For thousands of years, human language has provided us with beautiful and complex ways of sharing important ideas. At the same time, language can derail attempts to communicate even the most basic pieces of critical information. We testers are the heralds of vast amounts of data, and it is our responsibility to use that data wisely—or not at all. Whether you are the information presenter whose voice not being heard or the information receiver who needs ways to spot errors in the message, a review of how metrics can be skewed—through ignorance, bias, or malice—provides us with the ability to think beyond content to the ethics of presentation. Using scientific research, case studies, and an interactive “try it yourself” experience, Deborah Kennedy explores both sides of metric—the good and the bad. Take away key insights to present your message without built-in barriers and arm yourself against disreputable attempts to sway you with unwisely presented data.

1. !!
T6
Metrics!
10/16/2014!9:45:00!AM!
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Testers, Use Metrics Wisely or
Don’t Use Them at All
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Presented by:
Deborah Kennedy
Aditi Technologies
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Brought(to(you(by:(
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340!Corporate!Way,!Suite!300,!Orange!Park,!FL!32073!
888G268G8770!H!904G278G0524!H!sqeinfo@sqe.com!H!www.sqe.com

2. Deborah Kennedy
Aditi Technologies
A Client Engagement Manager with Aditi Technologies, Deborah Kennedy leads
onshore delivery for a large financial services client. With almost twenty years of
experience in core development and testing, Deborah spent much of that time
working as a process improvement consultant, specializing in revitalizing and
streamlining QA departments as well as bringing more awareness to IT’s role in
executive messaging. Deborah has collected a wealth of information about how
team members perceive information, the power of metrics in the right hands, and
the payoff of understanding the impact of “the small details.”

3. Metric'Ethics
Deborah(Kennedy(
StarWest(2014(
Topics
• What(are(our(Ethical(Responsibili<es?(
• Decep<on(through(Ignorance(
• Decep<on(through(Bias(
• Decep<on(through(Malice(
• Do(you(trust(these(graphs?(
"Figures(oDen(beguile(me,(par<cularly(when(I(have(the(arranging(of(them(myself;(in(which(case(the(remark(aLributed(to(
Disraeli(would(oDen(apply(with(jus<ce(and(force:('There(are(three(kinds(of(lies:(lies,(damned(lies,(and(sta<s<cs.'“((
( ( ( ( ( ( ( ( ( ( ( (
( ( ( ( ( ( ( ( ( (S(Mark(Twain,(1906(

4. What'are'our'Ethical'Responsibili6es?
Business'Ethics'
• Make(Money(
• Improve(Company(Image(
• Benefit(Stakeholders(
• Grow(Compe<<ve(Advantage(
• Increase(Consumer(Confidence(
(
Personal'Ethics'
• Intellectual(Growth(
• Honesty(
• Credibility(
• Dependability(
• Loyalty(
• Integrity(
What(happens(when(someone(is(caught(
viola<ng(those(Ethics?(
Business'Ethics'
• Causes(Loss(of(Money(
• Damage(to(Company(Image(
• Injury(to(Stakeholders(
• Diminishes(Compe<<ve(
Advantage(
• Decreases(Consumer(Confidence(
• Implies'a'general'expecta6on'of'
Decep6on'
Personal'Ethics'
• Hinders(Intellectual(Growth(
• Highlights(Dishonesty(
• Reduces(Credibility(
• Introduces(Instability(
• Uncovers(Disloyalty(
• Implies'a'general'expecta6on'of'
Decep6on'

5. Decep6on'through'Ignorance
• Lack(of(Clarity/Accuracy(
• Too(Much(Informa<on(
• Repor<ng(the(Wrong(Data(
Lack'of'Clarity/Accuracy
These(two(graphs(show(the(same(
data(for(different(<me(ranges.(
X

6. Too'Much'Informa6on
X X
Repor6ng'the'Wrong'Data
X

7. Decep6on'through'Bias
• Skewing(the(Graph(
• Illusionary(Correla<on(
• Sta<s<cula<on(
Skewing'the'Graph
0(
5,000,000(
10,000,000(
15,000,000(
20,000,000(
25,000,000(
30,000,000(
June(
July(
Aug(
Sept(
Oct(
Nov(
Dec(
Govt.'Pay'Roll'is'STABLE!'
19,400,000(
19,420,000(
19,440,000(
19,460,000(
19,480,000(
19,500,000(
19,520,000(
19,540,000(
19,560,000(
19,580,000(
19,600,000(
19,620,000(
June(
July(
Aug(
Sept(
Oct(
Nov(
Dec(
Govt.'Pay'Roll'is'UP!'

8. Illusionary'Correla6on
Post%hoc%ergo%propter%hoc:("aDer(this,(therefore(because(of(this"(
Sta6s6cula6on
Year'1' Year'2'
Bread( 5¢( 10¢(
Milk( 20¢( 10¢(
0%(
100%(
200%(
Year(1( Year(2(
Bread(
Milk(
Avg(A(
0%(
100%(
200%(
Year(1( Year(2(
Bread(
Milk(
Avg(B(
Sta.s.cal%+%Manipula.on(
Has(the(Cost(of(Living:(
• Gone(Up?(
• Gone(Down?(
• Stayed(Level?(

9. Decep6on'through'Malice
• Intent(to(Distract(
• Intent(to(ShiD(Blame(
• Intent(to(Save(Face(
Intent'to'Distract

10. Intent'to'ShiG'Blame
Intent'to'Save'Face

11. Do'you'trust'these'graphs?
#1

12. #2
#3

13. #4
R1(
#5

14. #6
References
Huff,(Darrell.(How$to$Lie$with$Sta,s,cs.(New(York:(Norton,(1993.(Print.(
Pleat,(Zachary.("Today(In(Dishonest(Fox(News(Charts."(Media$Ma1ers$for$
America.(MediaMaLers.org,(12(Dec.(2011.(Web.(<hLp://mediamaLers.org/
blog/2011/12/12/todaySinSdishonestSfoxSnewsScharts/185162>.(
Gary_C.("BAD(NEWS:(IPhone(Beats(Android(2:1(in(MocoSpace(Future(Buyers(
Social(Graph."(Eurodroid$RSS.$Eurodroid,(29(Apr.(2010.(Web.(11(Feb.(2014.(
<hLp://eurodroid.com/2010/04/29/badSnewsSiphoneSbeatsSandroidS21SinS
mocospaceSfutureSbuyersSsocialSgraph/>.(
Robbins,(Naomi.("Winner(of(the(Bad(Graph(Contest(Announced."(Forbes.(Forbes(
Magazine,(30(May(2012.(Web.(11(Feb.(2014.(<hLp://www.forbes.com/sites/
naomirobbins/2012/05/30/winnerSofStheSbadSgraphScontestSannouncedS2/>.(

15. Thank'You
Deborah(Kennedy(
deborahk@Adi<.com(