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AnElectoralSysteminCrisis

luluFries’dat&AnselmoSampietro

incollaborationwithFritzScheuren

July18,2016

Havingconfidenceinourelectionsiscentraltoourfaithinourgovernmentandallofthedecisionsthatwemakecollectivelyasanation.Butarethecandidateswhowintheonesweactuallyvotefor? AlargeandgrowingbodyofresearchprovidesconvincingevidencethatU.S.electronicvotingequipmentinmanyareasthroughoutthecountryisnotcountingthevotesaccurately.Thiscouldbeduetomalfunctionsincomputerequipmentthatin43statesisoveradecadeold,andlongpastitsnatural

life.However,inmanycases,theevidencestronglysuggeststhatfraudisthelikelyexplanation.Theseproblemshavebeenoccurringsinceatleast2004,andarecertainlypresentinthecurrent2016presidentialprimaries. Thedocumentationconsistsofstatisticalgraphsanalyzingdatafromfivepresidentialcycles,aswellasoff-yearracesfromacrossthecountry.Thedataillustratesthatthereareunusuallylargediscrepanciesbetweensmallprecinctandlargeprecinctelectionreturns,andnoticeabledifferencesbetweenhand-

countedandmachine-countedprecinctresults.Eveninisolation,thedatagivescauseforconcern.Thestatisticalevidenceisreinforcedbyphysicalevidenceandcongressionalhearings:manualrecountsthatdonotmatchthetotalsofthemachinesbeingaudited;andtestimonyunderoathaboutdirect

knowledgeoftamperingwithelectronicvotingequipment.

Weexaminedtheelectionresultsofthe2016presidentialprimaries,andfoundirregularitiesintheoverwhelmingmajorityofthetwenty-onestatesthatweanalyzed.Thedataindicates,inparticular,thatthetotalsreportedintheDemocraticracebetweenHillaryClintonandBernieSandersmaynotbe

correct.Instateafterstate,independentexaminationbytwoseparateanalystsfoundsuspectstatisticalpatternsgivingClintoninflatedpercentages,thatinalllikelihoodarenotfullybasedonactualvotes;andleavingSanderswithwhatappeartobeartificiallydepressedtotals.

Thedifferencebetweenthereportedtotals,andourbestestimateoftheactualvotetotals,varies

considerablyfromstatetostate.However,thesedifferencesaresignificant—sometimesmorethan10%—andcouldchangetheoutcomeofthe2016Democraticpresidentialprimary.Wefoundirregularitiesinthe2016Republicanpresidentialprimaryaswell,andwhileconcerning,wedonot

believetheyarelargeenoughtochangetheoutcomeofthatrace.

FritzScheuren,amemberofthestatisticsfacultyatGeorgeWashingtonUniversity,andaformerpresidentoftheAmericanStatisticalAssociation,hasbeenacollaboratorinthisresearch.Examiningthedatafromthestudy,Scheurensaid,“Asastatistician,Ifindtheresultsofthe2016primaryvoting


unusual.Infact,Ifoundthepatternsunexpected[andpossiblyeven]suspicious.Thereisagreaterdegreeofsmoothnessintheoutcomesthantheroughnessthatistypicalinraw/realdata.”

Itisimportanttonotethatthefactthatacandidatebenefitsfromirregularitiesdoesnotimplythata

candidateisresponsibleforthem.

InJanuary2014,ThePresidentialCommissiononElectionAdministrationpublishedareportstating,“PerhapsthemostdirewarningtheCommissionheardinitsinvestigation…concernedtheimpendingcrisisinvotingtechnology.Well-knowntoelectionadministrators,ifnotthepublicatlarge,this

impendingcrisisarisesfromthewidespreadwearingoutofvotingmachinespurchasedadecadeago(p.62.)”Thisreportwasissuedovertwoyearsago,butunfortunatelyverylittlehasbeendonesincethentorectifytheproblem.Sotheissueswearereportinghere,ofsecurityproblemsonoldandfailing

machines,arenotsurprising.Howeverwedidfindsecurityissueswithevennewerelectronicvotingequipment,suchasthemachinesinNewYorkState.

Atacongressionalbriefingonvotersuppression,heldonApril21,2016,Rep.HankJohnson(D-Georgia)expressedgraveconcernaboutthesecurityofthevotingequipment:“Thereisaveryinsidious,

treacherousanddeceitfulmethodofvotersuppression,andithastodowiththeintegrityofthevotingprocessitself…onepossibility,andIthinkit'saverygoodone,isthatsomeone'smanipulatingthecountingofthevotes.Someoneishackingintothesecomputersthattabulatethevotes."

AnEnvironmentofCorruption

Theportraitofanelectoralsystemincrisisisfurthersupportedbyreportsfromelectionintegrity

organizations,mediaoutlets,andindividualsonsocialmediathatvotingisincreasinglytakingplaceinacorruptenvironment.Thiscontextualevidenceofvoterspurgedfromtherolls,registrationslostinthe

mail,partyregistrationsbeingchangedwithoutavoters’knowledgeorintent,votersbeingsentincorrectballots,ashortageofballots,pollingplacesbeingclosed,discouraginglylonglinesintargetedprecinctsandstates,anddisturbinglylargedisparitiesbetweeninitialexitpollsandofficialresults,lends

credencetotheargumentthatifoneformoffraudisalreadyinplay,anotherformoffraudismoreplausible.ThisinformationisbeingaggregatedbyelectionintegritygroupssuchasElectionJusticeUSA,throughvotertestimonialsandlawsuitsthatareinprogressaroundthecountry.

Figures1and1Aareexamplesofdisenfranchisedvotersfromthe2016presidentialprimaries.Stories

likethesehavebeenubiquitousinmanystates,includingArizona,NewYorkandCalifornia.MoreoftheseinstancesaredocumentedinthisarticleonHeavy.com.


Fig.1—Facebookpost:BeckyDillon,aCaliforniavoterforcedtovoteviaprovisionalballot,June7,2016

Fig.1A—CourtorderrequestofChloePecorino,afirst-timeNewYorkvoterwhoseregistrationwaslostinthemail.ShewasunabletocastaregularballotintheDemocraticpresidentialprimarydespite

requestingacourtorderonApril19,2016

DocumentscourtesyofElectionJusticeUSA


ThisispartofthetextoftheaffidavitthatMs.PecorinofiledwithElectionJusticeUSA:

“I,ChloePecorino,remainunregisteredinthestateofNewYorkasaDemocrat.IhavemademultipleattemptstoconfirmmyvoterregistrationwiththeDMVandtheBrooklynBoardof

Elections…IregisteredthroughachangeofaddressformwiththeDMVinearlyMarch.IregisteredasaDemocrat.IknowtheDMVreceivedtheformbecausemynewaddressisonfileasofMarch18th,2016.IneverreceivedconfirmationaboutmyregistrationwhichiswhyI've

beencallingtheBrooklynBoardofElectionsforthepastthreeweeks.”

SomeofthesetamperingissuesmayberelatedtoaDecember2015massivedataleak,reportedbyresearcherChrisVickery,thatincluded“personal,public,andsomenon-publicinformationon191millionregisteredvoters.”AccordingtoaForbesarticlebyThomasFox-Brewster,Vickeryfound“300GB

ofvoterdata,whichincludesnames,homeaddresses,phonenumbers,datesofbirth,partyaffiliations,andlogsofwhetherornottheyhadvotedinprimaryorgeneralelections.Thedataappearstodatebackto2000.”Forbesstatedthattheinformationwasopenlyavailableonline,andthat“Itwouldappear

everyregisteredUSvoterisincludedintheleak.”

HowWouldWeKnowiftheVotingMachinesWereNotCountingtheVotesCorrectly?

Thebestwaytocheckwouldbetocounttheballotsbyhand,orexamineanypaperorelectronictrailavailableinathoroughandpublicaudit.Thisisnothappening.AccordingtoadatabasecompiledbyCitizensforElectionIntegrity,only12statesrequireapost-electionauditof“everycontestandballot

issuevotedontheballot.”Eventhatestimateisgenerous.Forexample,NewYorkislistedasoneofthose12states,butin2015,itspost-electionauditlawwaschangedfromrequiringathree-percenthandcountaudittosimplyrunningthoseballotsthroughthemachineagain.AlanGoldston,aNewYork

electionlawconsultant,said“thisisnotarecountatall.”

Sotheshortanswertothisquestionis:Wewouldn’tknowifthetotalswerewrong.Orwouldwe?Wouldtherebeotherindicationsthatthemachine-countisnotaccurate?

Evidence

Ifvoting-machineresultswereinaccurateonaregularbasis,therewouldbesomeevidenceofit.One

indicatorwouldbethatvotescountedbymachineswouldgivedifferentresultsthanvotescountedbyhand.Infact,thisisnowbeingseeninelectionsalloverthecountry.

Inthe2016DemocraticprimaryinKingsCounty,NewYork(Brooklyn,)agroupofaffidavitballotswerehand-countedbyagroupofvolunteers.Comparingthehand-countswiththemachine-counts,thereisa

noticeabledifference(Figure2).Ineverysingleassemblydistrictweexamined,exceptone,HillaryClintonperformedbetterwhenthevoteswerecountedbymachine.Thisisasmallsampleoftheoverallballotscast,buttheconsistencyoftheresultsmakesaconvincingcasethatsomethingisamiss.


Fig.2—Hand-countedballotsshow aconsistentlyhigherreturnforSandersinthe2016NewYorkpresidentialprimary

GraphbyAnselmoSampietro

Comparisonshavebeenmadepreviouslybetweenvotingresultsinhand-countedprecinctsandmachine-countedprecincts.Whentherehavebeendiscrepancies,theyhavebeenpassedoffasthe

resultofdemographics.Thisisareasonableconcern,sinceitispossiblethatvotersofaparticularpoliticalperspectivecouldtendtochooseaparticulartypeofvotingequipment.However,inthisinstance,becausethetwosamplesets(hand-countedandmachine-counted)arefromidentical

precincts,withvotersparticipatinginthesameelectiononthesameday—thereisnodemographicvariabletotakeintoaccount.

Theseaffidavitballotsarefromvoterswhowerenotabletovotebyregularballot.IntheNewYork2016primary,over120,000voterswerepurgedfromtherollsinBrooklynalone,andalargenumberofvoters

alsohadtheirvoterregistrationchangedwithouttheirknowledgeorintent.

Sandersvoterstendtobeyoungerandmoreindependent,soonemightthinkthattheywouldbelesslikelytoregisteraheadoftime,andmorelikelytoshowupintheaffidavitsample.Howeveroftheover120,000affidavitballotscast,onlyabout30,000wereactuallycertifiedandcounted.Itisthatfinal

“approved”subsetbeingcountedinourstudy.ThosevoteswouldhaveonlyincludedofficiallyregisteredDemocrats,notindependentsorlateregistrants.Thoseofficiallyapprovedaffidavitvotes,whencountedbyhand,areshowingaconsistentlyhigherpercentageforSandersthanwhenthevotes

arecountedbymachine.Therearetwopossibleexplanationsforthis.Oneisthatthemachinesarecountingthevotesdifferently.Theotheristhatthevoterswhowereforcedtouseaffidavitballotswere

targetedSandersvoters.Possibly,bothofthesefactorsareatwork.Eitherway,thedataindicatesthefootprintofmanipulationintheelection,andcallsintoquestionthevalidityofthereportedresults.


Figure2AshowstheresultsofarecountinHillsboroughCountyinthe2008NewHampshireDemocratic

primary.Thereweredifferencesinalmosteveryprecinctbetweentheoriginalmachinecountandthemanualrecount.

Fig.2A—2008NHDem.presidentialprimary Themanualre-countshowslargediscrepancieswiththeoriginaltotals

Source:TheBradblog

Inthe2016WisconsinandMassachusettspresidentialprimaries,therehavealsobeenstarkdifferences

betweenthecandidates’percentagesinhandcountandmachinecountprecincts.


Hacking

Theexamplesprovidedabove,showingdifferencesbetweenhandcountsandmachinecounts,cannotbeexplainedbydemographics.Ifthediscrepanciesarenotduetodemographics,thereiseithersome

issuewiththevotingmachinesorthehandcounts.Informationsurroundingthosetwoprotocolssuggeststhattheproblemwouldbewiththemachinesandnotthehandcounts.

Inhiswell-researchedpostontheoddresultsofthe2016MassachusettsDemocraticprimary,TheodoredeMacedoSoarespointsoutthatCanada,Australia,Denmark,France,Ireland,Italy,Sweden,andSpain

areamongthe59countriesthatrelyonhand-countedpaperballotstodeterminetheirresults.Incontrast,concernsaboutsecurityandaccuracyhaveplaguedelectronicvotingmachineswherevertheyhavebeenimplemented.

In2009,Germany’shighestcourtbannedtheuseofcomputersinthevotingprocessamidstconcerns

thattheprocesswasnottransparent.JonathanSimon,aHarvard-educatedattorneywhoistheco-directoroftheElectionDefenseAlliance,saysonhiswebsite,“There’svirtualunanimityamongtheexpertswhohavestudiedelectronicvotingmachinesthatinsidersorhackerscanchangetheresultsof

electionswithoutleavingatrace.”HecitesstudiesfromJohnsHopkins,Princeton,UniversityofMichigan,TheBrennanCenterForSocialJusticeatNYU,thestatesofCaliforniaandOhio,andeventheU.S.GovernmentAccountabilityOfficetobackuphisclaim.

J.AlexHaldermanteachescomputerandnetworksecurityattheUniversityofMichiganandhas

successfullycompromisednumerousvotingsystems.Hepaintsavividandunnervingdescriptionofonehack,“Within36hoursofthesystemgoinglive,ourteamhad…almosttotalcontroloftheserver

software,includingtheabilitytochangevotesandrevealvoters’secretballots.”Haldermanpointsoutthatthethreattoourelectionscouldbecomingfrompoliticalplayersinsideourcountry—orevenfromabroad.TestifyingbeforetheD.C.BoardofEthicsandElectionsaboutoneofhismanyvoting-machine

hacks,Haldermanmakesitclearthattherisksarenottheoretical.“WhilewewereincontrolofthesesystemsweobservedotherattackattemptsoriginatingfromcomputersinIranandChina.Theseattackerswereattemptingtoguessthesamemasterpasswordthatwedid.Andsinceitwasonlyfour

letterslong,theywouldlikelyhavesoonsucceeded.”

YoucanviewDr.Haldermanperformingasuccessfulhackonavotingmachineinthisclipfromthedocumentary“HollerBack—[not]VotinginanAmericanTown.”

WhoisResponsible?

Atthispoint,weareunabletosaywhomightberesponsibleforanydatabreachestothevotingequipment.Therecouldbeanynumberofindependentplayerswhowouldbenefitfromthevictoryofa


particularcandidateandwouldbewillingtotakeactiontoinfluencetheresults.Ourresearchalsoindicatesthatinsomeelectionsthefootprintofmorethanoneunofficialplayerisevident.

WhatisthatPattern?

Wearenowgoingtoutilizeadifferenttechniqueinoursearchforevidenceofelection-result

irregularities.Wewillfocusprimarilyonstatisticalirregularities,andbythatwemeanresultsthatdefystatisticallaws.Thetechniqueweareusingiscalledthe“CumulativePrecinctVoteTallyChart,”alsoknownasaCVT(cumulativevotetally)graph,orCVS(cumulativevotestudy).TheCVTgraphhasa

numberofadvantagesinexaminingelectionresults.Exitpollsandthediscrepanciesbetweenthemandtheofficialresultshavereceivedalotofattentioninthe2016presidentialcycle.TheCVTgraphusesactualvotes,andnotpost-votesurveys,sotheresultsaremoreconclusivethanexitpollcomparisons.

Secondly,althoughitisbasedonsolidstatisticalprotocols,itdoesnotrequirestatisticaltrainingtounderstand,andisthereforesuitedtohelpingboththestatisticalandthenon-statisticalcommunityfullygraspthelargedistancebetweenthevotetotalscurrentlybeingreportedandthestatisticalnorm.

Third,statisticianBethClarksonexplainsthatelectiondatatendstohavealotof“noise.”ShelikestheCVTanalysisbecause,“Itallowsyoutoseeatrendthatisdifficulttospotinanoisydataset.“

ThetechniqueisbasedontheLawofLargeNumbers(Figure3.)Investopediaprovidesastraight-forwardexplanation,“Aprincipleofprobabilityandstatisticswhichstatesthatasasamplesizegrows,

itsmeanwillgetcloserandclosertotheaverageofthewholepopulation.”

Fig.3—IllustrationoftheLawofLargeNumbersusingrollsofasingledie WikigraphbyNYKevin


Interpretingthislawforelections,thesamplesizeisthenumberofvotes,andthemeanisthecandidate’spercentage.Inpracticewhathappensisthatthelargerasampleofvotesthatyoucollect,

thecloseryoushouldgettothecandidate’saveragepercentageofsupportinthatlocale.Thisiseasytoseeinaction.Ifyouandyourfriendssupportacandidate,itdoesnotmeanthecandidatehasthatlevelofsupportoverall.Butabroadersampleofvotersinyourcommunitywillgenerateamoreaccurate

pictureofthecandidate’sactuallevelofsupport.Thisisthebasicconceptbehindallpolling;andthisistheprinciplethatisthefoundationfortheCVTgraphs.

UsingCVTgraphstodemonstrateirregularitiesinelectionresultshasbeencontroversial.Asaresult,we’regoingtorelaythemethodologyandbackstoryofthetechnique,confirmthatitaccurately

demonstratesastatisticalpatternthatexists;investigatewhetherthereisademographicexplanationforthatpatternandexplorewhat,ifanything,thepatternsignifies.

TheCVTgraphshowstheprecinctsaddedtogethercumulativelyfromthesmallesttothelargestalongtheX-axis.OntheY-axisitshowsthetwocandidates’percentages(Figures4and4A).Intheseinstances

from2000and2004,theCVTgraphresemblesthegraphillustratingtheLawofLargeNumbers.Becausetheprecinctsareaddedtogethercumulativelyasyoumovefurtherrightonthegraph,itbecomesharderandharderforanyindividualprecincttoovercometheaveragepercentageofallthevotesthat

havebeenaddedupsofar,andthedatatendstochartasaflatline,atleastitdiduntil2004.Sometimearound2004,orpossiblyalittleearlier,otherpatternsemergethatwewilldiscussshortly.

Fig.4—2000AlachuaFloridaDemocraticpresidentialprimaryGraphbyAnselmoSampietro


Fig.4A—2004AlachuaFloridaDemocraticpresidentialprimary(Thesecondgraphisazoomed-inviewofthebottomhalfofthefirstgraph.)

fromRonPaulForumby“Liberty1789”

Youmaybesurprisedtoseesomeoftheabovegraphscreditedto“Liberty1789.”Oneofthereasons

forthecontroversysurroundingtheCVTgraphisthatitwasdevelopedontheInternetbynon-professionalsoutsideofacademicstatisticalcirclesbyforumuserspostingunderpseudonyms.Youcouldn’treallyaskforaworsestartforastatisticalmethodtobetakenseriously.

Thegraphwasfirstusedin2012byagroupofRonPaulsupporterswhohadstronganalyticaland

engineeringskills.ThefirstformalpresentationofthetechniquewasmadebytwoofthoseRonPaulsupporters,ChoquetteandJohnson,intwoonlinepapers.ButaccordingtoChoquette,theideaofchartingtheprecinctsfromthesmallesttothelargestwasconceivedbyanengineernamedPhilEvans,

whousedtheonlinehandle“TheMan.”

Evansremembersthenighthefirststartedtonoticeanunusualpatternintheelectionreturns.“In2012IwaswatchingCNNreportontheGOPprimaryresultsinNewHampshireandwhatstruckmewasthat[Ron]Paulreceiveddoublethepercentinsmallprecinctsasinlarge.Iwonderedwhatthatcouldbe.”

Evansdesignsandbuildsindustrialmachinery,andhisworkinvolvescomplexdataanalysis.Hebecamefascinatedwiththequestion:Whywouldonecandidategetsuchalargerpercentageofthevotesinthelargeprecincts?

Afterstudyingthedataintensivelyforsixweeks,Evanscametoaconclusionthatstunnedhim—but

alsomadesense.Hebecameconvincedthatinthelargeprecincts,someofthecandidates’voteswerebeingshiftedtoanothercandidate.Whyonlyinthelargeprecincts?Itwouldbeeasiertodisguisethe

differences,hethought.Inthesmallprecinctswithonlyafewvoters,theshiftwouldbemuchmorenoticeable.Therewereatleasttwowaysitcouldbedone—throughsoftwareinthemachines;orthroughthesoftwareusedwhenthetotalswerecentrallytabulated.Hewantedtoillustratethevote-

switchinghebelievedwasoccurring.Hesays,“SixweekslaterIhadfiguredoutamethodforexpressingthisusingExcelandreleasedapaperthatisstillonlinetoday.”


Evanssayshisinitialgraphsfromthatpaperweremodifiedbyanotherforumuser,“Liberty1789,”intothe“CumulativePrecinctVoteTallyChart.”EvansandhisfellowRonPaulsupportersbeganusingitto

graphmanyoftheelectionresultsofthe2012Republicanprimary.Hereisthepatterntheysawinstateafterstate:acandidatereceivesahigherpercentageofvotesinlargeprecinctsthanhe/shereceivesinsmallprecincts.Thisincreaseoccursinamathematicallyproportionatepattern,inotherwords,asthe

precinctsgetlargerthecandidate’ssupportgetslargerattheexpenseofothercandidates.Oftenthisincreaseisenoughtochangetheoutcomeorthedynamicoftheelection.Whichcandidatereceivesincreasedsupportinthelargerprecinctsdependsontheparticularrace.In2012,thecandidatethat

benefitedfromthepatterninalmosteveryracewasMittRomney(seeFigure5).

Fig.5—2012IowaRepublicanpresidentialcaucus source:“RepublicanPrimaryElection2012Results:AmazingStatisticalAnomalies”

graphbyChoquetteandJohnson

AfterRonPaullosttheelection,EvanssuspectedthatPaulhadbeencheated.Evanssays,“Itwasfrustratingbecausehewasgivingspeechesinlargevenueswiththousandslinedupoutsidebeyond

capacity,whiletheothercandidate[s]weresomewhatlonely.”Itdidn’tmakesensetoEvans,buthehadnowaytoprovethatPaul’svoteshadbeenstolen.

Inthefallof2012,ChoquetteandJohnsonwroteupthefindingsoftheforumandcirculatedtheir

paperswidelyontheInternetandviaemail.Inthosepaperstheyoccasionallyusetheword“alleged;”butforthemostparttheymadeboldclaimslike,“WhencandidateMittRomneyisontheballothealwaysgainsvotesthroughVoteFlipping”(v1.6p.4)and,“Thisdocumentexposeswhatmayverywell

bethegreatestcaseofelectionfraudevertooccurinUShistory(v1.6p.20.)”Theysparkedconsiderablediscussionwithintheelectionreformcommunity.However,theirstudywasreceivedinthestatisticalcommunitywith(understandable)skepticism.


ThemostobviousflawinChoquetteandJohnson’spaperistheirclaimthatDemocraticpartyelections“don’tshowthisproblem.”ItturnsouttherearemanyDemocraticpartyelectionsthatexhibitthis

patterntoo.However,despitethisweakness,theirstatisticalgraphshavebeenconfirmedtobeaccurateinthreeseparatestudies(Clarkson,PlattsmouthNebraskaHighSchoolScientificLogicClass,andLindeman).Eachofthesefoundmoreelectionswherethepatternappears.

WeaskedKellieOttoboni,agraduatestudentatUCBerkeley,toconfirmtheaccuracyofthegraphsinall

threeofthesestudiesandshereplicatedandconfirmedtheaccuracyofonegrapheachofClarkson;theNebraskaHighSchoolScientificLogicClass;andtwoofMarkLindeman’sgraphs.

BethClarkson,whoconductedoneofthestudies,isaqualitycontrolengineerwithadoctorateinstatistics.ShereadChoquetteandJohnson’spaperandtestedtheirtechniqueherselfonanumberof

elections.IntheelectionsClarksonexaminedinKansas,Ohio,andWisconsin,shefoundthesameunusualincreaseforonecandidateinthelargeprecincts.Asastatisticianshefoundtheresults“terriblysurprising.”

ClarksonpublishedanarticleinSignificance,affirmingboththestudies’analysis,andconclusions,

saying,“ThedataI’veanalyzedsupportstheirhypothesisthatwehaveaserious,pervasive,andsystematicproblemwithelectronicvotingmachines.”SheiscurrentlysuingKansaselectionofficialsforpermissiontoauditthepapertrailofoneoftheelectionssheanalyzedinordertocomparethe

machine’spaperrecordswiththerecordedresults.Sofarheraudithasnotbeenpermitted,andsheremainsconcerned:“Iffraudwereoccurring,thesearethekindofpatternswewouldexpecttosee.”

Clarksondiscoveredthatstatisticalpatternsandacandidate’spercentageofthevoteshare,varybetweendifferentmodelsofelectronicvotingequipment(Figure6).Shealsofoundthattherewere

statisticalirregularitiesfavoringmorethanonecandidate,leadinghertosurmise,“themanipulationisnotlimitedtoasinglepowerfuloperator.Myassessmentisthatthedatarevealsmultiple(asleasttwo)

agentsworkingindependentlytosuccessfullyaltervotingresults.”


Fig.6—2012Ohiopresidentialgeneralelection(MittRomneyvs.BarackObama)Statisticalpatternsandcandidates’percentagesvarywithdifferentmodelsofvotingequipment

GraphbyBethClarkson

InFigure6,theES&SDS200vote-scannershowsanirregularstatisticalpatternthatfavorsRomney(reddots.)ButtheES&SDS100andtheHartEscanshowanirregularpatternthatfavorsObama(bluelines.)

ClarksonisgraphingthepercentagesastheyimpacttheRepublicanvote,sowhenalineisgoingup,itisfavoringtheRepublican(Romney)andwhenalineiscomingdown,itisfavoringtheDemocrat(Obama).KellieOttoboniofUCBerkeleyreplicatedClarkson’sresearchandconfirmeditsaccuracy(Figure6A).

Fig.6A–2012Ohiopresidentialgeneralelection(MittRomneyvs.BarackObama)

Clarkson’sresearchwasreplicatedandconfirmedaccuratebyKellieOttoboniofUCBerkeley GraphbyKellieOttoboni


TherearesomeDREsinthiselectiondemonstratinganormal(relativelyflat)statisticalpattern.Butitwouldbeill-advisedtoconcludethatthosemachinesaresecure.Basedonconversationswithsecurity

expertslikeMattBishopatUCDavisandHaldermanattheUniversityofMichigan,itisalmostalwayspossibletobreachthesecurityofthesemachines.AfterhackingtheWashington,D.C.Internetvotingpilotprogram,Haldermansaid,“Ifthisparticularproblemhadnotexisted,I’mconfidentthatwewould

havefoundanotherwaytoattackthesystem.”ColumbiaUniversitypoliticalscientistMarkLindemananddatascientistLeviBowleshavebothpublishedworkconfirmingtheexistenceofthepattern,butarguingthatitisnotindicativeoffraud.We

foundtheirresearchflawedandtheirlogicunconvincing,andhaveprovidedadetailedbreakdownoftheseissueslaterinthispaper.

OnefactthatisclearfromallthesestudiesisthatinmanyU.S.elections,certaincandidatesarereceivinganincreasedshareofthevoteastheprecinctsgetlarger.Thecrucialquestionis,Why?Isthere

aninnocuousdemographicexplanationfortheincrease?Orisitsomethingthatisindicativeoferrororfraud?

WhenDidthePatternBegin?Weareunabletopindownexactlywhenthepatternoriginated.Inafascinatingtripdownelectionfraudmemorylane,writerVictoriaCollierdescribesnumeroustroubledU.S.elections.Itwouldbeinstructive

todoastatisticalanalysisononeoftheracesthatshecitesasan“up-set”likeChuckHagel’s1996NebraskaSenatevictory.”Threedaysbeforetheelection…apollconductedbytheOmahaWorld-Heraldshowedadeadheat[but]HageltrouncedNelsonbyfifteenpoints,”Colliersays.“Thisdivergence

frompre-electionpollingwasenoughtoraiseeyebrowsacrossthenation.”

Fornow,wecanstatethatracesthatweexaminedfrom2004andearlierdidnotshowthepatternofincreasedcandidates’percentagesinlargeprecincts.LookingagainatFigure4,weseethatinthe2000and2004racesinAlachua,Florida,eachcandidate’sshareofthevotesisroughlythesameinsmalland

largeprecincts.However,by2008,thisisnotthecaseinmanyracesaroundthecountry(seeFigure7).


Fig.7—2008NewHampshireandMinnesotaDemocraticpresidentialprimaries

Inracesaroundthecountrycandidates’beginreceivinglargerpercentagesofthevoteinlargeprecincts GraphsbyMarkLindeman

IntheNewHampshireandMinnesotaDemocraticprimaries,aswellasinotherracesin2008,

candidatesreceivealargerpercentageofthevotesastheprecinctsgetlarger.InNewHampshire,thepatternbenefitsClinton.InMinnesota,Obamaistheonewhogainsvotesharesinthelargeprecincts.The2008NewHampshireDemocraticpresidentialprimarywasalsotheracewelookedatinitiallywhere

themanualrecountdidnotmatchtheoriginalmachinetotals(Figure2A).

Figure8showstheWisconsinRepublicanprimaryintwodifferentelectioncycles.In2000,nocandidatehasmuchofanincreaseinthelargeprecincts.Butin2016,TedCruz’spercentagenoticeablyincreasesinthelargeprecincts,whileDonaldTrump’spercentageofthevotegoesdown.

ThesetwocomparisonsalsodemonstratethatthepatternishappeninginbothDemocraticand

Republicanraces.


Fig.8–2000and2016WisconsinRepublicanpresidentialprimariesshowdifferentstatisticalpatterns GraphsbyAnselmoSampietro

Doingfurtherresearchonhistoricalraceswillhelpidentifypossibleearlyappearancesofthepattern.Colliersays,“Throughoutthe1980sand1990s,theuseofopticalscannerstoprocesspaperballotsbecamewidespread.”Butprobablythemostseminalyearforelectronicvotingequipmentwas2002,

whenstatesacrossthecountryexperiencedalargeinfluxofcomputer-basedvotingsystems,withthepassageofthe(perhapsironicallynamed)HelpAmericaVoteAct.

Smooth,Unidirectional,andMathematicallyPredictable.

Figure9showsagraphofthe2016LouisianaDemocraticPrimary.TheanalysisisbyBethClarksonandAnselmoSampietroconfirmeditsaccuracy.


Fig.9—2016LouisianaDemocraticpresidentialprimary

Candidates’votesharesvarybyasmuchas37%betweensmallandlargeprecincts GraphbyBethClarkson

ThisgraphisincompleteviolationoftheLawofLargeNumbers.Foracandidatetoreceivethislevelof

increasedsupportinthelargeprecincts,eachnewprecinctmustbesoheavilyweightedthatitdefiestheaverageofalltheotherprecinctsthathavealreadybeenaddedtogether.Thisisamajorstatisticalirregularity.

Inthesmallprecincts,thedifferencebetweenClintonandSandersisapproximately10%(Clinton48%–

Sanders38%).However,inthelargestprecinctsthedifferencebetweenthecandidatesis47%(Clinton70%–Sanders23%.)Thatisadifferenceof37%supportbetweenthesmallestprecinctsandthelargestprecincts.

Toseehowheavilyweightedthelargeprecinctsare,wegraphedthemseparately,countybycounty,

dividingthelargest25%fromtheremaining75%(Figure10).Withinalmosteverycounty,Clinton

receivesahigherpercentageofthevoteinlargeprecinctsbyunusuallyhighmargins,sometimesby

closeto40%.InWashingtonCountyyoucanliterallyseethemomentthatthedatastartstochange

around600votes.


Fig.10–2016LouisianaDem.presidentialprimary,countyleveldata-thedifferenceinthecandidates’percentagesbetweensmallandlargeprecinctsisunusuallyhigh-insomecasescloseto40%

GraphsbyAnselmoSampietro

Therearethreeothercharacteristicsofthisdatathataresuspect:

1) Thedataissmooth.Thelinesintheoverallstatechartgostraightupandstraightdown;andlinesofdatainthelargeprecinctsarealsoquitestraight.ThisiswhatDr.Scheurenisreferringtointheopeningofthepaperwhenhesays,“Thereisagreaterdegreeofsmoothnessinthe

outcomesthantheroughnessthatistypicalinraw/realdata.” 2) Thedataisunidirectional.Inthestatewideresults,thedataonlymovesinonedirection:

Clintongoesup;Sandersgoesdown.Thepercentagesneverdemonstratethekindofupsand

downscausedbyorganicvotingbehavior.3) Thedatafollowsamathematicallypredictablepattern.Clinton’ssupportisincreasingina

mathematicallypredictableway.Ineachprogressivelylargerprecinctshegetsaslightlylarger

levelofsupport.Thisisapossibleindicationthatamathematicalalgorithmhasbeenappliedtotheresults.

DemographicFactors

Thedatawe’veexaminedsofarshowsthatthepatternemergedinvariouslocationssometimeafter2000.Thisarguesagainstademographicexplanation,sincethedemographicsareconsistent,anditis

thetimeperiodthatischanging.


DiggingdeeperintotheLouisianadatafurtherunderminesthetheorythatdemographicfactorsareresponsiblefortheincreasedvotesharesinlargeprecincts.Here,thesheersizeofthedifference

betweenthesmallandlargeprecinctshasnoplausibledemographicexplanation.

NateCohnattheNewYorkTimeshasinsistedthatthecandidates’percentagesbeingcorrelatedwithprecinctsizeintheSouthisduetoalargeconcentrationofblackvotersintheprecinctsthatsupportClinton.

Fig.11–NateCohn’stweetmakesaweak,sarcasticargument,implyingthatallofthestatisticalirregularitiesintheSouthcanbeexplainedbyClinton’sleadamongblackvoters

InatweetpostedonJune27,2016(Figure11),Cohntakesanunfortunatelyflipattitudetowardtheevidence—whichallowshimtopassonanalyzingthedata,butdoesnothelpanyoneelsesortout

whetherthisisapatternthatweneedtobeconcernedabout.TheunderlyingquestionofCohn’stweetisthis:Isthereademographicexplanationthatcanaccountfora37%shiftinsupportforClintonfrom

thesmallestprecinctstothelargestprecincts?Forinstance,isthiscorrelationhappeningbecauseClinton’ssupportissostrongamongblacksandtherearesomanyadditionalblacksinthelargeprecinctsthatitcanaccountfora37%increase?

JustabrieflookatthedemographicsinLouisiana,contradictsthistheory.The2010censussummaryfile

(searchWashingtonParish,Louisiana–andthenclickon“RaceandHispanicorLatinoorigin,forexample)indicatesthatbothWashingtonandWinnparisheshaveablackpopulationof31%(Figure11A).Soevenif100%ofblackvotersweresupportingClinton,(whichtheyarenot)andeveniftheyall

livedinthelargeprecincts,itcouldnotexplaina37%increaseinhersupportinthelargeprecincts.


Fig.11A–CensusdatashowsthereisnotalargeenoughblackpopulationinWinnorWashingtonParish

toexplainthepercentageofsupportClintonisreceivinginthelargeprecincts

EvenifyoucouldexplaintheincreaseinClinton’ssupportinLouisianabysayingthattherearemoreblacksinthoselargeprecincts,itdoesnotexplainCruz’sincreasedsupportinlargeprecinctsinWisconsin,orRomney’sincreasedsupportinlargeprecinctsinalmosteverystatein2012.Whileone

mightbeinclinedtolooktodemographicstoexplaindiscrepanciesinonestateoranother,thefactthatthesamepatternispresentacrossmultiplestateswithverydifferentdemographicsarguesstronglyagainstitbeingcausedbydemographicfactorsalone.

Despitethelackofharddata,theideathatthesecorrelationscanbeexplaineddemographicallyremains

popular.EitanHersh,amemberofthepoliticalsciencefacultyatYale,toldusthathewouldbetmoneyonademographicexplanation.Weappreciatehisgamblingspirit,let’sseeifhisconfidenceiswell-placed.

First,let’stakeacloselookattheprevailingnarrativethatthemostpredictivedemographicfactorof

whetheravoterwillsupportClintonorSandersisrace.Thisisactuallynotthecase.ArticlesinTheAtlantic,theLosAngelesTimes,andVoxhaveallreportedthatthesinglemostsalientfactorindeterminingwhetheravoterwillsupportClintonorSandersinthe2016primaryisage.Voxquoted

politicalscientistAlanAbramowitzassaying,"Itwasage,andbeyondthatnothingmattered,”afterhe“ranamultivariateanalysistohelpfigureoutthisquestion.”

InthesameVoxarticle,JeffSteinrefinesthenarrativethatClintonisbeatingSandersamongblackvotersbypointingoutthat“severalpollshaveputSandersaheadofClintonamongyoungAfrican-

Americans;intheReuterspollingdata,forinstance,SandersbeatsClintonby25pointsamongblackvotersaged18to29.”AccordingtoLeeMiringoff,directoroftheMaristCollegeInstituteforPublicOpinion,intheLosAngelesTimespiece,"Theagefactorseemstotrumpeverything."

HowmuchadvantagedoesClintonhaveamongoldervoters?Wetooktheaverageofthreepollsto

determineClinton’sapproximatestatisticalleadwitholdervoters(which,basedonthesepolls,we’re

definingas45andolder):

• NBCNews/WallStreetJournalpollfromApril18,2016(quotedintheLosAngelesTimes)givesClintona27%leadwithvotersover50

• IowaentrancepollgivesClintona23%leadwithvoters45–64(TheAtlantic)• IowaentrancepollgivesClintona43%leadwithvotersover65(TheAtlantic)


AveragingthesethreepollsshowClintonwitha31%leadamongoldervoters.SoevenifthelargestprecinctsinLouisianaconsistedofonlyvotersovertheageof45(whichclearlytheydonot),youstill

couldnotexplainashiftof37%,usingthemostimportantdemographicfactorinthecontest.

Isitpossiblethatacombinationoffactorssuchasraceandagecouldexplainthedifferencesinthesepercentages?Intheory,yes.Buttheprecinctsthatwouldhavetoexisttojustifythesepercentageshavenobearingonreality.Theywouldneedtobeunusualneighborhoodswhereolderblackandwhite

voterslivetogetherinmixed-race,largeprecincts,withveryfewyoungpeople.Inactuality,blackneighborhoodstendtoconsistoffamiliesandpeopleofallages;andmixed-raceneighborhoodsarenottypicallymadeupprimarilyofpeopleoverforty-five–notinLouisiana–andnotinotherpartsofthe

countrywhereweseethispatternoccurringoverandoveragain.

InLouisiana,thedemographicargumentseemstoevaporatewhenthedataiscarefullyinvestigated.Whatexplanationissupportedbythedata?Let’slookatthe2016Wisconsinprimarytoanswerthat.

PhilEvans,theengineerwhooriginatedthe“CumulativePrecinctVoteTallyChart,”didananalysisofthe2016WisconsinRepublicanPrimary(Figure12).Thegraphcompareshand-countedcountiestothe

machine-countedcountiesintherestofthestate.Thepatternoflargervotesharesinlargerprecinctsisnotpresentinthehand-countedcounties.Allcandidatesreceiveapproximatelythesamepercentageofthevoteinbothsmallandlargeprecincts.However,inthemachine-countedcounties,Cruzdoesbetter

astheprecinctsgetlarger;Trumpfaresworsethanhedidinthehand-countedcountiesand17%worseinthelargestprecincts.Sampietroconfirmedtheseresultsandalsochartedthemasscatterplots(Figure12A).

Fig.12–2016WisconsinRepublicanpresidentialprimary Hand-countedcountiesandmachine-countedcountiesshowdifferentresults

graphsbyPhilEvans


Fig.12A–2016WisconsinRepublicanpresidentialprimary Hand-countedcountiesvs.machine-countedcounties(scatterplots)

graphsbyAnselmoSampietro

Wehavecreatedscatterplotsofourresearch,butingeneralaredemonstratingthelinechart,becausethepatternismorevisiblethere.Eachdotonthescatterplotrepresentsthepercentageofvotesacandidatereceivesinasingleprecinct:thehigherthedot,thelargerthepercentageofvotesforthe

candidateinthatprecinct.Thefurthertotherightadotis,thelargertheprecinct.Inthehand-countedWisconsinresults,thedotsarelevel:precinctsizedoesn’tinfluencethepercentageofvotesforeachcandidate,andthepercentagesarerelativelyconsistent.Inthemachine-countedresults,Cruz’s

percentageofvotessystematicallyincreasesastheprecinctsgetlarger.ThedescendingshareofvotesforTrumpisalsovisible.

Onceagain,onecandidatedoesinexplicablybetterinthelargeprecincts,butthistimethereisalsoanassociationwiththevotingequipment—becauseunlikeLouisiana,Wisconsincountssomevotesby

hand—allowingforanaddedlayerofcomparison.Wehaveanassociation,butisitacauseandeffectassociation?Arethemachinescausingthisincreaseinacandidate’stotals,oristheresomeotherexplanation?

Thechoiceofvotingequipmentisnotrandom.Forexample,itcouldbethattheprecinctsthatchooseto

counttheirvotesbyhandarealsoprecinctsthatincludemore“independentthinkers”andarethereforemoreattractedtothemaverickcampaignofTrump.

Ifthatisthecase,thenwewouldexpectthesameindependentthinkersinthesameprecinctsontheDemocraticsidetosupportthemaverickcampaignofSanders.Isthatwhatwefind?DotheWisconsin

hand-countedprecinctsgiveSandersagreaterpercentageofsupportthantheWisconsinmachine-


countedprecincts?No,theydonot(Figure13).Sandersreceivesapproximately57%ofthevoteinbothhand-countedandmachine-countedprecinctsinthatrace.

Fig.13–2016WisconsinDemocraticpresidentialprimary-unliketheRepublicanpresidentialprimary

thisraceshowsverylittlecorrelationbetweenprecinctsizeandanycandidates’percentageofthevote graphsbyAnselmoSampietro

Thisseemsstrange.Ifit’sademographicpattern,whyisn’titevidentonbothsides?Themediahas

commentedonthestrikingsimilaritiesbetweentheTrumpandSanderscampaigns.Bothcandidatesarepoliticaloutsiders,deliveringscathingcriticismsofthestatusquoandopposingtradedeals.Bothcampaignshavedonewellwithwhitemalevoters.Itwouldseemlikelythatademographicpatternlarge

enoughtoaffecttheRepublicanraceby17%wouldalsohavesomeimpactonvotersintheDemocraticrace.

KeyArgumentsPart1

Wehavenowestablishedsixkeyargumentsthatthepatternwearewitnessingisirregular;isnotdueto

demographicfactors;andisinsomewayconnectedwiththeuseofelectronicvotingequipment:

1) Datashowsthatelectionsinthesamelocationdevelopedthepatternafter2000,contradictingademographicexplanation.*

2) ThepatternviolatestheLawofLargeNumbers.

3) Thepatternissmooth,unidirectionalandmathematicallypredictable.4) Thelargediscrepanciesinthepercentagesbetweensmallandlargeprecinctsdefyaplausible

demographicexplanation.


5) Thepatternisevidentinawidevarietyofgeographies,withverydifferentdemographics,furthercontradictingademographicexplanation.WehavealreadyshownthepatterninIowa,

Ohio,Wisconsin,Florida,andLouisiana.Thepatternhasbeenconfirmedbytwoanalystsinthe2016presidentialprimaryinConnecticut,Delaware,Florida,Illinois,Kentucky,Louisiana,Massachusetts,NewYork,NorthCarolina,Ohio,SouthCarolina,Tennessee,WestVirginia,and

Wisconsin.Ithasadditionallybeenconfirmedbyatleastoneanalystinthe2016presidentialprimaryinAlabama,California,Georgia,Oklahoma,andTexas.

6) Thepatternappearsinmachine-countedraces,butnothand-countedraces.Publichandcounts

withasecurechainofcustodyareconsideredinternationallythemostsecureformofcountingvotes,sothispointstothevotingmachinesasalikelysourceoftheproblem.

*PhilEvanstoldusthattheonlyracestheyfoundthepatterninpriorto2000,wereinelections

whereanindividualorofficialshadbeenconvictedoffraud.Wehavenotyethadachancetoverifythatinformation.

NewYorkState—MultipleIssuesRaiseSeriousConcerns

Inthe2016NewYorkDemocraticpresidentialprimary,theCVTgraphsofcertaincountiesareirregular

(Figure15).BothRichmondCounty(StatenIsland)andKingsCounty(Brooklyn)revealastrongcorrelationbetweenprecinctsizeandcandidates’percentages.

Fig.15—2016NewYorkDemocraticpresidentialprimary RichmondandKingsCounty,bothinNewYorkCity,showirregularCVTgraphs

graphsbyPhilEvans

Earlierinthepaperwedemonstratedthathand-countedandmachine-countedballotsinKingsCountygivedifferentpercentagesforthecandidates.ThesuspectCVTgraphinKingsCountyreinforcesthe


findingsofirregularelectionresultspresentedearlier,andpaintsanincreasinglytroubledportraitofpotentiallycompromisedvotetotals.Butthereisanotheroddfactorinthisstate’sresults.

PrecisePercentages

WeareshowingtheNewYorkCitygraphs(Figure15)toillustrateaveryspecificpoint.Thedata

supportstheideathattheoverallstatevotetotalsarebeingmassagedtoachieveapredeterminedpercentage.

ThefinalreportedtotalsinNewYorkstatewerealmostexactly58%Clintonto42%Sanders.Itisnecessarytogotothethirddecimaldigittoseeadifference:57.995versus42.005.

DougJohnsonHatlemreportedonthisissue,pointingoutthat,“TheoverallresultsinNewYork,asannouncedonelectionnight,deviatedfromaperfect58–42splitby0.005345.That’s97votesoutofover1.8million.”

InKingsCounty,thereportedtotalswerealmostexactly60%Clinton,to40%Sanders:(59.72%Clinton,40.27%Sanders.)The.27differenceiscausedbyabout800votesoutof300,000.IntheBronx,the

percentageswerealmostprecisely70%to30%(69.59%Clintonto30.41%Sanders),thedifferencebeingjust616outof151,908totalvotes.TheBronxCountyCVTgraphisalsoirregular(Figure16).

Fig.16—2016NewYorkDemocraticpresidentialprimary-theBronxCountyCVTgraphshowsastrong

correlationbetweenprecinctsizeandcandidatepercentage graphbyPhilEvans


Inaninterview,J.AlexHaldermanwasconfidentthatitwouldbepossibletoassignapercentagetoa

particularcandidate,“Ifyou’remanipulatingthecentralcountingsystems,thenyoucanmakeanoveralladjustment.”Askedwhetherit’spossibletogetaccesstothecentralcountingsoftware,hereplied,“Probably.It’sbeenourexperienceinthelastdecadewiththeDieboldsystems,forinstance,thatitwas

certainlypossibleinthemachinesandcountingsystemsweexamined.”

NewYorkCityalreadyhadtwounusualpiecesofdatagivingcauseforconcern:

• Thedifferencebetweenhand-countandmachine-countresults• IrregularCVTgraphsinmultiplecounties

Addtothat:

• Precisepercentagesinthetotalelectionresultscouldbeanindicationthattheresultshavebeenmanipulatedtoachieveaspecificpercentageforthecandidates.

Thesearethreeseparatebutreinforcingfacts,illustratingwhythetotalsforthisstatearesuspect.


ANormalGraph

ColumbiaCounty,NewYork—ahand-countedcounty—offersagoodpointofcomparisontotheabovegraphs(Figure17).

Fig.17—2016NewYorkDemocraticpresidentialprimary,ColumbiaCounty Thishand-countedcountyhasanormalCVTgraph

graphbyAnselmoSampietro

Inthisgraph,thereisalargedegreeoffluctuationbothupanddownontheleftsideofthegraph.Then,byabout1,300votes,thegraphsettlesintoafairlyevenstraightline,withsmall,randomfluctuations.It

maintainsabasicallyflatlinethroughallofthelargestprecincts.Thisisaverynormal-lookingCVTgraph.

ColumbiaCountyDemocraticElectionCommissionerVirginiaMartinsaystheylookateverysingleballotincompetitiveraces.InaninterviewwiththeRegister-Star,shedescribestheirprocess:“Whenavoterscansaballot,itdropsdownintoaballotbagatthebottomoftheopticalscanningmachine.Attheend

oftheday,twoinspectors,oneRepublicanandoneDemocrat,openthemachine,pulloutthebagandzipitshut.”Thentheyproceedtocount100%oftheballotsbyhand.Inaninterviewonpublicradio,shesaidthatColumbiaCounty’svote-countingprocessisfocusedonsecurity,accuracy,andtransparency:

“Wearevery,verycarefulaboutthechainofcustody…Everythinggetshandcounted,andtheresults


thatwecertifyarebasedonthathandcount.”Sheconcludedbypointingoutthatthehandcountisopentothepublic.

Opportunity,Incentive,andRationalization

Fig.18–TheFraudTriangle:opportunity,incentive,andrationalization

Fraudinvestigationsinotherindustrieslookforthreefactors:opportunity,incentive,andrationalization.TheeasewithwhichDr.Haldermanandothersecurityexpertshavebeenableto

penetrateandcontrolthesoftwareofthevotingequipment,makesitapparentthattheopportunityexists.

Theotherfactors,incentiveandrationalization,arealsopresent.“There’salotatstake,”saysJonathanSimonoftheElectionDefenseAlliance.“ControloftheAmericangovernment,controloftheeconomy,

controlofthemilitary.Whenthere’salotatstakethere’sanincentivetofraud.”

Rationalization?It’sunfortunate,butthereareindividualsandgroupsactiveinthepoliticallandscapewhofeeljustifiedtakingactionsthatcrossethicalboundaries.OntheDemocraticside,leakeddocumentsrevealedthattheDemocraticNationalConventionsupportedClintonastheeventual

nominee,althoughtheirpublicpositionwasoneofneutrality.TheGucciferdocument,“2016GOPpresidentialcandidates,”says,“Ourgoalsinthecomingmonthswillbetoframe…theeventualnomineeearlyandtoprovideacontrastbetweentheGOPfieldandHRC[HillaryRodhamClinton.]”

OntheRepublicanside,formerFloridaRepublicanPartychairmanJimGreertestifiedin2012that

Republicanshadmeetingsabout“keepingblacksfromvoting.”


Thesearejusttwoexamples.Therealityisthatpoliticalcampaignshaveoperativeswhoworktodiscreditandtargettheotherside,andthatthesecampaignssometimescrossethicalboundaries.

RisingThreatofDataAttacks

Theadvancedageofmanyofthevotingmachinesbeingusedintheareaswestudiedmakesitquite

possiblethatatleastsomeofthestrangepatternswearewitnessingareduetohardwareorsoftwarefailures.Buttheunidirectionalresultsofthestatisticalpatternsmakeitunlikelythaterroristhecentralsourceoftheseissues.

Inansweringthequestionofwhetherornotthedatainourvotingmachinesisbeingbreached,itis

importanttobeawareofhowmanymajorgovernment,financialinstitutions,andhigh-profileindustriesinthecountryhavebeensuccessfullyattacked.InMay2016,NewYorkAttorneyGeneralEricSchneidermanreleasedastatementsaying,“hisofficehasreceivedanover40%increaseindatabreach

notificationsinvolvingNewYorkerssofarthisyear.”IRSCommissionerJohnKoskinentoldFortunemagazine,“Wearebasicallyattackedoratleastprobedoveramilliontimesaday.”OneofthoseattacksontheIRSresultedinaspectacularlysuccessfulMay2015hackinwhich,“hackershadusedGet

Transcript[anIRStool]tostealthepersonalinformationof724,000people.”TheU.S.ArmyandNavy,thePentagon,andNASAwereallsuccessfullypenetratedbyahackernamedGaryMcKinnon.TheGuardianquotedMcKinnonassayinghecouldscan“65,000machinesinlessthannineminutes.”This

wasbetween1999and2002;hackershavedevelopedconsiderablymoresophisticatedprotocolssincethen.

Banksandcorporationsarealsodefendingagainstconstantattacksontheirdataandfinancialresources.InFebruary2016,CNNreportedthathackersstolecashfrom100banksandriggedATMsto

spewcashin“oneofthelargestbankheistsever,”totalingapproximately$1billioninstolenfunds.OtherfinancialinstitutionsthathavebeensuccessfullyattackedincludeJPMorganChase,Citigroup,the

FederalReserveBankofNewYork,andsecurityanalystsworkingforBankofAmerica.ThehackercollectiveAnonymoushas,bythemselves,hackedtheChurchofScientology,HiddenWiki,SanFranciscoBART,theDepartmentofJustice,andtheWorldTradeOrganization.Majorcorporateattacksinclude

theTargetdatabreachthatexposedthefinancialinformationof40millioncustomers,theSonyemailscandal,andAdrianLamo’sattackontheNewYorkTimes,whichwasapparentlysoeasythataccordingtogeek.com,Lamo“createdanentryinthe[NewYorkTimes]Op-Eddatabaseforhimself,complete

withcellphonenumber,realname,ande-mailaddress.Inthedescriptionfieldofthedatabase,hesimplyentered’Computerhacking,nationalsecurity,communicationsintelligence.’”

EvaluatingtheOpposition Tworeportshavebeenfiledthat,whileconfirmingthepatternofincreasedcandidatepercentagesinlargeprecincts,arguethatitisnotanindicationoferrororfraud.


InNovember2015,MarkLindeman,apoliticalscientistatColumbiaUniversity,confirmedtheexistenceofthepattern.Buthetookissuewiththeconclusionsofpreviousstudies,dismissingtheiranalysisas

“unsupported”andhaving“nofoundation.”Lindeman’sanalysisdoesnotholduptoclosescrutiny.HeattacksChoquetteandJohnsonwithoutpresentinganyrealdatatobackhisassertions.Heoftenmisinterpretsevidenceandselectivelyignoresfactsthatdon'tmatchwithhistheory.Hisaddendum

critiquingClarksonisequallyweak.Clarksonagreesthathisanalysisisnotstatisticallysupported,writingtous,“MyownworkincludingshareofregisteredRepublicansshowsthatevenwhenthatdataisincluded,thenumberofvotescastremainsasignificantfactor,whichcontradictshisanalysis.”

Aseconddataanalyst,LeviBowles,coveredClarkson’sworkinaseriesoffiveblogpoststitled,Kansas

ElectionFraud.Bowlesisconcerned,asarewe,thatthepatternswearewitnessingcouldbeduetodemographicissues,andhemakeshispointemphaticallyinhiscomments,“…thereisn'tgoodstatisticalevidencethatthemachinesareworkingincorrectly.Theevidence,isthatthereisanunderlying

correlation[ofincreasedpercentageswithlargerprecincts],thoughafterwerecognizetheworldiscomplexandcreation/existenceofprecinctsisnotarandom,stochasticprocess,weseethatcorrelationcompletelydisappear.”

Bowles’critiquedoesnotprovideanexplanationfortheappearanceofthepatternsincetheyear2000.

Precinctshaveneverbeenrandomlycreateddistricts.Sowhywasn’tthispatternpresentinearlierelections?

Furthermore,thecreationofprecinctsisimpactedbyavarietyoffactors,butthosefactorsvaryfromstatetostate.Yetwearewitnessingthepatternacrossabroadselectionofstates,withstrikingly

differentdemographicssuchasWisconsinandLouisiana.Eachofthosestateshasitsownprocessforcreatingprecincts,sotheexistenceofthepatternacrosssuchabroadarrayofstatesarguesagainstit

beingduetofactorsthataffectprecinctformation.

Tosummarizeourcritiqueofthiseffort:Bowlesgoesbeyondrhetoricalargumentsandutilizesastatisticalanalysistocomparethecorrelationofdemographicfactorswiththecorrelationofprecinctsize;buthefailstoapplytherightstatisticalmodel,deliversweakornon-relevantevidence,and

eventuallydoesvalidateClarkson'swork,againsthisownintent.

LargeRepublicanPrecinctTheoryNotValid

Bowlesdoescomeupwithoneintriguingtheorywefoundworthpursuing.HespeculatesthatthereasonforRepublicancandidatesgaininglargerpercentagesinthelargerprecinctsisthatthelargestprecinctsareactuallyconservative-leaningsuburbanprecincts.Hecreatesamapthatshowsthatat

leastsomelargeprecinctsareconservativeintheirpoliticaloutlook.WeaskedClarksontoseparateoutthesuburban“conservative”precinctsfromthe“democratic-leaning”urbanprecinctsandgraphthemseparately.IfBowles’theoryiscorrect,thatthecorrelationofhigherRepublicanpercentagesisdueto

largeconservative-leaningsuburbs,thenthecorrelationwillshowupevenmorestronglyinthoseconservativesuburbs.Isthatwhathappens?


No.IntheRepublican-leaningsuburbs,thereisnoincreaseintheRepublicancandidate’spercentageinthelargeprecincts(Figure19).TherearefewerRepublicansinsidethecityofWichita(37%vs.48%),but

theincreaseintheshareoftheRepublicanvoteinthelargerprecinctsisplainlyevidentthere.Bowlestheory,thatthisstatisticalpatternisduetotheexistenceoflargeconservativeprecinctsinthesuburbs,isnotcorrect—atleastinthisinstance.Whateveriscausingthepattern,hereitisvisiblyimpactingonly

theinner-cityDemocratic-leaningprecincts.Thatraisesthequestionofwhethertheseinner-cityprecinctshavebeentargetedinsomeway.

Fig.19—2014KansasSenaterace-theincreaseofacandidate’spercentageinthelargeprecinctsisonlyseenintheinnercityprecincts,notthesuburbanprecincts

GraphbyBethClarkson

KeyArgumentsPart2

Continuingwithourlistofthekeypointsthatwehaveestablished:

7) Electionresultswithprecisepercentagepoints,inracesthatarealreadyshowingstatisticalirregularities,raisefurtherconcernthattheresultsmaynotbebasedonactualvotes.

8) Theconsistencywithwhichonecandidatebenefitsfromthepatternarguesagainstitbeinggeneratedbyarandomcomputererror.

9) IntheKansas2014Senateracetheirregularstatisticalpatternisevidentinsidethecitylimitsof

Democratic-leaningWichita,butnotinthemoreRepublicansuburbanareas.ThisarguesagainstthetheorythatthepatternisduetolargesuburbanprecinctsbeingmadeupofagreaterpercentageofRepublicanvoters.


LeftSide/RightSideFluctuation

TheretendstobealotoffluctuationontheleftsideoftheCVTgraph.Thisistobeexpected,becauseinsmallprecincts,beforealotofvotesareaccumulated,thegraphwillswingfromtheinfluenceofjusta

fewvotes.Occasionallywehaveseengraphswithsuchsteepunidirectionalmovementontheleftsideofthegraphthatitisconcerning.The2016DelawareDemocraticpresidentialprimaryisanexampleofthis(Figure20).

Fig.20—2016DelawareDemocraticpresidentialprimary Theslopeontheleftisunusuallysteepandunidirectional

graphbyAnselmoSampietro

WefoundscreenshotsonlineconfirmingthattheactualvotecountforSandershaddecreasedsharplyafterearlyreporting(Figure21).ThesetwofactorstogetherleadustosaythattheresultsinDelaware

bearfurtherinvestigationandmaynotbeaccurate.


Fig.21—2016DelawareDemocraticpresidentialprimary,SussexCounty

Sandersvotesgodownfrom6,247to5,630 screenshotcourtesyofRenoBerkeley

ReversedEffect

Inthe2016primaryelection,ontwooccasionswesawirregularchartsthattiltedinSanders’sfavor

(Figure22).Butthiswasrare.Ourtheoryisthatvotetotalsarebeingmanipulatedtoachieveapre-determinedpercentage,andthatinthisprocessvotesmightbeshiftedatvarioustimesbetweenmultiplecandidates.


Fig.22—2016NewYorkDemocraticpresidentialprimary,BroomeCounty TheCVTgraphshowsSanderswithanunusualgaininvotesinthelargestcounties

Ascreenshotshows112.8%reporting,followedbybothcandidatesexperiencingalossofvotes graphbyAnselmoSampietro&screenshotcourtesyofAimeeRoxColeman

VoterFraudvs.ElectionFraud

Thereisoneothercritiquethatisworthtakingthetimetoexamine.ProfessorJ.CelesteLay’scommentinaninterviewwithDougJohnsonHatlemisagoodexampleofthis:

“MostofthisdiscussionisdrivenbySanderssupporterswhoaredisappointedheisnotwinningand

wanttoclaimhehasmoresupportintheDemocraticPartythanheactuallydoes…Untilprovenotherwise,I’llgowiththenumerousstudiesdemonstratingtheinfinitesimalamountofvoterfraudinU.S.elections.”

Layisconflatingvoterfraudwithelectionfraud.Theyareoppositebehaviors.Voterfraudiswhena

singlevoteristryingtoscamthesystembyvotingtwice,orvotingwhenheorsheisnotauthorizedtodoso.IthasbeendemonstratedtoberareintheU.S.Electionfraudreferstotheillegalinterferencewiththeprocessofanelection.Inelectionfraud,thevotersarethevictimsofanorganizedefforttorob

themoftheirrightfulinfluence.Howcommonisit?

AccordingtoWalterR.Mebane,Jr.andAllenHicken,bothonthepoliticalsciencefacultyattheUniversityofMichigan,electionfraudoccursinamajorityofdemocracies.Intheir2015GuidetoElectionForensics,theywrite,“Duringthefirsthalfof2015alone,forexample,allegationsofelection

fraudoccurredinBangladesh,India,Israel,Macedonia,Nigeria,Pakistan,Russia,Togo,theUnitedKingdom,theUnitedStates,andZambia…datacompiledbyKelleyandKolev(2010)onnational


electionsconductedinmorethan170countriesfrom1978–2004indicate…61%ofcountriesexperiencedsomedegreeof(known)cheating.”

Incorrespondence,Mebanewasfirmthat,“theproblemofdetectingelectionfraudreallyisextremely

difficult.”Thisraisesthelargerissue:Isitincumbentonthevotingpublictopoliceelectionsandprovethatfraudistakingplace?Orisitmoreappropriatelyincumbentonelectionofficialstoprovideconvincingevidencethattheelectionresultsareaccurateandsecure?

TestimonyUnderOath

Itwouldbehelpfulifsomeonewouldcomeforwardandtestifyunderoaththattheyhadtamperedwith

orbeenaskedtotamperwiththeelectionresultsofvotingmachines.InhisarticleonMedium,SpencerGundertremindedusthatClintCurtisdidexactlythat.

In2004,computerprogrammerClintCurtistestifiedunderoathbeforetheU.S.HouseJudiciaryCommitteethathe“wroteaprototypefor[atthattime]presentCongressmanTomFeeney,”ofa

programthat“wouldflipthevote51–49.Whoeveryouwantedittogotoandwhicheverraceyouwantedtowin.”Curtistestifiedthathewastoldbyhiscompanythat,“weneededtocontrolthevoteinSouthFlorida(5:23.)”

Inthisclipfromthedocumentary,Uncounted,Curtisdemonstratesthe“flip.”At4:17hesays,“Twenty-

fourlinesofcode.Youneverseeit.”

FutureElections

Evenasdoubtsriseabouttheaccuracyandsecurityofelectronicvotingmachines,countiescontinuetoinvestmillionsofdollarspurchasingthem.InMarchMarionCountyIndianaspent1.4milliondollars

purchasingelectronicvotingequipmentthatincludedtheES&SDS200in-precincttabulators;thesamemachinesthatproducedirregularelectionresultsinthe2016NewYorkprimary.InSedgwickCountyKansas,thecountycommissionersapprovedameasurethisweektospend7.8millionovertenyearson

newES&Smachinesaswell.BethClarksonwasatthemeeting,andexpressedherconcerns,butthevotetopurchasethemachinesstillpassed.Thatvotewascountedbyhand.

The2016Primary

Themajorityofthedataweexaminedsuggeststhatthetwocandidatescurrentlyslatedtoaccepttheirparty’snominationinthe2016presidentialprimaryraces,receivedadifferentnumberofvotesthan

whathasbeenofficiallyreported.


OntheRepublicanside,statisticalanalysisindicatesthatDonaldTrumpprobablyreceivedmorevotesthanwhathasbeenreportedandcertified.Becausehewasabletoovercomehisopposition,evenwith

theirregularities,hisselectionasthepresumptiveRepublicannomineeissupportedbythedata.

Aswestatedintheopening,thisisnotthecaseontheDemocraticside.Theoverwhelmingmajorityofthealmosttwodozenstatesthatweanalyzed,demonstratedirregularities.InalmosteveryinstancethediscrepanciesfavoredHillaryClinton.Inalllikelihoodthecurrentresultshaveassignedheragreater

percentageofthevotethanshemayhaveactuallyreceived,whilesimultaneouslyunder-reportingBernieSanders’legitimatevoteshare.

Thedifferencebetweenthereportedtotals,andourbestestimateoftheactualvote,variesconsiderablyfromstatetostate.Howeverthesedifferencesaresignificant–sometimesmorethan10%

–andcouldchangetheoutcomeoftheelection.Weintendtoreportonthepercentagethateachstatemaybeoff,basedonastatisticalanalysisofasmanystatesaspossible.

Itishardtoconceiveofalegitimatetransferofpowerfollowinganelectionthathasbeenthisflawed.Werecommendthatmanyoftheseelectionsbeexamined,andiffoundtobeinaccurate,decertified.

Wherepaperballotsareavailable,itwouldbeinformativetocountthembyhand.Wherepaperballotsarenotavailable,itwouldbemoreequitabletoholdasecondvote,onethatdoesnotutilizeanyelectronicvotingequipment.Itwouldbebestifthecountsarevideotaped,andforobserverstobeable

towatchincloseenoughproximitytoverifytheaccuratecountofeveryballot.Possiblytheballotsthemselvesneedtobeindividuallyphotographedanditemized.Handcounts,historically,havealsobeensubjecttoelectionfraud,sotheprotocolsmustfocusontransparency,chainofcustody,and

secureandverifiableresults.

Weunderstandthatthisisunprecedented.WhiletheU.S.doeshavealonghistoryofelectionfraud,wedonotbelieveithaseverbeenthiswell-documentedpriortotheendoftheelectioncycle.

PhilipB.Stark,astatisticsprofessoratUCBerkeley,whohasbeeninstrumentalindesigningnew

auditingtechniquesforelectionsagreesthat,“Closerscrutinyofelectionsisnecessaryandwelcome…thatrequiresapapertrail,convincingevidencethatthepapertrailiscompleteandaccurate,andarisk-limitingauditofthepapertrailorafullmanualtallytoprovideconvincingevidencethatthepapertrail

matchestheannouncedresult.”

BethClarksonadds,“It’spossiblethattodoagoodauditwouldbemoreexpensiveandlesstransparentthanahandcountbecausetodoagoodauditrequiresexpertise.”

SecuringOurDemocracy

Thelackofaccuracyinourelectionsistrulyabetrayalofourancestorswhofoughtanddiedforthedemocraticprocess.ItisabetrayalofthesoldierswholosttheirfeettoamputationintheValleyForge

winter.Itisabetrayalofthewomenwhowenttoprisonandstarvedthemselvestojointhefranchise.Itisabetrayalofthecivilrightsworkerswhodiedfortherighttoregistertovote.Therecanbenodebate


aboutwhetherornotthevoteisaccurate.WemustknowthatitisaccuratethewayweknowthattheEarthrevolvesaroundthesunandnotviceversa.

Thepeoplewhowintheseelectionswilldecidewhetherornotwegotowar,howmanypeopleget

whatjobs,whereourchildrenattendwhatqualityschools,thequalityoftheairwebreatheandthewaterwedrink,andsomuchmore.Theentireidentityofournationrestsonourself-imageasaself-correctingdemocracywhoseleadersareaccountabletothevotersthatelectthem.Tonotknowwith

100%confidencethatthoseleadersaretheleadersthatweactuallyvotedforistheveryessenceofanexistentialcrisis.Thisisastateofemergency.Wemustmoverapidlytosecuretheintegrityofthevote.

Weneedtoimmediatelyimplementrobustauditprocedures.Then,asrapidlyaspossible,wemustjointheotherlegitimatedemocraciesoftheworldandimplementasystemofpaperballots,hand-counted

inasecureprocessthatisopentothepublic,invitesmediascrutiny,andhasstrongchainofcustodyprotocols.Inthisway,wecanachieveaccurate,verifiableresults.EachcitizenoftheUnitedStates,andindeedtheworld,deservesthisfromus.

AbouttheTeam

luluFries’datisanEdwardR.Murrowaward-winningjournalistanddocumentaryfilmmaker.She

receivedaBestDocumentaryawardforherfirstfeature-lengthdocumentary(producer/director)HollerBack—[not]VotinginanAmericanTown;afilmthatexploressystemicissuesinourelectionsthatdiscouragevoterparticipation.Clipsareavailableforviewinghere.Hernetworknewsexperience

includesassignmentsasaneditorforCBSEveningNews,Nightline,SundayMorning,TheTodayShow,andGoodMorningAmerica;SheproducedandeditedprofilesofDemocraticcandidatesforMSNBCand

hasdonelong-formatdocumentaryworkwithNBCNewsandCNBC.ShewasontheeditingteamofGideon’sArmy,anEmmy-nominateddocumentarythatfollowsthepersonalstoriesofpublicdefendersintheDeepSouth.Forachangeofpace,shecreatedanddirectedvoterregistrationPSAsforVH1that

featuredTriumphtheInsultComicDog.ShehasproducedanddirectedfrequentlywithherindependentproductioncompanyShugahWorks,includingthreeshortfilmsforNewYorkCityschools.Herfullbioisavailablehere.Followherontwitter@shugahworks.

AnselmoSampietroholdsaMasterofStatisticsdegreefromtheUniversityofBologna,Italyandhasalso

studiedattheUniversityofTechnologySydney,inAustralia,andtheUniversityofWarsaw,inPoland.HecurrentlyleadsateamofdataanalystsforacompanybasedinLondon.Hecollaborated(throughInnoCentive)withGeneralFusion,aCanadianstart-upthatisdevelopingcleanandreliablenuclear

fusionreactors,tobuildastatisticalmodelpredictingplasmaperformance.HehasspecializedintheuseofstatisticalanalysissoftwareR,whichistheanalytictoolusedthroughoutthisarticle.

FritzScheurenisaSeniorFellowandVicePresidentatNORCintheCenterforExcellenceinSurveyResearch.Scheurenhasanunparalleledrecordofworkoncomplexsubstantivetasksrelatedto


samplingandtotheanalysisofdatafromgovernmentagenciesandprivatesectorinstitutions.NotableworkatNORCincludesalandmarkprojectresolvingissueswithIndianTrustFundaccountsattheU.S.

DepartmentoftheInterioraswellasworkwiththeFederalReserve,particularlytheSurveyofConsumerFinancesandattheCensusBureau.ScheureniscurrentlyleadingimpactevaluationsofMillenniumChallengeCorporation(MCC)programsinGeorgiaandLesotho,andformerlyadvisedthe

MCConimpactevaluationsinArmeniaandVanuatu.Hebringsaprovenabilitytosolvehard,non-standardproblemsandthetheoreticalbackgroundtobackthesesolutionsupwithsoundstatisticalarguments.

ScheurenalsoservesontheStatisticsFacultyatTheGeorgeWashingtonUniversity,wherehehadbeen

aVisitingProfessorofStatistics.AtGWU,hecreatedasuccessfulsurveysamplingcertificateprogramwhichhestillteaches.Mostofhisover450appliedandtheoreticalpapers,presentations,monographs,andbooksareonsamplingaspectsofdatacollectionprimarilyinasurveycontext,withanemphasison

sampledesignandestimation,processquality,andthehandlingofmissingdata.Hehaswrittenoverhalfadozenbooks.

Scheurenservedatthe100thPresidentoftheAmericanStatisticalAssociationandchairedtheASASectionsonSurveyResearchMethodsand,later,theSocialStatisticsSection.Hehasbeentheir

ScientificSecretaryattheInternationalAssociationofSurveyStatisticians.HeisaFellowoftheAmericanStatisticalAssociationandtheAmericaSocietyfortheAdvancementofScience.

ElectionJusticeUSA–thepaperwasassistedinitsresearchanddevelopmentbyElectionJusticeUSA,anationalnon-partisancoalitionofseasonedelectionintegrityexperts,statisticians,attorneys,journalists

andactivists,whosemissionistomakesureeachAmerican'srighttovotecanbeexercisedwithoutissueinaccurateandhonestelections.


LinkstoElectionResults

2000WIRepublicanpresidentialprimary

2000FLAlachuaCountyDemocraticpresidentialprimary

2004FLAlachuaCountyDemocraticpresidentialprimary

2016DEDemocraticpresidentialprimary

2016LADemocraticpresidentialprimary

2016NewYorkDemocraticpresidentialprimary:

http://vote.nyc.ny.us/downloads/csv/election_results/2016/20160419Presidential%20Primary/01000200000Citywide%20Democratic%20President%20Citywide%20EDLevel.csv

2016NYBroomeCountyDemocraticpresidentialprimary

2016NYColumbiaCountyDemocraticpresidentialprimary

2016WIRepublicanandDemocraticpresidentialprimary

LinkstoChoquetteandJohnson’sElectionData

Version1.5

Version2.1

LinkstoClarkson’sElectionData

LinkstoLindeman’sElectionData

VotingEquipmentbyType

Wisconsinvotingequipment