You are to write a review of the attached article in a Word document. I have attached a copy of the rubric I will use to grade your review. Review it carefully because it contains the detailed instructions for the assignment. Attach your review to this assignment and name your file with your first name, last name, and

qattachments_679f5f5c38f5e1da5113f1e50a554149f782500d \u0011\u001a>\u0003\t\u0006\u00018\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u00017\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001 P8X\u0002@\u0002\u0002"\u0002\u000e\u0002\u0001\u0001\u0002\u00021\b\u0002dCalibri1\b\u0002dCalibri1\b\u0002dCalibri1\b\u0002dCalibri1\b\u0002dCalibri1\b\u0002dCalibri1\b\u0002dCalibri1\b4\u0001\u0002dCalibri1\b\u0001\u0002dCalibri1\b\u0002dC \u0002dCalibri1.\u0001\b\u0001\u0002dTimes New Roman1.\u0001\b\u0002dTimes New Roman1.\u0001\b\u0001\u0002dTimes New Roman"\u0002\u000e\u0002\u0001$"\u0002\u000e\u0002\u0001# \t\u0006\u0001 \u0001 \t\u0006\u0001 \u0004 \t\u0006\u0001 P\u0001 \t\u0006\u0001 P\u0001 \t\u0006\u0001 \u0001 \t\u0006\u0001 \u0001 \t\u0006\u0001 \"\u0011\u0011\u000b\u000b\u0001\u0002 \t\u0006\u0001 `\u001a\u0001 \t\u0006\u0001 \u0004 \t\u0006\u0001 \u0002\u0011\u0019\u0002\u0012\u0002\u0013\u0002 \t\u0006\u0001 \"\u0011\u0011\u001f\u001f- \t\u0006\u0001 \u0001 \t\u0006\u0001 \u0001 \t\u0006\u0001 a>\u0003\u0001 \t\u0006\u0001 \u0001 8"\u0002\u000e\u0002\u0001"\u0002\u000e\u0002\u0001@\u0002 @\u0002 \u0001 8 "\u0002\u000e\u0002\u0001 @\u0002 00_)\u000e\u0005\u0002}\b-}\b 00_)\u000e\u0005\u0002}\b-}\b 00_)\u000e\u0005\u0002}\b-}\b 00_)\u000e\u0005\u0002}\b-}\b 00_)\u000e\u0005\u0002}\b-}\b 00_)\u000e\u0005\u0002}\b-}\b 00_)\u000e\u0005\u0002}\b-}\b 00_)\u000e\u0005\u0002}\b-}\b 00_)\u000e\u0005\u0002}\b-}\b 00_)\u000e\u0005\u0002}\b-}\b 00_)\u000e\u0005\u0002}\b-}\b 00_)\u000e\u0005\u0002}\b-}\b 00_)\u000e\u0005\u0002}\b-}\b 00_)\u000e\u0005\u0002}\b-}\b 00_)\u000e\u0005\u0002}\b-}\b 00_)\u000e\u0005\u0002}\bA}\b 00_)\u000e\u0005\u0002ef[$ -}\bA}\b 00_)\u000e\u0005\u0002ef[$ -}\bA}\b 00_)\u000e\u0005\u0002ef[$ -}\bA}\b 00_)\u000e\u0005\u0002ef[$ -}\bA}\b 00_)\u000e\u0005\u0002ef[$ -}\bA}\b 00_)\u000e\u0005\u0002ef[$ -}\bA}\b 00_)\u000e\u0005\u0002L\u0004[$ -}\bA}\b 00_)\u000e\u0005\u0002L\u0004[$ -}\bA}\b 00_)\u000e\u0005\u0002L\u0004[$ -}\bA}\b 00_)\u000e\u0005\u0002L\u0004[$ -}\bA}\b 00_)\u000e\u0005\u0002L\u0004[$ -}\bA}\b 00_)\u000e\u0005\u0002L\u0004[$ -}\bA}\b 00_)\u000e\u0005\u000223[$ -}\bA}\b 00_)\u000e\u0005\u000223[$ -}\bA}\b 00_)\u000e\u0005\u000223[$ -}\bA}\b 00_)\u000e\u0005\u000223[$ -}\bA}\b 00_)\u000e\u0005\u000223[$ -}\bA}\b! 00_)\u000e\u0005\u000223[$ -}\bA}\b"\u0002\u000e\u0002\u0001 00_)\u000e\u0005\u0002[$ -}\bA}\b# 00_)\u000e\u0005\u0002[$ -}\bA}\b$ 00_)\u000e\u0005\u0002[$ -}\bA}\b% 00_)\u000e\u0005\u0002[$ -}\bA}\b& 00_)\u000e\u0005\u0002[$ -}\bA}\b' 00_)\u000e\u0005\u0002[$ -}\bA}\b( \u0002\u0011\u0019\u0002\u0012\u0002\u0013\u0002\t\u0006\u000100_)\u000e\u0005\u0002\t\u0006\u0001\t\u0006\u0001[$ -}\b'}\b)\u000e\u0005\u0002 }\b\t\u0006\u000100_)\u000e\u0005\u0002\t\u0006\u0001[$ -\u0002\u0002\u0002\t\u0006\u0001##0.\u0002\u0002\u0002\t\u0006\u0001 \u0002\u0002\u0002\t\u0006\u0001}\b'}\b* 00_)\u000e\u0005\u0002\t\u0006\u0001[$ -???\t\u0006\u0001##0.???\t\u0006\u0001 ???\t\u0006\u0001}\b-}\b+ 00_)\u000e\u0005\u0002}\b-}\b \u0002\u0002\u0002\t\u0006\u0001 ???\t\u0006\u0001 Page 1 qattachments_679f5f5c38f5e1da5113f1e50a554149f782500d 00_)\u000e\u0005\u0002}\b-}\b00_)\u000e\u0005\u0002}\b-}\b. 00_)\u000e\u0005\u0002}\b-}\b/ \u0002\u0002\u0002\t\u0006\u000100_)\u000e\u0005\u0002}\bA}\b0 a\t\u0006\u000100_)\u000e\u0005\u0002\t\u0006\u0001[$ -}\bA}\b1 00_)\u000e\u0005\u0002[$ -}\bA}\b2 00_)\u000e\u0005\u0002\t\u0006\u0001?[$ -}\bA}\b3 00_)\u000e\u0005\u000223[$ -}\b-}\b4 00_)\u000e\u0005\u0002}\b'}\b5 ??v\t\u0006\u000100_)\u000e\u0005\u0002\t\u0006\u0001\t\u0006\u0001[$ -\u0002\u0002\u0002\t\u0006\u0001##0.\u0002\u0002\u0002\t\u0006\u0001 \u0002\u0002\u0002\t\u0006\u0001}\bA}\b6 }\b\t\u0006\u000100_)\u000e\u0005\u0002\t\u0006\u0001\u0001\t\u0006\u0001[$ -}\bA}\b7 \u0002\u0011\u0019\u0002\u0012\u0002\u0013\u0002e\t\u0006\u000100_)\u000e\u0005\u0002\t\u0006\u0001\u0002\u0011\u0019\u0002\u0012\u0002\u0013\u0002\t\u0006\u0001[$ -}\b'}\b8 00_)\u000e\u0005\u0002\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001[$ -\t\u0006\u0001##0.\t\u0006\u0001 \t\u0006\u0001 \t\u0006\u0001}\b'}\b9 ???\t\u0006\u000100_)\u000e\u0005\u0002\t\u0006\u0001[$ -???\t\u0006\u0001##0.???\t\u0006\u0001 ???\t\u0006\u0001}\b-}\b: 00_)\u000e\u0005\u0002}\b-}\b; 00_)\u000e\u0005\u0002}\bU}\b\u0003 00_)\u000e\u0005\u0002}\b(}\b? 00_)\u000e\u0005\u0002}\b(}\b@\u0002 00_)\u000e\u0005\u0002}\b(}\bA 00_)\u000e\u0005\u0002}\b(}\bB 00_)\u000e\u0005\u0002}\b(}\bC 00_)\u000e\u0005\u0002}\b(}\bD 00_)\u000e\u0005\u0002}\b(}\bE 00_)\u000e\u0005\u0002}\b(}\bF 00_)\u000e\u0005\u0002}\b(}\bG 00_)\u000e\u0005\u0002}\b(}\bH 00_)\u000e\u0005\u0002}\b(}\bI 00_)\u000e\u0005\u0002}\b(}\bJ 00_)\u000e\u0005\u0002}\b(}\bK 00_)\u000e\u0005\u0002}\b(}\bL\u0004 00_)\u000e\u0005\u0002}\b(}\bM 00_)\u000e\u0005\u0002}\b(}\bN 00_)\u000e\u0005\u0002\" 20% - Accent1'\bM'\b\t\u0006\u0001 20% - Accent1ef\b\t\u0006\u0001\t\u0006\u0001%\" 20% - Accent2'\bM'\b"\u0002\u000e\u0002\u0001\t\u0006\u0001 20% - Accent2ef\b\t\u0006\u0001\t\u0006\u0001%\" 20% - Accent3'\bM'\b&\t\u0006\u0001 20% - Accent3ef\t\u0006\u0001\t\u0006\u0001%\" 20% - Accent4'\bM'\b*\t\u0006\u0001 20% - Accent4ef\t\u0006\u0001\t\u0006\u0001%\" 20% - Accent5'\bM'\b.\t\u0006\u0001 20% - Accent5ef\u0002\t\u0006\u0001\t\u0006\u0001%\" 20% - Accent6'\bM'\b2\t\u0006\u0001 20% - Accent6ef\t\u0006\u0001\t\u0006\u0001%\" 40% - Accent1'\bM'\b\t\u0006\u0001 40% - Accent1L\u0004\t\u0006\u0001\t\u0006\u0001%\" 40% - Accent2'\bM'\b#\t\u0006\u0001 \u0002\u0002\u0002\t\u0006\u0001 ???\t\u0006\u0001 Page 2 qattachments_679f5f5c38f5e1da5113f1e50a554149f782500d 40% - Accent2L\u0004\u0001\u0002\t\u0006\u0001\t\u0006\u0001%\" 40% - Accent3'\bM'\b'\t\u0006\u0001 40% - Accent3L\u0004\u0001\u0002\t\u0006\u0001\t\u0006\u0001%\" 40% - Accent4'\bM'\b+\t\u0006\u0001 40% - Accent4L\u0004\u0001\u0002\t\u0006\u0001\t\u0006\u0001%\" 40% - Accent5'\bM'\b/\t\u0006\u0001 40% - Accent5L\u0004\u0001\u0002\t\u0006\u0001\t\u0006\u0001%\" 40% - Accent6'\bM'\b3\t\u0006\u0001 40% - Accent6L\u0004\u0004\t\u0006\u0001\t\u0006\u0001%\" 60% - Accent1'\bM'\b \t\u0006\u0001 60% - Accent123\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001%\" 60% - Accent2'\bM'\b$\t\u0006\u0001 60% - Accent223\u0002-\"\u0011\u0011\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001%\" 60% - Accent3'\bM'\b(\t\u0006\u0001 60% - Accent323\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001%\" 60% - Accent4'\bM'\b 60% - Accent423\u001a \t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001%\" 60% - Accent5'\bM'\b0\t\u0006\u0001 60% - Accent523'\b\u0007\b\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001%\"! 60% - Accent6'\bM'\b4\t\u0006\u0001 60% - Accent623\u001f\u0002\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001%\""\u0002\u000e\u0002\u0001Accent1'\bA'\b\t\u0006\u0001Accent1O\u0002\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001%\"#Accent2'\bA'\b!\t\u0006\u0001Accent2\u0001PM\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001%\"$Accent3'\bA'\b%\t\u0006\u0001Accent3 Check Cell'\b\u0002'\b\t\u0006\u0001 Check Cell\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001%\t\u0006\u0001???\t\u0006\u0001\t\u0006\u0001???\t\u0006\u0001\t\u0006\u0001???\t\u0006\u0001\t\u0006\u0001???\t\u0006\u0001\"+\u0001\t\u0006\u0001'\b '\b\t\u0006\u0001Comma\" 5Inp\fut'\bu'\b\t\u0006\u0001Inp\fut\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001??v\t\u0006\u0001%\t\u0006\u0001\u0002\u0002\u0002\t\u0006\u0001\t\u0006\u0001\u0002\u0002\u0002\t\u0006\u0001\t\u0006\u0001\u0002\u0002\u0002\t\u0006\u0001\t\u0006\u0001\u0002\u0002\u0002\t\u0006\u0001\"6L\u0004inked Cell'\bK'\b\t\u0006\u0001L\u0004inked Cell\t\u0006\u0001}\b\t\u0006\u0001%\t\u0006\u0001\t\u0006\u0001\u0001\t\u0006\u0001\"7Neutral'\bA'\b\t\u0006\u0001Neutral\t\u0006\u0001\t\u0006\u0001\u0002\u0011\u0019\u0002\u0012\u0002\u0013\u0002\t\u0006\u0001\t\u0006\u0001\u0002\u0011\u0019\u0002\u0012\u0002\u0013\u0002e\t\u0006\u0001%\" \t\u0006\u0001Note\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\"9Outp\fut'\bw'\b\t\u0006\u0001Outp\fut\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001???\t\u0006\u0001%\t\u0006\u0001???\t\u0006\u0001\t\u0006\u0001???\t\u0006\u0001\t\u0006\u0001???\t\u0006\u0001\t\u0006\u0001???\t\u0006\u0001\":\u0001\t\u0006\u0001'\b$'\b\t\u0006\u0001Percent\" ;Title'\b1'\b\t\u0006\u0001TitleI}\b\t\u0006\u0001%\" \u0003 ? ? C D~ A$@\u0002 @\u0002 @\u0002~ A$@\u0002F~ A4@\u0002 @\u0002 @\u0002 ~ A4@\u0002F~ AI@\u0002 @\u0002 @\u0002~ AI@\u0002F~ Page 3 qattachments_679f5f5c38f5e1da5113f1e50a554149f782500d A4@\u0002 @\u0002 @\u0002~ A4@\u0002F#GY@\u0002 %\u0001\u0001n BH#GY@\u0002 %\u0001\u0001nF\u0002\u0004\u0001\u0001 FBBBB>\u0003\u0006@\u0002\b\u0010\b\u0010\u0012\u0002\u0004\u0001\u0001\u0004gg\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001D \t\u0006\u0001 ...\u000eOh'+'0HP\\t\u0004\u0001\u0001 \u0013\u0004JimFrancis AlieMicrosoft Excel@\u0002\u0002.u@\u0002\u0001aQ\u0007\u0007@\u0002&t\u0002\u0001\u0001\u0003\t\u0006\u0001 \u0007\u0002\u0011\u0019\u0002\u0012\u0002\u0013\u0002.\"\u000b+ \u0002\u0002ToshibaSheet1Sheet1!Print_AreaWorksheets Named Ranges !"\u0002\u000e\u0002\u0001#$%&\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001()\u000e\u0005\u0002*+ Page 4 qattachments_679f5f5c38f5e1da5113f1e50a554149f782500d \t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001\t\u0006\u0001 8\u0001\u0002dCalibri18\u0001\u0002dCalibri1\b8\u0001\u0002dCalibri1\b>\u0003\u0002dCalibri1\b4\u0002dCalibri1\b x\" for testing and either ignore the remainder of the items or test \"a few\" of the items x items make up a very large percentage of the population, rendering the x (the 100% layer) and all other items. The 100% layer will include all items greater than or equal to the tolerable misstatement (performance materiality) for the test, but will likely be lower if there are little or no individual items greater than or equal to performance materiality. In other words, x can be no greater than performance materiality for the test and will likely be lower; this is more or less the same as the software would do for an MUS test. Step 2. Determine the sampling parameters as in an \"attributes\" test, with regard to the number of items in the population, confidence level (the flip side of risk), and tol- erable error rate. The expected error rate should be set at one-half of the tolerable error rate. The attribute in this test is whether the inventory item is properly valued. The resulting sample size will be three to four times larger than a sample for discovery sampling. The benefit of this larger sample size is that if the population is error-prone, the sample will include enough errors to provide the basis for the calculation of a confidence interval that can serve as a reasonable basis for an audit decision. Step 3. Input the sampling parameters into an \"attributes\" sample size calculator. The result will be a sample size of more items than MUS, but fewer than CVS (which considers the variability of the entire population). Step 4. Perform the test on the 100% layer and the sample items (selected randomly). Step 5. Input the results, including the 100% layer, into a CVS evaluation program. What happens next depends on the sample results. The CVS evaluation program will produce several numbers that are used in the following evaluation rules: n Precision n Lower confidence limit/lower error limit (LEL) EXHIBIT 1 Results for Practical Approach 1 Sample Number 8 Number of Misstatements Rate of Misstatement Precision Lower Confidence Estimated Misstatement Upper Confidence Proposed Correction 1 13 7.4% 286,692 (258,141) 28,552 315,244 2 17 9.7% 478,327 (257,115) 221,212 699,540 3 15 8.6% 315,483 (550,994) (235,510) 79,973 4 15 8.6% 237,220 (399,814) (162,594) 74,626 5 8 4.6% 252,794 (324,703) (71,908) 180,886 6 17 9.7% 800,424 (368,253) 432,171 1,232,594 Reject 7 21 12.0% 368,268 (493,441) (125,174) 243,094 8 10 5.7% 334,009 (329,553) 4,457 338,466 9 17 9.7% 330,763 (772,299) (441,536) (110,774) 10 14 8.0% 346,298 (697,857) (351,558) (5,260) APRIL 2014 / THE CPA JOURNAL Projected error Upper confidence limit/upper error limit (UEL). Rule 1. If precision is greater than performance materiality (PM), the test fails that is, because the results are not sufficiently precise, there is no basis upon which to propose any correction. This leaves one with three alternatives: n Increase the sample size and test enough items in a single additional step to achieve a precision that is less than PM; this might be a lot of work. n Ask the client to rework the population to reduce the error rate and then retest the population from scratch. Neither of these two alternatives will endear an auditor to a client, but it is not an auditor's fault that the population has so many errors in it. n Finally, see if lowering the confidence level produces an acceptable precision. It is with some trepidation that I even mention this third alternative. While theoretically acceptable, this choice is too prone, in my opinion, to rationalization. The confidence level at which the test was originally designed and performed should not be second-guessed because of n n Many auditors may not like the idea of proposing correction where none is needed, but in the real world you will never know whether the population is misstated. the results. Accordingly, I urge that one not be seduced by this alternative. If the work is ever questioned, it will be difficult to defend the assertion that confidence was lowered to accommodate the results of the test. Rule 2. If precision is less than PM, the test may be relied upon, but further analysis is required. Rule 2a. If the magnitude of either LEL or UEL is greater than PM, a correction of misstatement must be posted to the schedule of unadjusted differences for further evaluation. See Rule 3. Rule 2b. If the magnitudes of LEL and UEL are both less than PM, the population can be accepted as presented. Rule 3. When Rule 2a requires a proposed adjustment, the amount of the adjustment is the greater of the difference between PM and either the LEL or UEL, and it may be a positive or negative value, depending upon the sign of the LEL or UEL. For example, consider a series of results based on the following: n Population, 4,890 items; value, $14,512,000; performance materiality, $800,000 n 230 items are misstated, some over, some under; a 4.7% rate of misstatement; n Net misstatement is approximately zero (this is a fact one will never know in the real world, but this is a test case) EXHIBIT 2 Results for Practical Approach 2 Sample Number Number of Misstatements Rate of Misstatement Precision Lower Confidence Estimated Misstatement 11 10 8.6% 853,594 (684,355) 169,240 1,022,834 Reject 12 8 6.9% 464,457 (842,441) (377,984) 86,473 (42,441) 13 10 8.6% 561,791 (176,698) 385,093 946,884 146,884 14 16 13.8% 740,005 (866,017) (126,012) 613,993 (66,017) 15 7 6.0% 461,602 (769,426) (307,825) 153,777 16 11 9.5% 313,667 (288,498) 25,170 338,837 17 11 9.5% 293,546 (300,782) (7,236) 286,309 18 13 11.2% 658,178 (1,071,067) (412,889) 245,288 (271,067) 19 5 4.3% 442,952 (282,611) 160,341 603,293 20 12 10.3% 836,411 2,566 838,977 1,675,388 Reject APRIL 2014 / THE CPA JOURNAL Upper Confidence Proposed Correction 9 Using an attributes sampling program, a sample of 175 is calculated using 90% confidence, 5% tolerable error, and 2.5% expected error n For illustrative purposes, 10 different random numbers were used to pull 10 different samples. (Do not attempt this in the fieldclients won't pay for it.) How do we interpret the results shown in Exhibit 1? Sample #6 will be rejected because precision (800,424) is greater than PM n include all items tolerable misstatement (PM) for the test, but will likely be lower, perhaps much lower, if there are no individual items, or few individual items, performance materiality. In other words, x can be no greater than PM for the test and will likely be lower. Step 2. Part of the input to the CVS software requires entering values for PM and risk of incorrect acceptance (the flip side of confidence, which in my experience is usually 80%, 90%, or 95%; i.e., risk of As much as auditors might like to ignore the risk of understatement, the auditor's report speaks of material misstatement. That is a bidirectional concept. (800,000). This follows Rule 1 above. This will happen from time to time, as there is always some chance of the sample producing out-of-bounds results, even for this test population where the net misstatement is zero. As noted above, auditors have a choice: increase the sample size until enough items are tested to achieve precision that is less than PM, or ask the client to rework the population to reduce the error rate and then retest the population from scratch. Because there are 17 errors in this sample, a 9.7% error rate, one should not encounter any resistance to this request. The other nine results are quite interesting in that there is no proposed correction for any of themthis is a good thing, given that the population is, in fact, not at all misstated. Practical Approach 2 Step 1. Input the population into CVS software that will divide the population into two strata, items > x (the 100% layer) and all other items. The 100% layer will 10 20%, 10%, or 5%). How one approaches which variables to use has a significant impact on the calculated sample size. For a more thorough discussion, see \"Relating Statistical Sampling to Audit Objectives\" by Robert K. Elliott and John R. Rogers, Journal of Accountancy, June 1972. Step 3. Allow the CVS software to calculate the sample size. There will be two components, the 100% layer and the actual sample of the remaining population. The resulting number of items to be tested (both strata) will be more than an MUS test but will likely be fewer than Practical Approach 1. This comes at a price, as shown below. Step 4. Select the 100% layer items. Select the sample items randomly. Perform the test. Step 5. Input the results, including the 100% layer, into a CVS evaluation program. What happens next depends on the sample results. Example 2. The results in Exhibit 2 are for the same 10 tests as in Practical Approach 1, but with only 116 items tested. Testing the smaller number of items, two samples will be rejected (#11 and #20). Four of the tests (#12, #13, #14, and #18) will result in proposed corrections where no correction is actually requiredbut if the proposed correction is recorded, the adjusted amount of the population is, in all cases, within PM of the actual amount of the population. Many auditors may not like the idea of proposing correction where none is needed, but in the real world you will never know whether the population is misstated, or by exactly how much, and as long as the planning about PM is sound, it is appropriate to propose such adjustments. Which Is the Better Approach? There is no correct answer to this question. The science is inescapablethe more items tested, the more precise the results become, allowing for better decisions. This argues for Practical Approach 1. The problem is that the extra precision comes at the expense of additional time, as compared to Practical Approach 2. Aside from the difference in rejected samples, both approaches yield acceptable audit results. My recommendation to auditors is try to find the budget for Practical Approach 1. There will be fewer rejected results, but, even more importantly, there will be fewer projected errors to request that the client correct. As much as auditors might like to ignore the risk of understatementhowever we rationalize it, such as the financial statements are \"conservative\"the auditor's report speaks of material misstatement. That is a bidirectional concept. Of course, there is also the point to be made that understatement is \"conservative\" only for assets. Are the non-sampling and substantive analytical procedures described above that provide some comfort about understatement enough? Ultimately that is a matter of an auditor's judgment. The practical approaches outlined above should illustrate why CVS is not too much work to use to test inventory valuation. q Howard Sibelman, MBT, CPA, is a director of subscriber services at Crowe Horwath International, New York, N.Y. APRIL 2014 / THE CPA JOURNAL Copyright of CPA Journal is the property of New York State Society of CPAs and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use