “in which information important to reasonable investors (in effect, the market) is immediately incorporated into stock prices.” In re Burlington Coat Factory Sec. Litig., 114 F.3d 1410, 1425 (3d Cir. 1997). Secondary trading market Open market with many buyers and sellers and impersonal trading Sufficiently developed market informed by analysts and knowledgeable

investors Sufficiently high trading volume Continuity and liquidity Responsive to information

25

Borrowing “Fraud on the Market” (cont’d)

When the market characteristics satisfy the FOTM prerequisites, individual reliance is rebuttably presumed. Quantitative/statistical proof used to show efficient market, not market

response to adverse information. Reliance is separate from the element of loss causation

Loss causation need not be proved as a condition to class certification. Erica P. John Fund v. Halliburton Corp.

But it presumably must still be susceptible to common resolution (Dukes).

Nevertheless, common proof of loss causation and damages also are typically based on quantitative market analysis, and plaintiffs often plead and argue loss causation facts in support of fraud on the market/reliance (to show that the market responded to adverse information).

26

Borrowing “Fraud on the Market” (cont’d)

Efforts to apply these concepts from securities fraud cases in consumer class actions represent one of the most prevalent uses of statistical and quantitative analysis in class certification.

Statistical and econometric analyses are typically offered to show that prices and sales of a consumer product were inflated because of fraud—fraud on the consumer market.

The difference is that markets for consumer goods and services are inherently different from securities trading markets.

27

Common Impacts: McLaughlin

Judge Weinstein certified nationwide class of light cigarette consumers under RICO.

Plaintiffs alleged implicit representation that light cigarettes are healthier; sought $800 billion.

Trial court’s conclusions: “Plaintiffs’ experts have demonstrated that they can probably extrapolate

to the class as a whole from individual class members’ experience as determined in individual discovery, surveys, and statistical proof.” Schwab v. Philip Morris USA, 449 F. Supp. 3d 992, 1123 (E.D.N.Y. 2006), rev’d sub nom. McLaughlin v. American Tobacco Co., 522 F.3d 215 (2d Cir. 2008).

Although some consumers may have received what was promised, “[r]eliable statistical evidence is available to help the jury decide, within a reasonably precise range, how many [class members] fall into the defrauded category.” Schwab, 449 F. Supp. 2d at 1128.

Surveys substituted for cross-examination.Reversed by McLaughlin v. American Co., 522 F.3d 215 (2d

Cir. 2008). Individual proof required: reliance, loss causation, injury, damages (and

limitations).

28

McLaughlin (cont’d)Plaintiffs relied upon sixteen experts, including economists who

proposed statistical and econometric analyses.John Hauser (marketing expert) conducted a “web-based conjoint

study” to determine, inter alia, what proportion of consumers relied upon health claims as a significant factor, and how consumers would react to cigarettes with different health characteristics.

Economist John Beyer proposed to perform a regression analysis to determine the price paid less the price consumers “would have paid”—again, fraud on the market—a/k/a “overcharge” or out-of-pocket loss. This is also called “price impact.”

Economist Jeffrey Harris proposed to determine a difference in the value expected and the value received (expectancy or “loss of bargain”) based on surveys asking consumers to compare their willingness to pay for light cigarettes that were not safer and light cigarettes that were safer. This is also called “loss of value.”

29

McLaughlin: ReliancePlaintiffs argued that defendants “distorted the body of

public” information, and that class members relied upon “the public’s general sense” that lights were healthier.

“The certainty that not all ‘light’ smokers chose their brands for the same reasons does not preclude a finding that an implied reduction in danger was a substantial factor in nearly all class members. See Expert Report of John Hauser, Part VIII.F.1.i (concluding based on reliable surveys that 90.1 percent of the class members based their decision to smoke ‘light’ cigarettes on health concerns).” Schwab, 449 F. Supp. 2d at 1125.

30

McLaughlin: Reliance (cont’d)

The Second Circuit rejected this theory as improper application of fraud on the market. “[T]he market for consumer goods, however, is anything but efficient

….” “Dr. Hauser came to this conclusion on the basis of a method

that determined whether, all things being equal, consumers prefer a safer cigarette. And as plaintiffs conceded at oral argument, no one who understood this question would prefer a more dangerous product to a safer one.” McLaughlin, 522 F.3d at 225, n. 6.

Individuals could have purchased for any number of reasons irrespective of perceived health advantage—lights are “cool.”

Lights were priced the same as regular cigarettes. Lights market did not decrease after adverse publicity. Also, three plaintiffs continued buying lights after they knew “the

truth.”

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McLaughlin: Loss CausationPlaintiffs relied on same theory of price inflation for

common proof of loss causation. “This argument fails because the issue of loss causation …

cannot be resolved by way of generalized proof.” McLaughlin, F.3d at 226.

Establishing price effect would require individual proof of reliance. I.e., the inflation theory is circular.

Because market did not respond to negative information, the Second Circuit found that this argument failed “as a matter of law.”

32

McLaughlin: Loss of Value Injury Theory

Note: Opinion partly conflates with price-impact theory. Not allowed under RICO precedents. Even if allowed, no reasonable means to calculate. “We are asked to conceptualize the impossible—a healthy

cigarette—and then to imagine what a consumer might have paid for such a thing.” McLaughlin, 522 F.3d at 229.

Expert’s survey evidence “pure speculation.” No showing under In re IPO that plaintiffs could support theory

with facts.

33

McLaughlin: Price-Impact Injury Theory

Even if plaintiffs’ multiple regression analysis could show the amount defendants would have had to reduce prices to account for lower demand, “plaintiffs have failed as a matter of law to adduce sufficient facts to show that the price impact model is a tenable measure of harm.” McLaughlin, 522 F.3d at 229.

No meaningful means of estimating market impact. Market failed to respond to publication of adverse information. Vague and speculative damage inquiry, contrary to RICO

precedent requiring exclusion of other causes. Plaintiffs’ expert admitted other factors also affect price.

34

McLaughlin: DamagesCourt rejected “fluid recovery” approach of

awarding aggregate “class” damages followed by “simplified proof of claim procedure” and cy pres.

Rough estimate of aggregate damages:is not authorized by Rule 23.violates Rules Enabling Act (changes substantive

law via Rule 23).deprives defendant of due process.

35

Common Impacts: In re Neurontin Sales and Mktg.

Practices Litig.Neurontin was approved for treatment of seizures

(epilepsy) and post-herpetic pain but widely used for other, off-label physiological and psychiatric treatments.

Economic loss claims by consumers and payors under RICO, New Jersey CFA, fraud, and unjust enrichment.

Alleged fraudulent promotion of seven off-label benefits.Proposed consumer and TPP classes, with subclasses

based on off-label categories.Causation problem: Which off-label prescriptions were

caused by allegedly fraudulent promotion?Plaintiffs relied upon econometric analysis to try to show

causation of “all” off-label prescriptions.

36

In re Neurontin IFirst class certification motion denied. 244 F.R.D. 89

(D.Mass. 2007).Judge Saris found econometric analysis of “plausible” way

to estimate aggregate prescriptions caused by fraudulent promotion.

But this left problems such as identifying which prescriptions/patients—class overbreadth.

Gave plaintiffs opportunity to show through “statistical proof” that essentially all prescriptions in each category were caused by fraud.

37

In re Neurontin IISecond class certification motion denied. 257 F.R.D.

315 (D. Mass. 2009).Neurontin I had relied on four cases, all of which

involved claims of price inflation affecting all class members (In re Synthroid, Schwab (McLaughlin), Zyprexa I, Aspinall).

Some expressly borrowed the theory of fraud on the market from securities cases.

“Of course, the instant suit does not involve price inflation or an efficient market.”

Court had in Neurontin I instead invited statistical proof that marketing affected all or nearly all prescriptions.

38

In re Neurontin II (cont’d)Cases decided subsequent to Neurontin I caused Judge

Saris to reconsider.Int’l Union of Operating Engineers Local N. 68 Welfare Fund v.

Merck & Co., 192 N.J. 372, 929 A.2d 1026 (N.J. 2006) (Vioxx).McLaughlin (2d Cir.).In re St. Jude Medical, 522 F.3d 836 (8th Cir. 2008).In re TJX Cos. Retail Security Breach Litig., 246 F.R.D. 389

(D. Mass. 2008).These cases:

Show defendant’s right to present evidence defeats predominance (In re St. Jude).

Confirm judicial disfavor of fraud on the market or presumption of reliance/causation in consumer fraud cases.

Mandate close scrutiny of expert opinions for class certification (In re New Motor Vehicles).

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In re Neurontin II (cont’d)Statistical analysis of prescription, sales, and detailing data by

Prof. Rosenthal (economist) purported to calculate, by indication, percentages of prescriptions caused by fraud.

For those indications with less than substantially all prescriptions caused by fraud, certification denied (individual inquiry required).

For prescriptions for indications >99% caused by fraud, individual inquiry not required, if Dr. Rosenthal’s “methodology can withstand close scrutiny.”

But opinion failed to establish commonality of proof of causation.

Problems included:Faulty assumptions that all detailing was fraudulent.No off-label detailing of named plaintiffs’ doctors.Failure/inability to account for factors other than fraudulent

detailing.Individuality of TPPs and their knowledge and reimbursement

decisions.

40

Common Impact: In re ZyprexaAlleged scheme of off-label marketing.Proposed TPP and consumer classes.Claims under RICO and state consumer statutes.Judge Weinstein certified a national TPP class under

RICO, but neither an individual consumer class nor state consumer law claims. In re Zyprexa Prod. Liab. Litig., 253 F.R.D. 69 (E.D.N.Y. 2008).

Reversed by Second Circuit.

41

In re Zyprexa, (District Court)Plaintiffs relied upon economists’ statistical analyses

purporting to show class-wide price effect (“excess price”) and quantity effect (“excess sales”) based on combinations of “yardstick” and before/after market comparisons.

“Here the total fraud resulted in an increased price as in securities cases, so the fact that some doctors, patients or others were aware of the fraud is irrelevant. Without the fraud the price would have been lower to all payors.” In re Zyprexa, 253 F.R.D. at 195.

42

In re Zyprexa (2d Cir.) “Excess price” analysis could not provide common proof of

but-for (transactional) causation, because drug pricing is inelastic.*proximate (direct) causation, because

alleged reliance by physicians is independent of negotiation and payment by TPPs (advised by PBMs)

variations in price negotiation by TPPs showed that the alleged chain of causation was incomplete.

“Excess sales” theory could not provide common proof of causation because, e.g., it assumed away all other information and factors affecting

prescriptions.TPPs continued to pay for Zyprexa.TPPs probably paid for different percentages of off-label

prescriptions.some prescribing doctors not misled. it ignored alternative prescriptions and costs, some of which could

even have cost more.

43

Common Impacts: RhodesRhodes v. E.I. Du Pont de Nemours and Co., 253 F.R.D. 365

(S.D.W.V. 2008)Medical monitoring claim based on contamination of drinking

water with C-8.Focus on three elements

Significant exposureRisk significantly increased over general populationReasonable necessity of monitoring

Plaintiffs offered two expert opinions.Dr. Gray, toxicologist, performed a “risk assessment” purporting to

show: a safe exposure level (regulatory/conservative) that the class incurred a increased risk of serious disease.

Dr. Levy, physician and epidemiologist, performed an epidemiological survey and opined that class was at a significantly increased risk and should have monitoring.

44

Rhodes (cont’d)Significant exposure

Risk assessment failed to show commonality because the general population exposure (background) remained unknown.

It also failed because it focused on the level of exposure necessary to cause health affects, not the level of exposure that would be significant relative to background exposure (conflating legal elements).

The court did not mention individual background exposure, another typical flaw in such certification arguments.

45

Rhodes (cont’d)Significantly increased risk

Dr. Gray’s risk assessment failed to establish commonality because it only purported to establish a

(conservative) level of safe exposure, and not a level of unsafe exposure causing significantly increased risk.

It also failed because a risk assessment cannot provide common proof that any individual suffered significantly increased risk (presumably because of individual background exposure).

Dr. Levy’s epidemiological survey failed to establish commonality because it showed only that C-8 could

cause some diseases, not a significantly increased risk of class members.

It also failed because it relied on preliminary and insufficient data (hypothesis generation rather than testing).

Even if given full credence, the aggregate evidence would not show class-wide significantly increased risk (again, factors like individual background could not be accounted for).

46

Rhodes (cont’d)Need for medical monitoring

“Dr. Levy’s testimony is not relevant proof of the proposed class’ need for medical monitoring because Dr. Levy misunderstands the medical monitoring relief established in Bower.” 253 F.R.D. at 380.

He instead proposed a public health monitoring program not based on conclusive epidemiological data and deferring individualized assessments of need until implementation. This is a prophylactic and precautionary program, not a tort remedy for individuals who have proved the elements of a claim.

47

Part III – Practical tips on presenting and challenging statistics

48

Challenging StatisticsDaubert challenges

The expert is not qualifiedThe statistical model is not soundThe methodology is flawedThe underlying data is unreliableWhat is the applicable Daubert standard on class

certification?Other challenges

The expert opinion is not relevant to any issue to be decided at trial.

The opinion does not show that an issue is susceptible to common, class-wide proof.

49

General Considerations Does the statistical evidence satisfy Daubert? Even if it satisfies Daubert standards, does it hold up to rigorous analysis? Does it satisfy the proponent’s burden of proof (resolution of conflicting

opinions) that Rule 23’s requirements are satisfied? Does it show what it purports to show? Are there inferential gaps in the analysis itself? Does it leave data or factors unaccounted for? Is circular reasoning involved, or does it purport to prove what it actually

assumes? Are there conceptual gaps between the data or conclusions and the true

requirements of Rule 23? Does it show that the answer to a question necessarily is the same for all

class members, or does it merely generalize that the answer might be the same for some of them?

Does it show that all class members were similarly affected, or only that each one might have been?

Is it consistent with governing substantive law? Is there a conceptual gap between the evidence and the proof

requirements of substantive law?

50

Common Fact Patterns to Watch Out For

Single policy or practice (Dukes) Does a single policy exist? Is there a way to prove a causal link between the policy

and some alleged harm? Can the causal link be resolved by reference to

common, classwide evidence.

Mass reliance—ask whether legal theory is such that individual reliance is not

required (if so, still have to consider the separate question of causation)

Reliance question can be both proved and resolved by reference to common evidence.

51

Common Fact Patterns to Watch Out For

(cont’d)Winners and losers

Some class members are actually better off as a result of the alleged practice.

Subclasses may cure this problem, but problem might be in identifying who goes in which category.

Trial by formulaStatistics used to aggregate damages

and apportion based on

52

Tools for challenging statistical evidence

Assumption vs. conclusion – Does the analysis prove a fact to be true, or does it assume the fact is true?

Underlying data – Where does it come from? Is it complete? Is it being interpreted correctly?

Methodology – Is it peer reviewed? Has it been discredited?

Relevance – Does the analysis address the right issue?Sample size – Is it big enough to be predictive?Error rate – How accurate are the predictions?Other logical fallacies

Is the analysis circular?Are variables ignored?

53

Tips for Dealing With Experts

54

Draw Inferences (optional)

Draw Inferences (optional)AnalyzeAnalyzeCollect

DataCollectData

Major Strategic Considerations

How collected? Trusted source?

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

Valid? Recorded properly? Categories

appropriate? What is the non-

response rate (survey)? Why?

Can the results be generalized?

How are charts/graphs presented?

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

Do analyses reach different opinions?

What variables were left out?

Did the expert answer the right question?

How do I estimate whatever is missing?

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

54

Common Statistical Flaws Illusory commonality

When (even reliable) statistics only purport to answer a question for X or X% of a class, or show that X or X% of a proposed class is affected, commonality does not exist (indeed, is disproved). Discrimination (Dukes) Consumer fraud (Zyprexa, Neurontin) Breach of contract (e.g., timeliness of payment)

Overlooked factors and intervening causes. Alternative drugs might be more expensive for some. Some people smoke lights for flavor or because they are “cool.”

Circularity/Assumed Reliance When an econometric analysis purportedly shows that causation can be

proved on a class-wide basis through a “price effect,” the analysis may assume reliance or causation rather than prove them.

Erroneous assumptions All off-label marketing is fraudulent (legal/factual error). Third-party payors have similar rates of reimbursement for off-label

prescriptions (factual error). All class members were unaware the drug was unapproved (factual error).

55

Parting ThoughtsHow far should the court go when certification and

merits questions overlap?There is often an overlapDukes says that the court must answer the certification

questions by resolving disputed facts if necessary.Should the Court resolve conflicting expert opinions?

According to Dukes, yes.Consider arguments about the practical consequences of the

Court’s failure to resolve disputed factsWhat about the defendant’s use of statistics?

disprove existence of common facts.Show variances between class members.E.g. Survey shows that not all customers found information

important when information forms basis for material misrepresentation or omission claim.

56

For Further Study David H. Kaye & David A. Freedman, Reference Guide on Statistics, Reference

Manual on Scientific Evidence 2d Ed. (Federal Judicial Center 1981) (http://www.fjc.gov/public/pdf.nsf/lookup/sciman02.pdf/$file/sciman02.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://www.columbialawreview.org/assets/pdfs/109/8/Cheng.pdf)

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/extImage/PUB_Wage_Hour_Litigation_1109_final.pdf)

Dukes, plaintiff’s Expert Dr. Richard Drogin’s Statistical Report (http://www.walmartclass.com/all_reports.html)

Dukes, class certification (http://www.walmartclass.com/staticdata/walmartclass/classcert.pdf)

Michael O. Finkelstein and Bruce Levin, Statistics for Lawyers: Second Edition (Springer, 2001)

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)

57

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