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Focused Assessment Program Exhibit 6A Appendix I

Sampling Steps

I. PLAN THE SAMPLE

A. Decide whether or not to sample. (Applies to all circumstances.)

1. Define the audit objective.

a) he audit objective usua!!" comes direct!" from the audit program oris a variation that has been modified b" the auditor to fit the specificcircumstances. If there is no standard audit program# the auditormust define an audit objective appropriate for the uni$ue audit.

b) %onsider a!! &no'!edge avai!ab!e to date. A!! avai!ab!e informationabout the compan" and its %ustoms transactions shou!d be

considered in p!anning the audit and an" re$uired samp!ing. hisinformation ma" come from prior audits# historica! fi!es# profi!es#$uestionnaires# ris& assessment# surve" resu!ts# input from other%ustoms discip!ines# etc. his information 'i!! he!p in refining theaudit objective and the audit tests re$uired to achieve that objective.

c) (nce the audit objective is defined# audit testing can be designed toachieve that objective. he appropriate audit testing 'i!! var"depending on the audit objective.

. Identif" the avai!ab!e data# records# and supporting documents.

a) he avai!ab!e information# its method of storage and retrieva!# and itsformat 'i!! direct!" impact the audit tests that can and shou!d beapp!ied.

b) For examp!e# if no e!ectronic fi!es are avai!ab!e# this 'ou!d severe!"!imit the macro ana!"sis that cou!d be performed and 'ou!d restrictthe samp!ing options as 'e!!.

*. Determine if macro ana!"sis is possib!e and 'i!! achieve the auditobjective.

a) +acro ana!"sis is an" high,!eve! ana!"sis not invo!ving the revie' ofindividua! items or transactions. +acro ana!"sis ma" inc!ude suchprocedures as considering tota! va!ue ba!ances or tota! dut" paid#ca!cu!ating potentia! va!ue or dut" impact# extracting and-orcomparing data and tota!s from %ustoms and importer s"stems#ana!"ing variances# ana!"ing specific characteristics of extracteddata# and ana!"ing re!evant data trends.

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b) +acro ana!"sis is a &e" part of assessing ris& exposure but ma" a!sobe used an"time it 'i!! he!p satisf" the audit objectives. It can bemore efficient and more precise than samp!ing and therefore# shou!dbe considered first. If macro ana!"sis 'i!! achieve the audit objective#then there is no need to perform the remaining samp!ing steps

herein. horough!" document a!! aspects of the macro ana!"sisperformed in comp!iance 'ith audit documentation po!icies.

c) +icro testing# on the other hand# is the revie' of individua! items ortransactions 0samp!ing) usua!!" in order to ma&e conc!usions aboutthe popu!ation or universe from 'hich the" are dra'n. heremaining steps pertain to such micro testing or samp!ing.

B. If macro analsis is not s!fficient to achie"e the a!dit o#$ecti"e% decideon nonstatistical &$!dgmental' or statistical sampling. (Applies tononstatistical and statistical sampling.)

1. Define the samp!ing objective. he specific samp!ing objective 0i.e.# thereason to samp!e# the $uestion "oure tr"ing to ans'er about theuniverse# 'hat "oure tr"ing to test-measure# the audit statement "ouneed to ma&e# etc.) 'i!! he!p determine 'hether nonstatistica! orstatistica! samp!ing is appropriate.

. 2onstatistica! samp!ing re!ies on auditor judgment to se!ect the samp!eitems and eva!uate the samp!e resu!ts 0except in the case of 1//3revie' 'here actua! resu!ts are &no'n). 4tatistica! samp!ing is anobjective process for random!" se!ecting the samp!e items andstatistica!!" eva!uating the samp!e resu!ts.

*. here are specific !imited circumstances in 'hich nonstatistica! samp!ingis appropriate. 2onstatistica! samp!ing is suitab!e if statistica! resu!ts arenot needed# there is a high degree of certaint" that a conc!usion can bedra'n 'ithout further samp!ing# and

a) the purpose is to ta&e a surve" in order to determine the necessit"for and extent of substantive tests# and-or.

b) there is a desire to concentrate audit effort in specific prob!em arearevea!ed b" a previous samp!e or other source of information# and-or

c) the universe is ver" sma!! and it 'ou!d be $uic&er and easier torevie' a!! or most of the items in the universe# and-or

d) the area is ver" sensitive and there is no room for error or exactresu!ts are needed so a!! of the items in the universe 'i!! berevie'ed.

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5. It is important to consider the first part of the re$uirement fornonstatistica! samp!ing 0i.e. statistica! resu!ts are not needed and there isa high degree of certaint" that a conc!usion can be dra'n 'ithout furthersamp!ing) because it is genera!!" not appropriate to ca!cu!ate comp!iancerates or to project do!!ar impacts 0va!ue or revenue) based on resu!ts of

sma!! nonstatistica! samp!es. %omp!iance rates and do!!ar impacts cou!dbe based on resu!ts of 1//3 revie's because the" represent actua!resu!ts.

. If statistica! resu!ts are needed or "ou need more than a nonstatistica!samp!e to ma&e a conc!usion 0e.g.# objective resu!ts# projections to theuniverse 'ith measurab!e precision# or comp!iance rates)# thennonstatistica! samp!ing is not appropriate 0un!ess 1//3 revie' ispossib!e).

6. If nonstatistica! samp!ing is chosen# s&ip to samp!ing step I.D. If

statistica! samp!ing is chosen# continue 'ith samp!ing step I.%. be!o'.(. If nonstatistical sampling will not satisf the sampling o#$ecti"e% decide

on which tpe of statistical sampling &attri#!te disco"er or "aria#lesampling' is appropriate. (Applies to statistical sampling.)

1. Attribute discover" samp!ing is a specia! &ind of attribute acceptancesamp!ing 'here the occurrence of even a sing!e error constitutes afai!ure of the universe. 7ariab!e samp!ing is a form of substantive testingthat is $uantitative in nature and can be used to determine varianceamounts or do!!ar impacts 0e.g.# materia!it",based comp!iance rates#revenue due# etc.).

. Attribute discover" samp!ing is appropriate 'hen the area of revie' issensitive and an" s"stemic error 'ou!d constitute noncomp!iance 0andpotentia!!" fraud). his ma&es it appropriate for the revie' oftransshipment or undec!ared ADD-%7D. Attribute discover" samp!ing isa!so appropriate 'hen no error is expected or errors resu!t in pena!tiesrather than revenue due 0such as bro&er or bonded 'arehouse audits).

*. 7ariab!e samp!ing shou!d be used in a!! other circumstances 'herestatistica! samp!ing is appropriate. 7ariab!e samp!ing ma" be ph"sica!unit samp!ing 0'here individua! items or ph"sica! units are se!ected) or

do!!ar unit samp!ing 0'here individua! do!!ars are se!ected).

D. Select the sampling frame and !nit. (Applies to nonstatistical andstatistical sampling.)

1. Identif" avai!ab!e frames# their samp!ing units# and formats 0i.e.#e!ectronic# hard cop" printout# or ph"sica! items).

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a) 8hether nonstatistica! or statistica! samp!ing is used# potentia!samp!ing frames and samp!ing units must be identified. A samp!ingframe is the ph"sica! or e!ectronic representation of the universefrom 'hich the samp!e is se!ected. he universe is the entire groupof items comprising the categor" or area of interest to the auditor 0to

be tested). he samp!ing units are the individua! units 0e.g.# items#transactions# !ines# do!!ars# ph"sica! fi!es# etc.) that are se!ected forrevie'.

b) he avai!ab!e frames and units must be eva!uated to determine'hich 'i!! best satisf" the audit and samp!ing objectives and the bestsamp!ing approach to ta&e. An e!ectronic frame is a!'a"s superior toan identica! ph"sica! frame or !isting because it provides moref!exibi!it" and efficienc" in the areas of frame ana!"sis# samp!ese!ection# and samp!e resu!ts eva!uation.

. %onsider the !eve! of summariation of the frame and units and identif"the avai!ab!e supporting records-documents and their !eve! ofsummariation.

a) Frames# units and supporting records and documentation can be atvarious !eve!s of summariation. he" ma" be at a ver" high !eve! ora ver" !o' !eve!. For examp!e# an entr" is made up of man"entr"-tariff !ines# 'hich ma" be made up of man" invoices# 'hichma" be made up of man" invoice !ines# 'hich ma" be made up ofman" parts-artic!es# 'hich ma" be made up of man" st"!es# 'hichma" be made up of man" sies and co!ors. Importer records anddocuments ma" group information simi!ar!" or b" man" othergroupings such as b" !ot# container# purchase order# date received#batch processed# month# supp!ier# merchandise categor"# etc.

b) (ften# the higher the !eve! of samp!ing# the more difficu!t the revie'because the more items and supporting documents that have to berevie'ed. 9ut this is not a!'a"s the case. It depends on the samp!eitems 0nature# !eve! of summariation and number) and the avai!ab!esupporting records and documentation 0ph"sica! or e!ectronic# !eve!of summariation# and effort re$uired to trace and verif" the samp!eitems).

c) he idea! situation is one in 'hich the supporting records anddocuments are summaried at the same !eve! as the samp!e items orone in 'hich the samp!e items are easi!" traced through and verifiedb" the supporting records and documents. Prob!ems occur orsignificant extra effort ma" be re$uired 'hen this is not the case 0i.e.#the samp!e items and supporting records and documents are at ver"different !eve!s and-or the samp!e items are not easi!"traced-verified).

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d) A!so &eep in mind the audit and samp!ing objectives , 'hat is beingtested. If the entered-reported data is being tested# then it 'ou!d notbe effective or efficient to samp!e and verif" at a much !o'er !eve!than that 'hich is reported 0e.g.# samp!ing at a !eve! of merchandiseco!or 'hen a!! co!ors are proper!" combined on an entr" !ine for

reporting c!assification# $uantit"# and va!ue).

*. 9ased on the avai!ab!e choices# se!ect the best frame and unit toeffective!" and efficient!" accomp!ish the audit and samp!ing objectives.

a) As&: ;If I se!ect this frame and samp!ing unit# 'hat am I rea!!" testingand 'hat procedures 'i!! I have to perform< 8hat and ho' man"records and documents 'i!! I have to revie'< 8hat difficu!ties 'i!! Ihave tracing the samp!e items through the records and documents<8hat manua! or e!ectronic ca!cu!ations or summariation 'i!! I haveto perform in order to trace and verif" the samp!e items< 8i!! this

satisf" the audit and samp!ing objective< Is there a better 0moreefficient or effective) frame or samp!ing unit<

b) An e!ectronic fi!e genera!!" 'or&s best 'ith an" &ind of nonstatistica!or statistica! samp!ing. If an e!ectronic fi!e is not avai!ab!e# aprintout-!isting or a ph"sica! item frame can be used for nonstatistica!and variab!e ph"sica! unit samp!ing. A sma!! printout or !isting thatcou!d easi!" be t"ped into E=,>uant cou!d be used for variab!e do!!arunit samp!ing.

c) If nonstatistica! samp!ing is being used# s&ip to samp!ing step I.?. Ifstatistica! samp!ing is being used# continue 'ith samp!ing step I.E.

be!o'.

E. )alidate the frame. (Applies to statistical sampling.)

1. he purpose of frame va!idation is to determine if it is an ade$uaterepresentation of the universe intended for testing

a) @emove credit-negative items and ero ba!ance items from theframe. Proper samp!ing re$uires that dup!icate items 0e.g.#credit-negative items 'ith corresponding debit-positive items) andero va!ue items that have more than one chance or no chance of

se!ection be removed from the frame either for separate revie'0separate samp!e or 1//3 revie') or for no revie'.

b) %ompare-reconci!e the chosen frame 'ith the intended universe oranother potentia! frame to tr" to verif" that it is a comp!ete andaccurate !isting suitab!e for the intended objective.

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For examp!e# if the intended universe is a!! B4P parts received and aframe extracted from an importer parts database is chosen# thisframe cou!d be compared to B4P reported to %ustoms in A%4. (r ifthe intended universe is a!! imported va!ue and c!assifications and aframe of A%4 entries is chosen# this frame cou!d be compared to the

inventor" receipts 0a!! imports) for the "ear.

. he primar" purpose of these t"pes of comparisons is to ensure that "ouhave good data from 'hich to samp!e. ?o'ever# as a form of macroana!"sis# these reconci!iations cou!d a!so revea! additiona! ris& areas orpotentia! prob!ems# such as potentia! unreported va!ue# misc!assifiedmerchandise# over,dec!arations of B4P# under,dec!arations of

ADD-%7D# etc.

*. Ana!"e an" variances and adjust the frame# accept the frame# or rejectthe frame and se!ect another as appropriate.

a) here are man" things that might cause a variance bet'een theframe and universe 0or t'o frames representing the sameinformation). 4ome common causes of variances are as fo!!o's:

01) iming or time frame differences. here are various dates in%ustoms A%4 s"stem 0create date# entr" date# export date) 'hichare usua!!" different from the dates in the Importers s"stem 0receiveddate# order date# paid date). herefore# there cou!d be some timingdifferences 'hen tr"ing to compare A%4 data 'ith importer data.

0) Exc!uded items. Due to the comp!exities of data and data s"stems

and the potentia! for miscommunication# it is common for 'ho!ecategories of data to be exc!uded from one frame or another. hismight be data associated 'ith a particu!ar countr"# vendor# division#importer ID# or bro&er. (r it might be data assumed to be uni$ue#dissimi!ar# or irre!evant such as samp!es# merchandise purchased foruse rather than resa!e# returns# merchandise in transit# dropshipments# consignments# or informa! entries.

0*) Prob!ems 'ith the data source or EDP s"stem. 4ometimes data isincomp!ete because on!" one partia!!" comp!ete source 'asaccessed 'hen the rest of the data is contained in another fi!e#database# or s"stem.

b) 7arious methods can be emp!o"ed to identif" the cause of avariance. >uestioning about merchandise receipt timing 'i!! he!p toidentif" and adjust for timing variances. %omputer ana!"ses 0such assumming and comparing tota!s b" countr"# +ID and vendor# tariffand merchandise descriptions) ma" he!p identif" missing categoriesof data. Brouping $ueries ma" sho' that dup!icate records arepresent.

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c) (nce the cause has been determined# a decision must be made ifthe frame can and shou!d be corrected or adjusted# accepted as is#or rejected and another frame used instead. he &e" 'i!! be theaudit and samp!ing objective 0the intended universe and testing) and'hether adjustments are actua!!" viab!e.

5. If attribute discover" samp!ing is being used# s&ip to samp!ing step I.?. Ifvariab!e samp!ing is being used# continue 'ith samp!ing step I. F. be!o'.

*. Anal+e the frame "aria#ilit and anticipated,potential errors. (Appliesto statistical variable sampling.)

1. Frame variabi!it" refers to the differences and simi!arities amongsamp!ing units 'ithin the frame# in terms of do!!ar amounts andcharacteristics.

. he degree of frame variabi!it" 'i!! he!p determine the re$uired samp!e

sie and the best samp!ing approach.

a) Determine the s&e'ness b" ca!cu!ating the measures of centra!tendenc".

01) %a!cu!ating the mean# median# and mode 0A7E@ABE# +EDIA2# and+(DE functions in +icrosoft Exce!) 'i!! indicate the s&e'ness of theframe. If the mean is greater than the median# then the frame is rights&e'ed# meaning that there are a fe' high do!!ar items and man" !o'do!!ar items. If the mean is !ess than the median# then the frame is!eft s&e'ed# meaning there are a fe' !o' do!!ar items and man" !argedo!!ar items. he greater the difference bet'een the mean andmedian# the greater the s&e'ness. 4&e'ness is an indication ofdo!!ar variabi!it" and ma" a!so point to the need for horionta!stratification 0b" do!!ar amount).

0) A high!" s&e'ed universe 0!eft or right) 'ou!d point to'ards a !argerstratified ph"sica! unit samp!ing. A high!" !eft s&e'ed universe 'ou!dpoint to'ards a !arger do!!ar unit samp!ing.

b) Determine the do!!ar variabi!it" b" ca!cu!ating the indices ofdispersion 0standard deviation and coefficient of variation).

01) 4tandard deviation is the average distance of individua! va!ues or theextent to 'hich individua! va!ues depart from the average. In+icrosoft Exce!# it can be ca!cu!ated b" using the 4DE7P function.he !arger the standard deviation# the more variation. 0Do not usean" other 4D functions in +icrosoft Exce! as 'i!! resu!t in a different#incorrect resu!t.)

0) he coefficient of variation 0%7) is the standard deviation expressedas a percentage. he formu!a is 4tandard Deviation of the frame -

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+ean of the frame 1//. he higher the %7# the more do!!arvariation in the frame. Benera!!"# a %7 /3 indicates !o' variation#a %7 bet'een /3 and 1//3 indicates moderate to high variation#and a %7 over 1//3 indicates ver" high variation.

0*) A higher %7 0/3) 'ou!d point to'ards a !arger stratified ph"sica!unit samp!e or a !arger do!!ar unit samp!e.

c) Determine if there are obvious do!!ar brea&s or groupings 0forhorionta! stratification). 0App!ies if the universe is high!" s&e'edand-or the %7 /3.)

01) ?igh s&e'ness# standard deviation# and %7 indicate high do!!arvariation and probab!" a need to stratif" at !east horionta!!" 0ondo!!ars) and possib!" vertica!!" 0on characteristics). 4orting the framein +icrosoft Exce! 0b" do!!ar amount) ma" revea! c!ear divisions orgroupings of simi!ar do!!ar amounts. 0his t"pe of ana!"sis ma" a!so

be performed b" creating various tab!es and reports in +icrosoft Access.)

0) (bvious do!!ar brea&s or groupings 'ou!d point to'ards a !argermanua!!" stratified ph"sica! unit samp!e. It cou!d a!so point to'ards ado!!ar unit samp!e 'ith a 1//3 revie' high do!!ar stratum if theobvious do!!ar brea& is bet'een high do!!ars and the rest of theframe.

d) Ana!"e the characteristics to determine if !ogica! groupings exist 0forvertica! stratification).

01) Ana!"ing the frame in +icrosoft Exce! 0sorting# subtota!ing# creatingpivot tab!es# etc.) b" description# part number# ?4 or tariff number#account number# product !ines# sie# $uantit" or an" other re!evantcharacteristic ma" revea! common characteristics or categories thatshou!d be grouped together. 0his t"pe of ana!"sis ma" a!so beperformed b" creating various tab!es and reports in +icrosoft

Access.)

0) A high!" variab!e frame in terms of characteristics 'ou!d pointto'ards a !arger stratified ph"sica! unit samp!e or mu!tip!e do!!ar unitsamp!es.

e) Identif" specia!# ver" !o' ris&# or ver" high ris& items and decide'hether to !eave them in the frame for random samp!ing or removethem from the frame for no revie' or 1//3 revie'.

01) hese ma" be# for examp!e# ver" !o' do!!ar items# ver" high do!!aritems# informa! entries# consignee entries# etc.

0) 7er" !o' do!!ar items ma" be e!iminated from the frame IF the teamagrees that there are no potentia! significant issues or errors that

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cou!d occur. 9e cautious about automatica!!" assuming that !o'do!!ar items are insignificant. It ma" not be appropriate to exc!udethe !o' do!!ars if:

0a) the frame contains c!usters 0the apparent!" !o' do!!ar itemsma" not be !o' in comparison to the individua! items ma&ingup the c!usters)#

0b) the" are significant in the aggregate#

0c) the" represent sensitive specia! trade areas# or

0d) the va!ue is &no'n or suspected to be significant!"understated.

0*) For examp!e# one compan" had approximate!" H*/#/// in !o' do!!arsamp!e merchandise out of H6 mi!!ion tota! reported va!ue. hese'ere !eft in the frame and some 'ere chosen in the samp!e. Duringthe attempt to support the samp!e merchandise va!ue# the importerdiscovered the" 'ere significant!" underva!ued and submitted adisc!osure for approximate!" H1. mi!!ion 'ith revenue due of aboutH*//#///. If these !o' do!!ar items had been de!eted from the framefor no revie'# the errors 'ou!d not have been discovered and this!oss of revenue 'ou!d not have been recovered.

*. Define the anticipated or potentia! errors.

a) he fre$uenc"# t"pes# and amounts of the anticipated or potentia!errors 'i!! he!p to determine the best samp!ing methodo!og" for the

situation.

b) Fre$uent errors# inc!uding sma!! errors# 'ou!d point to'ards ph"sica!unit samp!ing. Infre$uent !arge errors 'ou!d point to'ards do!!ar unitsamp!ing.

-. Determine the #est "aria#le sampling method &phsical !nit or dollar!nit' #ased on the res!lts of the frame analsis. (Applies to statisticalvariable sampling.)

1. Ph"sica! unit samp!ing genera!!" 'or&s best 'ith:

a) An e!ectronic frame# a printout or !isting frame# or a ph"sica! itemframe.

b) An" amount of frame variabi!it"# inc!uding one that is high!" variab!ein terms of do!!ars and characteristics.

c) 4amp!ing units that are individua! items or samp!ing units that arec!usters of items 'here revie'ing the entire c!uster is acceptab!e

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0i.e.# the c!usters consist of a fe' items and-or revie'ing 'ho!ec!usters 'ou!d not re$uire significant additiona! effort to trace throughthe supporting records and documents).

d) he anticipated or potentia! errors are fre$uent# inc!uding sma!!

errors.

. Do!!ar unit samp!ing genera!!" 'or&s best 'ith:

a) An e!ectronic frame or a sma!! printout or !isting frame that cou!d bet"ped into E=,>uant.

b) A frame that is not high!" variab!e or a frame that is high!" variab!e interms of do!!ars 0especia!!" !eft s&e'ed) but not in terms ofcharacteristics. 0Do!!ar unit samp!ing ma" be used 'ith a frame thatis high!" variab!e in terms of characteristics# but it 'ou!d re$uiremu!tip!e do!!ar unit samp!es.)

c) 4amp!ing units that are c!usters of items 'here revie'ing the entirec!uster is not acceptab!e 0i.e.# revie'ing entire c!usters 'ou!d re$uiresignificant additiona! effort to trace through the supporting recordsand documents).

d) he anticipated or potentia! errors are infre$uent !arge errors.

H. Esta#lish appropriate sample,strata si+es and sampling parameters.(Applies to nonstatistical and statistical sampling.)

1. 2onstatistica! 0judgmenta!) samp!es.

a) 4amp!e sies for nonstatistica! samp!es 'i!! depend on the t"pe ofaudit# audit objective# and samp!e objective.

b) For Focused Assessment 0FA) Pre,Assessment 4urve" 0PA4)#samp!e sies 'i!! be 1 to / depending on the resu!ts of the initia! ris&exposure and interna! contro! assessment as fo!!o's:

01) Jo' ris& exposure and strong interna! contro!s K !o' end of 1 to 1/range.

0) Jo' ris& exposure and ade$uate interna! contro!s K midd!e of 1 to 1/range.

0*) Jo' ris& exposure and 'ea& interna! contro!s K high end of 1 to 1/range.

05) +oderate ris& exposure and strong interna! contro!s K !o' end of to1 range.

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0) +oderate ris& exposure and ade$uate interna! contro!s K midd!e of to 1 range.

06) +oderate ris& exposure and 'ea& interna! contro!s K high end of to1 range.

0C) ?igh ris& exposure and strong interna! contro!s K !o' end of 1/ to /range.

0G) ?igh ris& exposure and ade$uate interna! contro!s K midd!e of 1/ to/ range.

0) ?igh ris& exposure and 'ea& interna! contro!s K high end of 1/ to /range.

c) For most other audits# nonstatistica! samp!e sies 'i!! genera!!" be1//3 of the revie' area. If the revie' area is much !arger than a

norma! statistica! samp!e sie 06/ to 1//)# then statistica! samp!ingshou!d be considered instead of nonstatistica! samp!ing.

. Attribute discover" samp!es.

a) 4amp!e sies for attribute discover" samp!es are determined b"running E=,>uant ADI4% 0D(4 7ersion *.1/) or Attribute 4amp!e4ie Determination Procedure 08indo's 7ersion 1./.1). heprocedure 'i!! genera!!" resu!t in samp!e sies 'ithin the range of to /# depending on the frame sie and specified samp!ingparameters of critica! error rate and government ris&. he critica!error rate is the maximum acceptab!e error rate in the universe. hegovernment ris& is the to!erab!e !eve! of ris& of accepting a fau!t"universe 0one 'ith an actua! error rate exceeding the critica! errorrate).

01) For circumstances 'here an" s"stemic error resu!ts innoncomp!iance 0e.g. FA Assessment %omp!iance esting or Fo!!o',up of transshipment or undec!ared ADD-%7D)# the appropriateparameters to use are 3 critica! error rate and 13 government ris&.

0) For those instances 'hen no errors are anticipated or errors resu!t inpena!ties rather than revenue due 0e.g.# bro&er or bonded 'arehouse

audits)# the appropriate parameters to use are 3 critica! error rateand 3 government ris&.

b) A!though the purpose of an attribute discover" samp!e is todetermine if an" error exists rather than estimate do!!ar impacts#there cou!d be situations in 'hich estimating do!!ar impacts based onthe samp!e resu!ts is appropriate or necessar". A desired precisionpercentage under 1//3 and confidence !eve! of 3 0same as for

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variab!e samp!ing) shou!d be estab!ished for just such a possibi!it".he desired precision percentage shou!d be based on auditor

judgment of 'hat 'ou!d be acceptab!e for the situation. heachieved precision percentage 'i!! be compared to the desiredprecision percentage 'hen determining the acceptabi!it" of the

projection.

*. 7ariab!e samp!es.

a) 7ariab!e samp!e sies depend on the variabi!it" in the samp!ingframe. he more variabi!it" in the frame 0do!!ars and characteristics)#the !arger the samp!e sie re$uired to achieve acceptab!e samp!eresu!ts. +inimum samp!e sie guide!ines 0based on statistica!princip!es) have been estab!ished to assist auditors in determiningappropriate variab!e samp!e sies.

01) Ph"sica! unit samp!es.

0a) If the frame is homogenous# then the minimum samp!ere$uired is 1 samp!e 'ith 1 stratum of 6/ items. Ahomogenous frame is one 'ith !o' variabi!it" in do!!ars andcharacteristics 0i.e. simi!ar do!!ars and characteristics).Indicators of !o' do!!ar variabi!it" are !o' s&e'ness# !o'standard deviation# !o' %7 0 /3) and no obvious do!!arbrea&s or groupings. Jo' characteristic variabi!it" 'ou!d be aframe 'ith no obvious groupings b" characteristics.

0b) If the frame is nonhomogenous# then the minimum samp!ere$uired is 1 samp!e of * random strata p!us 1 high do!!ar1//3 revie' stratum. A nonhomogenous frame is one 'ithhigh variabi!it" in do!!ars and characteristics 0i.e. dissimi!ardo!!ars and characteristics). Indicators of high do!!ar variabi!it"are high s&e'ness# high standard deviation# high %7 0 /3)and obvious do!!ar brea&s or groupings. ?igh characteristicvariabi!it" 'ou!d be a frame 'ith obvious groupings b"characteristics. he tota! samp!e sie shou!d be at !east 1//items. Each random stratum shou!d be at !east */ items#except 'hen */ items 'ou!d be more than 3 of the items inthe entire stratum. In that case# the stratum sie can be 3 or1 items# 'hichever is greater.

0c) Benera!!"# the !arger the tota! samp!e sie and the more strata#the better the achieved precision 'i!! be.

0) Do!!ar unit samp!es.

0a) If the frame is homogenous# then the minimum samp!ere$uired is 1 samp!e of 1// units. A homogenous frame is one'ith !o' variabi!it" in do!!ars and characteristics 0i.e. simi!ar

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do!!ars and characteristics). Indicators of !o' do!!ar variabi!it"are !o' s&e'ness# !o' standard deviation# !o' %7 0 /3)and no obvious do!!ar brea&s or groupings. Jo' characteristicvariabi!it" 'ou!d be a frame 'ith no obvious groupings b"characteristics.

0b) If the frame is nonhomogenous due to high do!!ar variabi!it"#then the minimum samp!e re$uired is 1 samp!e of 1// units.Indicators of high do!!ar variabi!it" are high s&e'ness# highstandard deviation# high %7 0 /3) and obvious do!!ar brea&sor groupings.

0c) If the frame is nonhomogenous due to high characteristicvariabi!it"# then the minimum samp!es re$uired are mu!tip!esamp!es of 6/ units each 0one for each characteristicgrouping). ?igh characteristic variabi!it" 'ou!d be a frame 'ithobvious groupings b" characteristics. Ph"sica! unit samp!ingis usua!!" better at hand!ing high variabi!it" in characteristics.9ut if c!usters are present 'hich 'ou!d be difficu!t to revie' intheir entiret" and if there on!" or * major characteristicgroupings# then do!!ar unit samp!ing ma" sti!! be used.

0d) Benera!!"# the !arger the tota! samp!e sie 0or the moresamp!es for characteristic variabi!it")# the better the achievedprecision 'i!! be.

b) 4amp!ing parameters for variab!e samp!es 'i!! be 3 confidence!eve! and desired precision percentage 1//3. he desiredprecision percentage shou!d be based on auditor judgment of 'hat is

acceptab!e for the situation. he achieved precision percentage 'i!!be compared to the desired precision percentage 'hen determiningthe acceptabi!it" of the projection.

II. SELE(T THE SAMPLE

A. Nonstatistical samples.

1. 4ince nonstatistica! samp!ing is based on auditor judgment# an" se!ectionmethod appropriate for the circumstances ma" be used. he auditorshou!d &eep in mind the audit and samp!ing objectives 'hen determiningthe best se!ection process.

. 4ome common techni$ues are as fo!!o's:

a) Purposive testing is a method that attempts to se!ect samp!e items'ith &no'n or suspected prob!ems. his method 'ou!d beappropriate for the FA PA4# 'hich is a ris&,based surve" to findprob!ems if the" exist. he auditor 'ou!d se!ect the highest ris&areas-items.

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b) %ross,section testing is a method that se!ects samp!e items from a!!parts of the area being tested. A common techni$ue is to designatea fixed percentage to test# such as 3# and then se!ect ever" nthitem to reach the 3. If this method emp!o"ed a random start# it'ou!d actua!!" be a statistica! s"stematic interva! se!ection. 9ut

often# items are just chosen haphaard!" across the area beingtested unti! the desired $uantit" is obtained. his method 'ou!d beappropriate for FA PA4 if there 'ere no identified higher ris& areasor items on 'hich to focus

c) Jarge do!!ar testing is a method that se!ects the !argest do!!ar itemsfor revie'. Emphasis is p!aced on the materia!it" of the itemsse!ected. his cou!d be appropriate for FA PA4 if the higher do!!aritems are determined to be the highest ris& items. ?o'ever# &eep inmind that a brea&do'n of interna! contro!s is often more pronouncedin the !o'er do!!ar items.

d) 9!oc& testing is a method that se!ects specific b!oc&s of units. heb!oc&s ma" be periods of time or consecutive groupings# such as a!!expense vouchers in Lune or a!! invoices 'ith vendor namesbeginning 'ith the !etters + through P. his method 'ou!d beappropriate for FA PA4 on!" if the se!ected b!oc&s represent the highris& areas-items.

e) %onvenience testing is a method of se!ecting the most convenientsamp!e items for revie'. he most readi!" avai!ab!e items arese!ected# 'ithout reason or randomness# simp!" because it isexpedient. @ecords that are in storage# in the bottom or bac& of fi!edra'ers# not "et fi!ed# or at another !ocation are exc!uded 'hen thist"pe of testing is used. his method rare!" ref!ects good auditor

judgment# ma" be manipu!ated b" the auditee# and is notrecommended for an" audit situation.

B. Attri#!te disco"er and "aria#le phsical !nit sampling.

1. he same se!ection methods ma" be used for both attribute discover"and variab!e ph"sica! unit samp!ing because both statistica! samp!ingt"pes se!ect ph"sica! units for revie'.

. he fo!!o'ing are samp!e se!ection options:

a) E=,>uant @A2M+ 0D(4 7ersion *.1/) or @andom 2umbersBenerator 08indo's 7ersion 1./.1) is a procedure that generatesrandom numbers that can then be manua!!" or e!ectronica!!" app!ied0using macros or mini,programs) to a frame to se!ect the samp!eitems. It is suitab!e for an e!ectronic frame# a numbered printout or!isting# or a numbered ph"sica! item frame. It cou!d a!so be used 'ith

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a sma!! unnumbered printout-!isting or ph"sica! items frame# but theframe 'ou!d have to be manua!!" numbered before the samp!e itemscou!d be se!ected.

his procedure can be used for manua!!" stratified ph"sica! unit

samp!es. If obvious do!!ar brea&s or characteristic brea&s 'ereidentified during frame ana!"sis# then @A2M+ ma" be run for eachmanua!!" identified stratum to random!" se!ect the samp!e items.

b) E=,>uant @A4E> 0D(4 7ersion *.1/) or @andom 2umber 4etsBenerator 08indo's 7ersion 1./.1) is a procedure that generatessets of random numbers that can then be app!ied to a frame to se!ectthe samp!e items. It is suitab!e for an unnumbered printout-!isting oran unnumbered ph"sica! item frame 'ith a hierarchica! structure.For examp!e# the first number in the set 'ou!d represent the page ordra'er and the second number in the set 'ou!d represent the !ine on

the page or the fi!e in the dra'er. It can be used 'hen stratificationis not necessar"# the frame is a!read" stratified# or the frame can bestratified prior to samp!e se!ection.

c) E=,>uant 4@A 0D(4 7ersion *.1/) or Ph"sica! Mnit 4amp!e4e!ection Procedure 08indo's 7ersion 1./.1) is a procedure thatcan stratif" 0on do!!ars) and random!" se!ect ph"sica! units. It issuitab!e for an e!ectronic frame or a sma!! printout-!isting that can bet"ped into the program.

It can be used for attribute discover" samp!e se!ection b" specif"ing1 random stratum and no high do!!ar stratum-items.

For variab!e ph"sica! unit samp!es# the procedure 'i!! automatica!!"sort and stratif" the frame into e$ua! do!!ar strata# and then random!"se!ect samp!e items for each stratum. It 'or&s best 'ith a frame thatis high!" variab!e in terms of do!!ars# but not in terms ofcharacteristics. If obvious do!!ar brea&s or characteristic brea&s 'ereidentified during frame ana!"sis# then E=,>uant @A2M+ 0@andom2umbers Benerator)# ma" be used instead to random!" se!ectsamp!e items for each manua!!" identified stratum.

d) +anua! s"stematic interva! se!ection is a procedure for manua!!"

se!ects ever" nth item 'ith a random start. It shou!d be considered'hen the on!" avai!ab!e frame is an unnumbered ph"sica! item frameand se!ecting ever" nth item 'ou!d resu!t in a better cross,section ofitems or 'ou!d be easier and $uic&er than using @A4E>. heprocess is as fo!!o's:

01) Estimate the frame sie 0if un&no'n). It is better to underestimatethan overestimate.

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0) %ompute the interva! 0frame sie - desired samp!e sie). runcatethe resu!t to a 'ho!e number.

0*) @un E=,>uant @A2M+ 0@andom 2umbers Benerator) to get arandom start bet'een 1 and the interva!. he random start 'i!! bethe first samp!e item.

05) Add the interva! to the random start to get the second samp!e item.%ontinue adding the interva! to se!ect the rest of the samp!e items.

0) Do not automatica!!" stop 'hen the desired samp!e sie is achieved.he process is not comp!ete unti! the end of the frame is reached. ostop before the end of the universe 'ou!d inva!idate the statistica!samp!e because ever" item 'ou!d not have an e$ua! chance ofse!ection. he actua! samp!e sie ma" be s!ight!" !arger than theinitia! desired samp!e sie.

06) he samp!e ma" be proper!" expanded b" removing the previous!"se!ected samp!e items from the frame and repeating the above steps0ca!cu!ating a ne' interva!# running E=,>uant @A2M+ @andom2umbers Benerator to get a ne' random start# and se!ecting theadditiona! items from the revised frame).

0C) he samp!e ma" be proper!" decreased b" random!" 0using E=,>uant @A2M+ @andom 2umbers Benerator) se!ecting items forremova! from the entire samp!e. It 'ou!d not be proper to mere!"disregard the !ast items se!ected. o do so 'ou!d inva!idate thestatistica! samp!e because ever" item 'ou!d not have an e$ua!chance of se!ection.

e) (ther computer programs# such as +icrosoft Access or 4A4# ma" beused if the e!ectronic frame is too !arge to fit into +icrosoft Exce! 0forana!"sis# manua! stratification# or app!ication of random numbers) ortoo !arge to fit into E=,>uant 4@A Ph"sica! Mnit 4amp!e 4e!ectionProcedure 0for stratification and-or samp!e se!ection). Auditorsshou!d consu!t 'ith a %A4 if the" encounter this situation.

(. )aria#le dollar !nit sampling.

1. Do!!ar unit samp!ing is uni$ue in that it random!" se!ects do!!ars insteadof ph"sica! units. he se!ected do!!ars 0do!!ar hits) are then tied to

ph"sica! units 'hich are revie'ed.

. he fo!!o'ing se!ection methods ma" be used for do!!ar unit samp!ing:

a) E=,>uant DM44EJ 0D(4 7ersion *.1/) or Do!!ar Mnit 4amp!e4e!ection Procedure 08indo's 7ersion 1./.1) is an automateds"stematic interva! se!ection procedure. It 'or&s 'ith an e!ectronicframe or a sma!! printout-!isting that can be t"ped into the program.

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he procedure 'i!! identif" the do!!ar hits# but if the samp!ing unitsare c!usters# then the ph"sica! items associated 'ith each do!!ar hitmust be identified manua!!". his is done b" ca!cu!ating cumu!ativetota!s for the c!uster items and then !ocating the item 'ithin thec!uster that contains the do!!ar hit.

b) +anua! s"stematic interva! se!ection. his method manua!!" se!ectsever" nth do!!ar 'ith a random start. 8hi!e it is possib!e for use 'itha printout or !isting# it is genera!!" not recommended for do!!ar unitsamp!ing due to the amount of effort re$uired to manua!!" se!ect thedo!!ar hits.

c) (ther computer programs# such as +icrosoft Access or 4A4# ma" beused if the e!ectronic frame is too !arge to fit into +icrosoft Exce! 0forana!"sis) or too !arge to fit into E=,>uant DM44EJ Do!!ar Mnit4amp!e 4e!ection Procedure 0for samp!e se!ection). Auditors shou!d

consu!t 'ith a %A4 if the" encounter this situation.III. D(/MENT ALL ASPE(TS * THE SAMPLE PLANNIN- AND SELE(TIN

A. A!dit doc!mentation m!st f!ll and clearl doc!ment all aspects of thesampling that was !sed. This doc!mentation m!st #e prepared foreach sample &nonstatistical and statistical' and m!st compl with a!ditdoc!mentation policies.

B. The following sample planning and selection items sho!ld #e incl!dedfor each sample0

1. A samp!ing p!an that documents the samp!e p!anning and se!ection mustbe inc!uded. 4tandard samp!ing p!an forms for this purpose arecontained in Appendix I7. he sections !abe!ed 4amp!ing App!ication#4amp!ing Approach# Mniverse and Frame Information# and 4amp!eInformation pertain to samp!e p!anning and se!ection and shou!d becomp!eted at this point.

. he samp!ing frame itse!f must be inc!uded as part of the auditdocumentation. E!ectronic frames can be direct!" incorporated into theautomated 'or&ing papers. If the frame is hard cop"# it can be scannedin or maintained separate!" if too vo!uminous for scanning. If it is

maintained separate!"# it shou!d be proper!" exp!ained and referenced inthe automated documentation in accordance 'ith audit documentationpo!icies.

*. he procedures used to va!idate the samp!ing frame must bedocumented. An" ana!"sis or fi!e comparisons done in an attempt tova!idate the frame as an ade$uate representation of the intendeduniverse must be ade$uate!" exp!ained.

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5. Ana!"sis of the samp!ing frame variabi!it" must be thorough!" exp!ainedand documented. his 'ou!d inc!ude the ca!cu!ation of measures ofcentra! tendenc" and indices of dispersion 0mean# median# mode#standard deviation# and coefficient of variation)# the determination of an"obvious do!!ar or characteristic groupings for manua! stratification# and

identification of specia! items for separate or no revie'. hese ana!"sesand the re!ated conc!usions must be fu!!" and c!ear!" presented.

. he samp!e sie and ho' it 'as determined must be inc!uded. Forattribute discover" samp!ing# this 'ou!d inc!ude the E=,>uant ADI4%

Attribute 4amp!e 4ie Determination Procedure output. For variab!esamp!ing# this ma" be a conc!usion on the frame ana!"sis documentationexp!aining the app!ication of the samp!e sie guide!ines based on theframe variabi!it".

6. he random se!ection methodo!og" must be documented. his inc!udes

the random numbers or random procedure app!ied for statistica! samp!esor the judgmenta! procedure and reasoning for nonstatistica! samp!es.E=,>uant output and its app!ication to the frame 0if used) must beinc!uded and exp!ained.

C. he se!ected samp!e items themse!ves shou!d be proper!" documented.his ma" be accomp!ished 'ith the samp!e se!ection documentationand-or the samp!e revie' documentation.

G. An" other documentation produced during the samp!e p!anning andse!ection shou!d be inc!uded as appropriate.

I). 1E)IE2 THE SAMPLE

A. 1e"iew each sample item.

1. Perform the revie' of each samp!e item based on the estab!ished criteriaand audit program as re$uired to achieve the audit and samp!ingobjectives.

. Mse the standard @A+I4 'or&sheet and add an" additiona! co!umnsre$uired to perform and document the revie'.

B. Determine the ca!se of each error and whether it is sstemic,nonsstemic and rec!rring,nonrec!rring.

1. he cause of the error is critica! to understanding the nature of theprob!em and ma&ing appropriate recommendations. he nature of theerror is a!so important for proper computation of comp!iance rates andprojection of do!!ar impact.

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. Each error 'i!! be identified as s"stemic or nons"stemic A2D recurring ornonrecurring for this purpose.

a) 4"stemic errors are those caused b" a deficienc" in the s"stem ofinterna! contro!s. If the s"stem is corrected or interna! contro!s

strengthened# the error shou!d not recur. %!erica! or human error0especia!!" if such errors are repetitive) that occurred because there'ere no interna! contro!s in p!ace to tr" to prevent or catch sucherrors 0i.e.# training# supervision# 'ritten instructions# monitoring#chec&ing# etc.) 'ou!d a!so be s"stemic. 4"stemic errors are a!sorecurring errors# even if on!" one is found# because the" cou!d recurdue to the s"stem deficienc". (n!" s"stemic errors are inc!uded inthe determination of comp!iance.

b) 2ons"stemic errors are those not caused b" an" apparent 'ea&nessin interna! contro!s. "pica!!" these are occasiona! c!erica! or human

errors that occurred despite ade$uate interna! contro!s 0i.e.# training#supervision# 'ritten instructions# monitoring# chec&ing# etc.).2ons"stemic errors ma" a!so be recurring if the" disp!a" a pattern ortrend that the" are !i&e!" to recur. For examp!e# repetitive c!erica!errors ma" be indicative of some sort of 'ea&ness in the interna!contro!s# such as incompetent personne!# inade$uate training# !ac& ofsupervision or monitoring# etc. he designation of s"stemic ornons"stemic is re$uired for the determination of comp!iance. (n!"s"stemic errors are inc!uded in the computation of comp!iance rates.2ons"stemic errors are not used 'hen ca!cu!ating comp!iance rates

c) @ecurring errors are those that cou!d recur in the frame from 'hichthe samp!e 'as ta&en. "pica!!" these are s"stemic errors. he"ma" a!so be nons"stemic errors that disp!a" a pattern or trend thatthe" are !i&e!" to recur 0e.g.# repetitive c!erica! errors are recurringerrors). he designation of recurring or nonrecurring is re$uired forrevenue projection. (n!" recurring errors are projected.2onrecurring errors are not projected. ?o'ever# nonrecurring errorsshou!d be added to the projected revenue !oss 'hen ca!cu!ating tota!revenue !oss.

d) 2onrecurring errors are those that 'ou!d not be expected to recur inthe frame from 'hich the samp!e 'as ta&en. "pica!!" these are

nons"stemic# iso!ated c!erica! or human errors that occurred despiteade$uate interna! contro!s 0i.e.# training# supervision# 'ritteninstructions# monitoring# chec&ing# etc.). he" cou!d a!so be errorsfound outside the samp!ing frame. he designation of recurring ornonrecurring is re$uired for revenue projection. (n!" recurring errorsare projected. 2onrecurring errors are not projected. ?o'ever#nonrecurring errors shou!d be added to the projected revenue !oss'hen ca!cu!ating tota! revenue !oss.

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). E)AL/ATE THE SAMPLE 1ES/LTS

A. (alc!late compliance% if applica#le.

1. %omp!iance# 'hen app!icab!e 0i.e. the determination of comp!iance is an

audit-samp!ing objective)# 'i!! genera!!" be based on the va!ue ofs"stemic errors found in the samp!e. 4ee Appendix I7 of this documentand FA Program Exhibit *F for more guidance on ho' to computecomp!iance rates.

. @emember that it is genera!!" not appropriate to compute comp!iancerates based on the resu!ts of sma!! nonstatistica! samp!es.

B. (alc!late the total re"en!e d!e.

1. Joss of revenue estimates shou!d be based on the most accurateinformation avai!ab!e. Actua! amounts# if &no'n 0e.g. 1//3 revie' 'as

performed)# 'ou!d be the first choice. (ther'ise# statistica! projectionsor other reasonab!e means of estimating revenue due ma" be used.

. 4tatistica! projections.

a) E=,>uant 4A+PJ Ph"sica! Mnit 4amp!e Eva!uation Procedure ma"be used to project revenue due for attribute discover" and variab!eph"sica! unit samp!es. he procedure projects the samp!e revenuedue to the universe and provides re!iabi!it" measures for eva!uatingthat projection. It provides t'o point estimates 0one for the ratiomethod and one for the difference method) a!ong 'ith associated

precision do!!ars and confidence interva!s based on the confidence!eve! specified. he confidence !eve! used 'i!! be 3. he pointestimate 'ith the !o'est precision percentage 0precision do!!ars -point estimate) shou!d be se!ected.

b) E=,>uant DM4A+ Do!!ar Mnit 4amp!e Eva!uation Procedure ma" beused to project revenue due for variab!e ph"sica! unit samp!es. heprocedure projects the samp!e revenue due to the universe andprovides re!iabi!it" measures for eva!uating that projection. Itprovides a point estimate a!ong 'ith associated precision do!!ars andconfidence interva!s based on the confidence !eve! specified. he

confidence !eve! used 'i!! be 3.

c) (ther computer programs# such as +icrosoft Access or 4A4# ma" beused to statistica!!" project and eva!uate statistica! samp!e resu!ts ife!ectronic fi!es are too !arge for E=,>uant 4A+PJ Ph"sica! Mnit4amp!e Eva!uation Procedure or E=,>uant DM4A+ Do!!ar Mnit4amp!e Eva!uation Procedure. Auditors shou!d consu!t 'ith a %A4 ifthe" encounter this situation.

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@esu!ts %omp!iance# 4amp!e @esu!ts Dut" Due# 4amp!e @esu!ts 7a!ue Impact# and 4amp!e @esu!ts (ther Oears-Areas pertain tosamp!e resu!ts eva!uation. hese sections shou!d be comp!eted at thispoint.

. he determination of comp!iance and ho' ca!cu!ated.

*. he tota! revenue due and its method of ca!cu!ation. his 'ou!d inc!udethe E=,>uant input and output if statistica! projections are used.

5. he ca!cu!ation and ana!"sis of the resu!ting precision percentage andan" actions ta&en for unacceptab!e precision must be inc!uded.

. he tota! va!ue impact# ho' ca!cu!ated# comparison 'ith EE thresho!ds#and referra! to EE if app!icab!e.

6. he impact on other "ears-areas and ho' determined.

C. An" other documentation produced during the samp!e resu!ts eva!uationshou!d be inc!uded as appropriate.

)II. 1EP1T THE SAMPLE 1ES/LTS

A. A ta#le of sampling information will #e incl!ded in the a!dit report foreach sample &nonstatistical and statistical'. The ta#les will show thesample n!m#er and re"iew area% frame description% sampling approach%frame si+e,"al!e,d!t% and sample si+e,"al!e,d!t. See Appendi3 ) fore3amples.

B. In addition% the a!dit report sho!ld incl!de an comp!ted compliancerates and total re"en!e loss comp!ted.

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