Presenting a live 90-minute webinar with interactive Q&A
Statistics in Class Action Litigation:
Admissibility and the Impact of Wal-Mart v. Dukes
Crafting Provisions to Allocate Risk, Avoid Common Pitfalls, and Minimize Liability
THURSDAY, OCTOBER 6, 2011
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Today’s faculty features:
Paul G. Karlsgodt, Partner, Baker Hostetler, Denver
Brian A. Troyer, Partner, Thompson Hine, Cleveland
Justin Hopson, Director, Hitachi Consulting, Denver
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Statistics in Class Certification
Proceedings
What they’re good for, and how to
discredit them
Paul Karlsgodt, Baker Hostetler
pkarlsgodt@bakerlaw.com
303.764.4013
Brian Troyer, Thompson Hine
brian.troyer@thompsonhine.com
216.566.5654
Justin Hopson, Hitachi Consulting
5
jhopson@hitachiconsulting.com
303.813.6057
Copyright 2011
Paul Karlsgodt
Brian Troyer
Justin Hopson
All 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”*
Statistics
Descriptive
Statistics
Describes relationships, correlations, events
Example: batting average, transit daily ridership, # of
calls
Common Terms: counts, summaries, %, average (mean,
median, mode), variation, range
Use and Limitations: Describes observations. Easily
audited
Inferential Statistics
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 72K
Common Terms: random sample, hypothesis testing,
confidence interval, significance
Use and Limitations: Estimate “how likely?” based on what
you’ve observed. Regression analysis subject to
estimation error
*(Finkelstein and Levin, p. 1)
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 sociological
 Economic and financial
 Epidemiological
 Pollution and toxic exposure
 Sales and prescriptions
 Product 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 practice
 As 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 factors
 E.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 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 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 claims
 Plaintiffs 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 injury
 Biased 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, but
 he could not determine with any specificity how regularly
stereotypes played a meaningful role, and
 could 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?
19
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
 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 analysis
 Was it responsible for the overall
differences ?
 What is left in the set of observations for,
“Wal-Mart, not Sam’s Club?”
21
1 Class Cert p. 25
Statistical Concepts in Dukes (cont’d)
• 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
 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
What Else?
Region
Personal
Traits
Dept
Store
Discretion
Gender
Personal decisions
Age
Family Situation
Mobility
Workload Expectations
Management
Behaviors
23
Policies &
Procedures
Full-/part-time
Tenure
Role
Previous job
Performance
Employment
Status
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.”
Paraphrasing: While disparity may
exist, the underlying root causes are
likely to be different among class
members
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: Reliance
 Plaintiffs 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




31
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.”
McLaughlin: Loss Causation
 Plaintiffs 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




34
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.
McLaughlin: Damages
 Court 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





36
(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.
In re Neurontin I
 First 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 II
 Second class certification motion denied. 257 F.R.D.




38
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.
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).
39
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 Zyprexa
 Alleged 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




43
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.
Common Impacts: Rhodes
 Rhodes 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 exposure
 Risk significantly increased over general population
 Reasonable 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 Statistics
 Daubert challenges
 The expert is not qualified
 The statistical model is not sound
 The methodology is flawed
 The underlying data is unreliable
 What 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
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50
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?
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 formula
 Statistics used to aggregate damages
and apportion based on
52
Tools for challenging statistical evidence
 Assumption vs. conclusion – Does the analysis prove a
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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
Collect
Data
Draw
Inferences
(optional)
Analyze
Major Strategic Considerations
 How collected? Trusted source?
 Is the method / measurement
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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?”
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 Thoughts
 How far should the court go when certification and
merits questions overlap?
 There is often an overlap
 Dukes 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 facts
 What 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
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57
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/statisticssurge-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)
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