Patent Publication Number: US-8527375-B1

Title: Methods systems and computer program products for identifying suspect data of an electronic tax return

Description:
CROSS-REFERENCE TO RELATED APPLICATION 
     This application is a continuation of U.S. Application Ser. No. 12/828,012, filed Jun. 30, 2010, issued as U.S. Pat. No. 8,346,635 on Jan. 1, 2013, priority of which is claimed under 35 U.S.C. §120, the contents of which are incorporated herein by reference as though set forth in full. 
    
    
     BACKGROUND 
     The invention relates to preparation of electronic financial documents such as electronic tax returns. Tax preparation programs have become very popular and allow a user, such as an individual, taxpayer, accountant, or tax professional, to prepare and electronically file a tax return using a computer. Known tax preparation programs present a list of tax-related questions or data entry fields to the user as a series of interview screens or fillable forms, in response to which the user enters the appropriate data or answers. For example, in an individual tax return, certain interview screens or questions may involve personal and family matters such as name, address, social security number, marital status, number of dependents, etc. Other interview screens and questions may involve financial matters such as wages, retirement plan contributions, and social security, state and federal taxes paid or withheld as provided in Form-W2, charitable contributions, mortgage interest paid, property tax paid, etc. The electronic tax return may also be a business or corporate return with business related income and expense items. After entry of the data, the tax preparation application prepares an electronic tax return, which is formatted as necessary and electronically filed with a tax authority such as the Internal Revenue Service (IRS), state tax authority, or other tax collecting entity. 
     While tax preparation programs have been used effectively in the past to prepare and file tax returns, their effectiveness relies in part upon data input into the electronic tax return. The data, whether entered manually or imported from another program or file, may be as accurate as its source and may be inaccurate for various reasons. For example, it is not uncommon for manually entered data to be wrong for various reasons including human error, defective keyboards and issues with programs and files. Human error may involve typographical errors such as pressing the wrong number, pressing a number one too many or one to few times, pressing two keys inadvertently, inserting a comma in the wrong place, inserting a decimal point in the wrong place, or simply entering correctly typed data in the wrong field. Similar errors may be caused by computer components such as a defective keyboard that has an inoperable or sticky key and programs or files that do not operate as intended or that have corrupted data. Similar issues may arise with data that is imported from another program or file. There may also be cases in which there that tax preparation program generates errors while importing data from another program or file. 
     These errors, however generated, can have significant impacts on the result generated by the tax preparation program. For example, rather than an income of $75,000, a typographical error may result in an income of $750,000, which would involve substantially more tax owed. Similar results can occur with other types of errors. While this example involves a 10× increase in income and may be identified by the preparer due to its magnitude, others errors may be more subtle and more difficult to identify while still having a significant impact on the end result, particularly if there are multiple errors. Thus, it can be difficult to pinpoint the sources of the error within an electronic tax return. 
     For example, an individual may be entitled to a tax deduction for a retirement plan contribution in the amount of $5000 but the individual (or another preparer such as the individual&#39;s accountant or tax professional) may inadvertently type in $500 instead or the retirement plan contribution data may be wrong for other reasons. While this error is also an 10× error, the fact that there is an error may not be readily apparent to the preparer since the reduction in taxes due to the lower amount entered may not signal to the preparer that there was an error. Thus, this error may go unnoticed, thereby resulting in a loss of a $4,500 tax deduction and more taxes paid. This not only results in loss of a potential deduction to reduce taxes, but it may also increase the risk and consequences of an audit if data of the tax return does not match data sent to the tax authority, e.g., if data of Form W-2 that includes retirement plan contribution data does not match the retirement plan contribution data of the tax return, thereby triggering further review or an audit by the tax authority. 
     Known tax preparation programs such as TurboTax® (available from Intuit Inc.) and other tax preparation programs conduct a final review (examples of final review screens illustrated in  FIGS. 13A-C ) to check for errors in a completed tax return and to assess the risk of an audit (example of audit screen shown in  FIG. 13D ) by a tax authority. The final review and audit check, however, are based upon tax return data entered. For example a tax preparation program such as TurboTax® may perform a final review to check for formalities such as empty fields that should be populated with data or boxes that need to be checked to ensure that no available deductions or credits were missed (e.g., if the preparer did not review certain interview screens or fill out certain sections of the tax return). Thus, while the calculations performed on the tax data may be accurate, the tax return is only as accurate as the data entered, and the tax preparation program does not analyze the data itself which is instead accepted as accurate and utilized in calculations. Consequently, the amount of tax due or the refund calculated by the tax preparation program will be based upon incorrect data if incorrect data is entered. Further, a tax preparation program such as TurboTax® may perform an audit risk assessment and generate a visual indication of audit risk as low, medium or high in the form of a bar graph. This analysis and the resulting visual representation, however, are also based upon data entered. 
     SUMMARY 
     Embodiments of the invention relate to analyzing quantifiable electronic tax data during preparation of the electronic tax return to specifically identify potential data entry errors or suspect data, e.g., by form and line number within the electronic tax return, and alerting a preparer of the tax return of the potential error or suspect data. The preparer may then review the specifically identified data and correct or verify the suspect data and continue preparation of the tax return. 
     With embodiments, a specific potential error or suspect data of the electronic tax return is identified by first quantifiable data being entered into the electronic tax return, selecting statistics, and comparing the selected statistics and second quantifiable data entered into the electronic tax return to determine whether the second quantifiable data is suspect or questionable data, e.g., as a result of the preparer making a typographical error, correctly typing the data but entering the data in the wrong field, or entering data of a different magnitude due to a misplaced comma, period, etc.) If the second data does not satisfy certain criteria with respect to the selected statistical data, then the tax preparation program alerts the preparer that the second data is a potential error or suspect data. The alert may be a visual and/or audio message, e.g., in the form of a message or window displayed on the screen or the field with the potential error being highlighted. Additional data about the potential error may also be provided such that the alert explains that the second data entered is a certain amount or percentage above or below the amount of second data that most other taxpayers, who also have similar first tax data, entered in their tax returns that were already filed. 
     Thus, embodiments provide a cross-check mechanism that utilizes both first data from the electronic tax return and external or third party data to determine whether other data within the electronic tax return is suspect or questionable. 
     One embodiment is directed to a method for identifying suspect quantifiable or numerical data of an electronic tax return that is involved in a tax calculation. The method comprises receiving first quantifiable tax data and second quantifiable tax data at a computing apparatus utilized to prepare an electronic tax return with a tax preparation program. A first field of the electronic tax return is populated with the first quantifiable tax data, a second field of the electronic tax return is populated with the second quantifiable tax data. The method further comprises the tax preparation program selecting statistical data based at least in part upon the first quantifiable tax data and comparing the second quantifiable tax data and the selected statistical data to determine whether the second quantifiable tax data satisfies pre-determined criteria with respect to the selected statistical data. 
     A further embodiment is directed to a method for identifying suspect data of an electronic tax return and comprises selecting, with the tax preparation program, tax return statistics based at least in part upon first quantifiable tax data entered into the electronic tax return prepared utilizing a tax preparation program and comparing, with the tax preparation program, the selected tax return statistics and second quantifiable tax data entered into the electronic tax return. The tax preparation program determines whether the second quantifiable tax data satisfies pre-determined criteria with respect to the selected tax return statistics. The method further comprises alerting the preparer of the electronic tax return if the second quantifiable tax data does not satisfy pre-determined criteria with respect to the selected tax return statistics. 
     Another embodiment is directed to a method for identifying suspect data within an electronic financial document of a corporate or business entity. The financial document may be, for example, an electronic tax return, financial statement, quarterly or annual report, balance sheet, income statement, cash flow statement, etc. The method comprises receiving first financial data and second financial data at a computing apparatus utilized to prepare the electronic financial document. The first and second financial data are involved in a calculation as opposed to being informative data that is not involved in a calculation. A first field of the electronic financial document is populated with the first financial data, and a second field of the electronic financial document is populated with the second financial data. The method further comprises selecting statistical data based at least in part upon the first financial data. The statistical data is selected by a program utilized to prepare the electronic financial document. The method further comprises comparing the second financial data and the selected statistical data to determine whether the second financial data satisfies pre-determined criteria relative to the selected statistical data and whether the second financial data is suspect data. 
     Yet another embodiment is directed to a computer program product comprising a non-transitory, computer readable storage medium having instructions which, when executed by a computer system, cause a processor to execute a process for identifying suspect data of an electronic tax return. In one embodiment, the instructions are executed to receive first quantifiable tax data and second quantifiable tax data at a computing apparatus utilized to prepare an electronic tax return such that a first field of the electronic tax return being populated with the first tax data, and a second field of the electronic tax return being populated with the second tax data (e.g., by manual entry of data by a preparer of the electronic tax return or by importation of the data). The first and second quantifiable tax data are the subject of calculations. The process further comprises selecting statistical data based at least in part upon the first tax data and comparing the second tax data and the selected statistical data to determine whether the second tax data satisfies pre-determined criteria relative to the selected statistical data and whether the second tax data is suspect data. 
     A further embodiment is directed to a computer program product comprising a non-transitory, computer readable storage medium having instructions which, when executed by a computer system, cause a processor to execute a process for identifying suspect data of an electronic tax return. In one embodiment, the instructions are executed to select tax return statistics based at least in part upon first quantifiable tax data entered into the electronic tax return prepared utilizing a tax preparation program, compare the selected tax return statistics and second quantifiable tax data entered into the electronic tax return to determine whether the second quantifiable tax data satisfies pre-determined criteria, and if not, alerting the preparer of the electronic tax return. 
     Yet another embodiment is directed to a computer program product comprising a non-transitory, computer readable storage medium having instructions which, when executed by a computer system, cause a processor to execute a process for identifying suspect data of an electronic financial document of a corporate or business entity. In one embodiment, the instructions are executed to receive first quantifiable financial data and second quantifiable financial data at a computing apparatus utilized to prepare the electronic financial document. A first field of the electronic financial document is populated with the first financial data, and a second field of the electronic financial document is populated with the second financial data. The first and second financial data are involved in a calculation. The process further comprises selecting statistical data based at least in part upon the first financial data and comparing the second financial data and the selected statistical data to determine whether the second financial data satisfies pre-determined criteria with respect to the selected statistical data. 
     Another embodiment is directed to a system for identifying suspect data of an electronic tax return. The system comprises a tax preparation program executing on a computer and a database, which may be part of or accessible by the tax preparation program or part of or accessible by another program utilized by or with the tax preparation program. The tax preparation program can be utilized by a preparer such as an individual, accountant or tax professional to prepare an electronic tax return. The system further comprises a tax data analysis program, which may be a module or the tax preparation program or a stand-alone program that operate with the tax preparation program. The database comprises statistical data, and the tax preparation program is programmed or operable to access the database to retrieve or access statistical data. During preparation of the electronic tax return, the tax preparation program is programmed or operable to receive first and second quantifiable tax data at a computing apparatus utilized to prepare the electronic tax return. A first field of the electronic tax return is populated with the first quantifiable tax data, and the second field of the electronic tax return is populated with the second quantifiable tax data. Both the first and second data are involved in a tax calculation. The tax preparation program is programmed or configured to select statistical data based at least in part upon the first tax data, compare the second tax data and the selected statistical data to determine whether the second tax data satisfies pre-determined criteria relative to the selected statistical data to determine whether the second tax data is suspect or questionable. 
     Another embodiment is directed to a system for identifying suspect data of an electronic tax return. The system comprises a tax preparation program executing on a computer and a database. The tax preparation program can be utilized by a preparer such as an individual, accountant or tax professional to prepare an electronic tax return. The system further comprises a tax data analysis program, which may be a module or the tax preparation program or a stand-alone program that operate with the tax preparation program. The database comprises statistical data, and the tax preparation program is programmed or operable to access the database to retrieve or access statistical data. During preparation of the electronic tax return, the tax preparation program is programmed or operable to select tax return statistics based at least in part upon first quantifiable tax data entered into the electronic tax return prepared utilizing a tax preparation program, compare the selected tax return statistics and second quantifiable tax data entered into the electronic tax return to determine whether the second tax data satisfies pre-determined criteria, and if not, alert the preparer of the electronic tax return. 
     Yet another embodiment is directed to a system for identifying suspect data of an electronic financial document of a corporate or business entity. The system comprises a program executing on a computer and that is utilized to prepare the electronic financial document and a database. The system further comprises a tax data analysis program, which may be a module or the tax preparation program or a stand-alone program that operate with the tax preparation program. The database comprises statistical data, and the preparation program is programmed or operable to access the database to retrieve or access statistical data. During preparation of the electronic financial document, the preparation program is programmed or operable to receive first quantifiable financial data and second financial data at a computing apparatus utilized to prepare the electronic financial document. A first field of the electronic financial document is populated with the first quantifiable financial data, and a second field of the electronic financial document is populated with the second quantifiable financial data, both of which are involved in a tax calculation. The preparation program is further configured or operable to select statistical data based at least in part upon the first financial data and compare the second financial data and the selected statistical data to determine whether the second financial data satisfies pre-determined criteria relative to the selected statistical data and whether the second financial data is suspect. 
     In a single or multiple embodiments, statistical data selected by the tax preparation program for comparison with data of an electronic tax return being prepared is data from one or more of previously filed tax returns (e.g., data compiled by a tax authority with which tax returns are filed) and data stored at a remote database and utilized by the tax preparation program, e.g., from a third party who collects or analyzes tax return data based upon various criteria such as income, etc. Thus, with embodiments, upon entering first tax data, statistical data related to different, second tax data is selected from the database, which may include various types of statistical data. In a single or multiple embodiments, the particular statistical data selected depends not only on the type but also the value of the first tax data entered into the electronic tax return. 
     According to one embodiment, a preparer enters first tax data or first tax data is imported into an electronic tax return. The tax preparation program categorizes or determines a category or type of the first tax data. For this purpose, the tax preparation program may use a table that associates form and line numbers with types or categories of data. The preparer enters second tax data or second tax data is imported into the electronic tax return that is also categorized. Statistical data of the second category is selected based at least in part upon the first tax data of the first category, and the second tax data and the selected statistical data of the second category are compared by the tax preparation program. 
     For example, first quantifiable tax data may relate to income (such as adjusted gross income, investment income, rental income, or retirement income) and the second tax data may related to a deduction or tax credit (such as deductions for real estate, a dependent, a charitable donation, an education expense, a medical expense, a retirement plan contribution, etc.). Thus, income data can be used to select statistics for another type of data such as a deduction, tax credit, etc. such that the deduction, tax credit, etc. data is then compared to the selected statistics. Embodiments may also involve deduction, tax credit or other data that is utilized to select statistics, and then income data may be compared with the selected statistics. Embodiments may also involve the same category of data, e.g., entering first income data, selecting statistics for second income data, and then comparing second income data with the selected statistics. Thus, embodiments may use various types of data to select statistics, and the selected statistics may be compared with various types of data. 
     In a single or multiple embodiments, the statistical data comprises a range of data that is to be expected given the entered first data. This may be expressed as a certain percentage of filers with first tax data X have second tax data Y between minimum and maximum statistical values. This may also be expressed in the form of standard deviation. For example, the selected statistical values may be three standard deviations from an average or mean value of second tax data to indicate second tax data values of ˜99% of other taxpayers who also have first tax data X or have first tax data X within a certain range. Thus, statistical data such as standard deviation from a mean or average, is selected based upon first data, and then compared to second data to determine whether the second data falls outside of the expected data as determined by the statistical data and is thus suspect and should be brought to the attention of the preparer. 
     The preparer may be alerted by a visual and/or audio message. In one embodiment, the alert is in the form of highlighting the field in which suspect tax data was entered. In another embodiment, the alert is in the form of a pop up message or window. The message may identify the specific location of the suspect data by form and line number and/or request the preparer to verify the suspect data. The preparer may be alerted immediately or shortly after the suspect data is entered and comparisons are completed during preparation of the tax return and before the tax return is completed such that the error can be completed while the preparer is on the same interview screen or section of the electronic tax return. Alerts may also be issued during a final review of a completed tax return. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a flow chart of one embodiment of a method for analyzing electronic tax return data to identify suspect or questionable data; 
         FIG. 2  is a block diagram of a system constructed according to one embodiment for analyzing electronic tax return data to identify suspect or questionable data; 
         FIG. 3  is a flow chart of another embodiment of a method for analyzing electronic tax return data to identify suspect or questionable data; 
         FIGS. 4A-C  illustrate examples of interview screens generated by the tax preparation program and that include fields for entry of tax return data, wherein  FIGS. 4A-B  illustrate interview screens for entry of alpha, numeric or alpha-numeric data that is not utilized in a tax calculation, and  FIG. 4C  illustrates an interview screen for entry of numeric, quantifiable data to be utilized in a tax calculation; 
         FIG. 5  is a chart illustrating how different types of data or data fields can be related to or associated with each other according to embodiments; 
         FIGS. 6A-B  illustrate tables that relates ranges of one type of data to ranges of other types of data, wherein  FIG. 6B  illustrates one embodiment in which data of ranges of adjusted gross income are related to respective ranges of different types of income tax data such as charitable contributions, mortgage interest and retirement contributions; 
         FIGS. 6A-D  illustrate tables with statistics of different types of tax data and how statistics can be selected and compared with tax data to identify suspect or questionable data; 
         FIG. 7  generally illustrates a curve illustrating how statistical data utilized in embodiment scan be represented as a certain number of standard deviations from a mean; 
         FIGS. 8A-D  illustrate how income data can be related or associated with deduction data, and how statistics related to deduction data can be selected based at least in part upon the income data such that a comparison of deduction data and selected statistics can be performed; 
         FIGS. 9A-C  illustrate comparisons performed by a tax data analysis program in which  FIG. 9A  shows deduction data in the form of charitable contribution data satisfying pre-determined criteria with respect to statistical data selected based at least in part upon income data as a result of falling between lower and upper ranges of the selected statistical data, and  FIG. 9B  shows charitable contribution data that is suspect due to not satisfying pre-determined criteria with respect to statistical data, and  FIG. 9C  illustrates a message displayed to the preparer that specifically identifies the suspect data through a pop up message window; 
         FIGS. 10A-B  illustrate comparisons performed by a tax data analysis program in which  FIG. 10A  shows deduction data in the form of mortgage interest data satisfying pre-determined criteria with respect to statistical data selected based at least in part upon income data as a result of falling between lower and upper ranges of the selected statistical data, and  FIG. 10B  shows mortgage interest data that is suspect due to not satisfying pre-determined criteria with respect to statistical data, and  FIG. 10C  illustrates a field containing the suspect data being highlighted in order to specifically identify the suspect data; 
         FIGS. 11A-B  illustrate comparisons performed by a tax data analysis program in which  FIG. 11A  shows deduction data in the form of retirement contribution data satisfying pre-determined criteria with respect to statistical data selected based at least in part upon income data as a result of falling between lower and upper ranges of the selected statistical data, and  FIG. 11B  shows charitable contribution data that is suspect due to not satisfying pre-determined criteria with respect to statistical data; 
         FIG. 12  is a block diagram of components of a computing apparatus or system in which various embodiments may be implemented or that may be utilized to execute various embodiments; and 
         FIGS. 13A-D  illustrate screens displayed by a tax preparation program related to a final review to check for any missed credits or deductions and guaranteeing calculations performed on entered data and to an analysis of risk of audit. 
     
    
    
     DETAILED DESCRIPTION OF ILLUSTRATED EMBODIMENTS 
     Embodiments relate to notifying a preparer of an electronic financial document such as an electronic tax return of suspect or questionable numeric or quantifiable data. A preparer may be notified of suspect data at various times during preparation of an electronic tax return including in real time, e.g., after entry of suspect data. An alert is issued to the preparer to notify the preparer of the suspect data and, according to embodiments, is specific in that the alert identifies a particular field or particular data, e.g., by form and number or with another specific identification. For this purpose, embodiments identify statistical data based upon a first type or category of tax data, and compare a second type or category of tax data with the selected statistics. 
     For example, referring to  FIG. 1 , in one embodiment, a method  100  for identifying suspect data of an electronic tax return includes receiving first quantifiable, numeric tax data and second quantifiable, numeric tax data at a computing apparatus utilized to prepare an electronic tax return at step  102 , and selecting statistical data based at least in part upon the first numeric tax data with the tax preparation program at step  104 . At step  106 , the tax preparation program determines whether second numeric tax data is suspect data based at least in part whether second numeric tax data satisfies pre-determined criteria relative to the selected statistical data, and if not, generates an alert to inform the preparer of suspect data at step  108 . 
     Thus, with embodiments, tax return data from the electronic tax return being prepared and statistical data from an external source are utilized to identify suspect or questionable tax return data so that the preparer can verify or correct the suspect data. If data is not determined to be suspect, then embodiments are transparent to the preparer who is otherwise unaware that data being entered into the electronic tax return is being analyzed to determine whether it is suspect or questionable. Further aspects of embodiments are described with reference to  FIGS. 2-12 . 
     Referring to  FIG. 2 , a system  200  constructed according to one embodiment for identifying suspect or questionable tax return data according to the embodiment illustrated in  FIG. 1  and other embodiments includes or involves a first computing apparatus or computer  210  on which a tax preparation program  212  (identified as “tax program”  212  in  FIG. 2 ) executes to prepare an electronic tax return  214  (identified as “tax return”  214  in  FIG. 2 ) based upon tax return data  213  received at the preparer computer  210 . The preparer computer  210  may a home or business computer utilized by the preparer  215  who may be an individual preparing his or her own personal tax return  214  or an accountant or a tax professional preparing a personal or corporate or business entity tax return  214 . For ease of explanation, reference is made to a user or preparer  215 , an electronic tax return  214  and a user or preparer computer  210 , but it should be understood that embodiments may involve individuals or consumer-level tax preparation programs  212  and corporate or business entity tax returns and professional tax preparation programs  212 . Examples of tax preparation programs  212  that may be programmed to incorporate embodiments or that may utilize embodiments that exist as a separate program include desktop or online versions of TURBOTAX, PROSERIES, and LACERTE, available from Intuit Inc. and other tax preparation programs  212  including, for example, programs available from Thomson Reuters, CCH and Drake Software and H&amp;R Block TaxCut. TURBOTAX, PROSERIES AND LACERTE are registered trademarks of Intuit Inc. 
     According to embodiments, the preparer computer  210  further comprises or accesses a tax data analysis program  216  and a database  218  of statistical data  219 .  FIG. 2  illustrates the tax preparation program  212  and the tax data analysis program  216  as separate programs, but in other embodiments, the tax data analysis program  216  may be a part or module of the tax preparation program  212 . Further, while  FIG. 2  illustrates the tax preparation program  212 , the tax data analysis program  216  and the database  218  residing on the same preparer computer  210 , one or more of these system components may reside on a remote computer that is accessible by the preparer computer  210  through a network. For example,  FIG. 2  illustrates a computer  240  that hosts the database  218  of statistical data  219  and that may also be operably coupled to or in communication with other sources of statistical data  219 . 
     The system  200  may also include or involve an intermediate computer  220  managed by a host  225 . The intermediate computer  220  is configured or operable to format and electronically file electronic tax returns  214  with a computer  230  of a tax authority  235 , examples of which include a federal tax authority, e.g., the Internal Revenue Service (IRS), a state tax authority or other tax collecting entity of the United States, a state thereof, or another country or state thereof (generally, “tax authority”). Examples of hosts  225  that provide intermediate computers  220  for these purposes include, for example, Intuit Inc., which provides a second or intermediate computer  220  or server of the Intuit Electronic Filing Center for electronically filing tax returns  212  and other hosts  225  that provide tax preparation programs  212  and electronic filing servers. 
     In the illustrated embodiment, the tax preparation program  212  is a local program that executes on the preparer computer  210 , but embodiments may also involve on-line tax preparation programs  212  that are hosted by a separate computing apparatus or server (not shown in  FIG. 2 ) and accessed by the preparer computer  210  through a network using a browser or by the intermediate computer  220 , which may host the tax preparation program  212 , tax data analysis program  216  and/or database  218  that is accessed by the preparer computer  210 . Another computer that is in communication with the preparer computer  210  may also be utilized for this purpose. For ease of explanation, reference is made generally to a tax preparation program  212 , tax data analysis program  216  and database  218  on preparer computer  210 , but it should be understood that one or all of these programs and database may be hosted by a remote computing apparatus and accessible through a network. 
     For these and other communication purposes, the preparer computer  210  is operably coupled to or in communication with the second or intermediate computer  220  through a network  250   a , and the intermediate computer  220  is operably coupled to or in communication with the tax authority computer through a network  250   b , and the computer  240  of the source of statistics  219  is operably coupled to or in communication with one or more other system components such the preparer computer  210  through a network  250   c . Each of the networks  250   a - c  and other networks discussed herein (generally, network  250 ) may be different, or two or more networks  250  may be the same depending on the system configuration and communication protocols employed. One or more or all of the networks  250  may be, for example, a wireless or cellular network, a Local Area Network (LAN) and/or a Wide Area Network (WAN). Reference to a network  250  generally is not intended to refer to a specific network or communications protocol, and embodiments can be implemented using various networks  250  and combinations thereof. 
     Referring to  FIG. 3 , one embodiment is directed to a method  300  for analyzing data  213  of an electronic tax return  214  that is involved in a tax calculation performed by the tax preparation program  212  to identify suspect or questionable data (generally, suspect data  213   s ). The method  300  comprises the preparer  215  launching the tax preparation program  212  to begin preparation of the electronic tax return  214 . At steps  304 - 310 , various tax return data  213  is received by the tax preparation program  212  at the preparer computer  210  and one or more fields of interview screens of the tax preparation program  212  are populated with received data  213 . 
     For example, at step  304 , and with further reference to  FIGS. 4A-B  general or non-quantifiable alpha, alpha-numeric and numeric data  402  that is not part of a tax calculation performed by the tax preparation program  212  (defined as non-quantifiable data  402 ) is received by the tax preparation program  212  or at the computer  215 , and at step  306 , one or more fields  412   a - j  ( FIG. 4   a ) and  412   k - r  ( FIG. 4B ) of one or more interview screens  410   a ,  410   b  generated by the tax preparation program  212  are populated with respective general or non-quantifiable data  402   a - j  and  402   k - r  (generally,  402 ) to prepare at least a portion of the electronic tax return  214 . 
     At step  308 , first quantifiable, numeric tax data  404   a  is received by the tax preparation program  212  or at the preparer computer  210 . With further reference to  FIG. 4C , another interview screen  410  may comprise fields  414   a - k  (generally,  414 ) for receiving quantifiable, numeric tax data utilized in a tax calculation performed by the tax preparation program  212 . The first quantifiable, numeric data  404   a  may be various types of quantifiable, numeric data involved in a tax calculation (e.g., wages  404   a  in the embodiment illustrated in  FIG. 4C ). Thus, quantifiable numeric data  404  is defined as being involved in a tax calculation, in contrast to general, non-quantifiable data  402 , which may be numeric data that is not involved in a tax calculation. At step  310 , one or more fields  414  of one or more interview screens  410  are populated with the first numeric quantifiable data  404   a  to prepare at least a portion of the electronic tax return  212 . 
     It should be understood that steps  304 - 310  may be performed sequentially, simultaneously and/or in different orders depending on, for example the sequence and content of interview screens  410  and the manner in which data  213  is imported or entered into the electronic tax return  214  (e.g., manually or imported from another program or file). For example, non-quantifiable data  402  and the first quantifiable data  404   a  may be manually entered or typed into various fields  412  by the preparer  215  or imported from another program or file. Data may be imported into the electronic tax return  214  that is at least partially prepared based upon a prior filed electronic tax return prepared using the same or other tax preparation program or from a file of a personal finance program that can obtain financial and transaction data and categorize or organize the data for manipulation or analysis by a user. Examples of personal finance programs that may be utilized for this purpose include QUICKEN, QUICKEN Online, and MINT, which are also available from Intuit Inc., Microsoft Money, available from Microsoft Corporation, Redmond, Wash. and various online applications including, for example, Wasabe. QUICKEN and MINT are registered trademarks of Intuit Inc. In this case, both of the non-quantifiable data  402  and the first quantifiable data  404   a  may be received by the tax preparation program  212 , which populates respective fields  412 . Thus, the interview screen  410  examples shown in  FIGS. 4A-C  are provided to generally illustrate examples of interview screens  410  (which, in this illustrative example, are generated by TURBOTAX) and how different fields  412  thereof are for entry of different types of data, non-quantifiable data  402  that is not part of a tax calculation and quantifiable, and numeric data  404  that is part of a tax calculation. 
     Referring again to  FIG. 3 , at step  312 , the tax data analysis program  216  identifies another field  412   b  within the same or other interview screen  410  or identifies another, second type or category of numeric or quantifiable data  404   b  that is used in a tax calculation and related to or associated with the first numeric or quantifiable data  404   a . For this purpose, the first quantifiable data  404   a  can be categorized or sub-categorized as necessary, e.g., “wages” can be categorized to be an element of “income” or “adjusted gross income” and a retirement plan contribution can be categorized to be an element of a “deduction,” etc. It should be understood that other types of tax return data  213  can be categorized accordingly and related or associated with each other, and other categories may be utilized. 
     With further reference to  FIG. 5 , the method  300  further comprises step  312  of identifying one or more other fields  412   b  or other quantifiable, numeric data  404   b  related to or associated with the first field  412   a  for the first quantifiable tax data  404   a . For example, as shown in  FIG. 5 , the tax data analysis program  216  may have or access various tables  500  linking or associating types of data used in tax calculations and their respective fields  412  of various interview screens  410 . In the illustrated embodiment, the table  500  identifies types or categories  502  of data and their respective locations  504 , e.g., tax form and line numbers or interview screen  410  locations. 
     Thus, for example, types or categories of quantifiable tax data  404  such as adjusted gross income (“AGI”), charitable contributions, mortgage interest, and retirement plan contributions, all of which are involved in tax calculations, may be related to each other. The determination of which types of quantifiable data  404  are related to each other may be made by the source or manufacturer of the tax preparation program  212  or tax data analysis program  216 , the tax authority  135 , and/or the preparer  215 . Further, while embodiments are described with reference to identifying other types of data related to a first quantifiable data  404   a  in the form of AGI, embodiments may involve different types of first quantifiable data  404   a , and such associations may be made or identified by the source or manufacturer, tax authority  135  and/or preparer  125  at various times including before any data is received to begin preparation of the electronic tax return  214  or during preparation of the electronic tax return  214  (e.g., at step  312 , which may occur before or after steps  304 - 310 ). 
     Referring again to  FIG. 3 , at step  314 , the tax data analysis program  216  accesses the database  218  to retrieve or access statistical data  219  of the related types or categories of quantifiable data  404 , which may be stored in a database  219 , memory or file of a remote computer  240  or at the preparer computer  210  at step  316 . 
     For example, referring to  FIGS. 6A-B , and continuing with the example above, the tax data analysis program  216  may retrieve statistical data in the form of a table  600  that includes various ranges of statistical data. As generally illustrated in  FIG. 6A , the table  600  shows, for example, that most tax filers having quantifiable tax data  404   a  of Type 1 or Category 1 within a range 1C  602   a  usually have quantifiable tax data  404   b  of Type 2 or Category 2 within a range 2C  602   b , quantifiable tax data  404   c  of Type 3 or Category 3 within a range 3C  602   c , and quantifiable tax data  404   d  of Type 4 or Category 4 within a range 4C  602   d.    
       FIG. 6B  illustrates one example in that involves types of quantifiable data  404   a - d  including AGI, charitable contributions, mortgage interest deductions and retirement contributions. Thus, in the illustrated embodiment, quantifiable data  404   a  of one type of income is related to or associated with quantifiable data  404   b - d  of one or more deductions. In the illustrated embodiment, columns are for the four different types of quantifiable data  404   a - d , and each of the six rows includes related ranges  602  of the different types of quantifiable data  404 . Thus, ranges of income such as AGI are related to or associated with respective ranges of deductions such as charitable contributions, mortgage interest, and retirement plan contributions in the illustrated example. 
     With the statistics table  600  shown in  FIG. 6B , if the first quantifiable tax data  404   a  used in a tax calculation is an AGI amount between 75,000 and 99,000, statistics for this particular range of AGI may be selected  610  as shown in  FIG. 6C , and the selected statistics table  610   a  indicates that, based on statistical data  219 , most filers having AGIs ranging between 75,000 and 99,999 also have charitable contributions between 1,000 and 3,000, mortgage interest between 6,000 and 15,000 and retirement contributions between 5,000 and 9,000. Of course, the actual statistical data may vary, and these numbers are provided as examples to explain how embodiments may be implemented. As another example, referring to  FIG. 6D , if the first quantifiable data  404   a  is instead an AGI amount between 250,000 and 499,999, then the selected statistics table  610   b  indicates that most filers having these incomes have charitable contributions between 10,000 and 50,000, mortgage interest deductions between $25,000 and $80,000 and retirement contributions between 20,000 and 40,000. Thus, the tables  600 ,  610 ,  620  shown in  FIGS. 6A-D  serve to identify related types of quantifiable tax data and their respective statistical data or ranges for a given value of first quantifiable tax data which, in the example described above, is AGI. Embodiments, however, may involve other types of first quantifiable data  404   a  besides AGI and other types and ranges of tax return data related or associated with the other types of first quantifiable tax data  404   a.    
     Thus, while  FIGS. 6A-D  illustrate tables with various ranges of different types of data used in tax calculations, embodiments may involve various types of quantifiable data  404  including, but not limited to, income, deductions and/or tax credits. Further, it should be understood that embodiments may involve analysis of each type of quantifiable data  404  or only certain types of quantifiable data  404 . For example, while the above example involves AGI, charitable contributions, mortgage interest and retirement contributions, other types of quantifiable data  404  may not be analyzed for various reasons including the selection or preference of the source of the tax preparation program  212  or tax data analysis program  216 , the lack of statistics on that type of data, or preparer  215  preference. Further, it should be understood that while  FIGS. 6A-B  illustrate four types of data and six ranges thereof, embodiments may involve different numbers of data types and ranges. Moreover, while  FIGS. 6A-D  illustrate an embodiment involving one type of income (AGI) and three types of deductions (charitable contribution, mortgage interest, retirement plan contribution), embodiments may involve one type of deduction and multiple types of income or multiple types of income and multiple types of deductions. Further, the “income” and “deduction” categories and sub-categories or types of data thereof are provided as illustrative examples of how embodiments may be implemented. Additionally, embodiments may involve income, deductions, other categories, and combinations thereof. 
     Statistical data  219  used to create the table  610  may be received from various sources of tax data including, but not limited to, one or more tax authorities  135 , which collect data of a plurality of filed tax returns, the host  125 , which may collect or receive tax data statistics from various sources including the tax authority  135 , or another source such as a financial or tax journal or publication (e.g., Kiplinger&#39;s, Fortune, etc.) that collects and provides or publishes tax statistics  219 . The database  218  may be local, e.g., part of the tax preparation program  212  or tax data analysis program  216 , or the database  218  may be stored on a remote server  240  (e.g., as shown in  FIG. 2 ) that is accessed by the preparer computer  110  through a network  250   a.    
     The statistical data  219  can also be expressed in different ways and represent different numbers of tax filers. For example, in the illustrated embodiment, the statistical data  219  is expressed in terms of a range of statistical values having a minimum value and a maximum value, and these ranges may represent 70%, 80%, 90%, 95% and other percentages of other filers depending on the accuracy of the statistical data  219 . Further, referring to  FIG. 7 , statistical data  219  may be expressed in terms of a mean value (μ)  702  and one or more standard deviations (σ)  704  relative to that mean value  702 . As shown in  FIG. 7 , a set of statistical data  219  may have a mean value  702  and be expressed in the form of a normal distribution or bell curve  706  such that the number of filers having certain data can be expressed in terms of standard deviations  704  relative to the mean  702 . 
     For example, the statistical data  219  may indicate that filers with a certain range of wages or AGIs typically have a mean or average charitable contribution amount of $6 k with a standard deviation of 1 k (such that, e.g., about 68% of filers with these income levels have charitable contributions between 5-7 k) and two standard deviations of 2 k (such that, e.g., about 95% of filers with these incomes have charitable contributions between 4-8 k), and three standard deviations of 2.5 k (such that, e.g., about 99% of filers with these incomes have charitable contributions between 3.5-8.5 k). It should be understood that embodiments may be implemented using various types, sources and expressions of statistical data  219 , and that the types, associations and expressions of statistical data  219  provided above are illustrative examples of how embodiments may be implemented. The number of standard deviations  704  utilized may depend on the desired confidence in the statistical data  219 , and according to one embodiment, three standard deviations (3σ) 704 are utilized such that the statistical data encompasses ˜99% of all tax filers. 
     Referring again to  FIG. 3 , at step  318 , second numeric or quantifiable tax data  404   b  is received by the tax preparation program  212  or at the preparer computer  210 , and at step  320 , a second field  414   b  for quantifiable data is populated with the received second quantifiable tax data  404   b  to prepare portion of electronic tax return  214 . At step  322 , if the second field  414   b  populated at step  320  is related to or associated with the previously populated first field  414   a  for quantifiable data  404 , then the tax data analysis program  216  determines that the second field  414   b  is related to another populated field  414   a  for quantifiable data and has been populated with quantifiable data. For this purpose, fields  414  for quantifiable data  404  can be identified as such (e.g., within the tax preparation program  212  or the tax data analysis program  216 ) and/or utilizing tables shown in  FIGS. 6A-D . 
     At step  324 , the tax data analysis program  216  compares second quantifiable  404   b  data and the selected first statistical data  610 , and at step  326 , determines whether the second quantifiable tax data  404   b  satisfies pre-determined criteria with respect to selected statistical data  610 , which was selected based at least in part upon the previously entered first quantifiable data  404   a . In the illustrated embodiment involving a range of statistical data, the predetermined criteria may be whether the second quantifiable data  404   b  falls within the range. If not, then at step  328 , the tax data analysis program  216  may generate an alert  260  to inform the preparer that the second quantifiable data  404   b  is questionable or suspect data  213   s . The alert  260  may be in the form of a pop-up message or window or highlighting the field containing the suspect data  213   s . At step  330 , the preparer  215  can then correct the suspect data  213   s  or verify that it is correct, and in either case, at step  332  (and if the second quantifiable data  404   b  satisfies the pre-determined criteria), the preparer  215  continues preparation of electronic tax return  214 . 
     If during step  330 , the preparer  215  enters new second numeric tax data to correct the previously entered data, the tax data analysis program  216  may again compare the new data to the selected statistics, and if the new data does not satisfy the pre-determined criteria (e.g. due to a further data entry error), the tax data analysis program  216  may again alert  260  the preparer  215 . Further, if additional or third quantifiable data  404   c - n  related to or associated with the first quantifiable data  404   a  is entered into the electronic tax return  214 , comparisons of the third or other quantifiable data  404   c - n  and respective selected statistical data  610  can be performed to determine whether the third or other quantifiable data  404   c - n  satisfies the predetermined criteria or whether it is suspect data  213   s.    
     At stage  334 , the electronic tax return  214  is completed, the statistical cross-checks are completed, suspect data  213   s  is verified and/or corrected, the final review for missing data or missing deductions is completed, and the audit check is completed by the tax preparation program  212 . At step  336 , the completed electronic tax return  214  is transmitted from the preparer computer  210  to the intermediate computer  202 , which may be an electronic filing system or server that coordinates electronic filing of the completed tax return  214 . The electronic tax return  214  is formatted and checked as necessary by the intermediate computer  220  and at stage  338 , electronically filed with the tax authority computer  230 . Further aspects of embodiment and examples of how embodiments may be implemented are described in further detail with reference to  FIGS. 8A-11C . 
     Referring to  FIG. 8A , and referring again to the selected statistical data  610  of  FIG. 6C , one example involves the first quantifiable data  404   a  being AGI  802  and selected statistics related to AGI (represented by bi-directional arrows) including charitable contribution  804 , mortgage interest deduction  806  and retirement account contributions  808  such that statistics of the charitable contribution, mortgage interest and retirement contribution are selected  610  based on the entered AGI such that when data of the charitable contribution, mortgage interest and retirement contribution are entered, they are compared to respective selected statistics  610  to determine whether the charitable contribution, mortgage interest and retirement contribution data are suspect data  123   s.    
     In the illustrated example, the AGI income value entered into the AGI field is 80,000, which is within the range of 75,000 and 99,999 ( FIG. 6C ), and statistics for this AGI show that most (e.g., based on a certain percentage or standard deviation determination) filers having an AGI within the same range had a charitable contribution deduction value between 1,000 and 3,000 ( FIG. 8B  illustrating a curve representing charitable deduction values), a mortgage interest deduction between 6,000 and 15,000 ( FIG. 8C  illustrating a curve representing mortgage interest values) and a retirement contribution (e.g., 401k, IRA, etc.) between 5,000 and 9,000 ( FIG. 8D  illustrating a curve representing charitable deduction values). 
     Referring to  FIGS. 9A-B , when the charitable contribution amount  404   b  is entered into a field  414   b , the entered charitable contribution amount  404   b  is compared to the charitable contribution statistics selected based upon the AGI. Thus, as shown in  FIG. 9A , if the charitable contribution amount  404   b  is $1,000 (represented by vertical arrow), then this falls within the selected range of 1,000 to 3,000 such that this charitable contribution amount  404   b  is not suspect or questionable, and no alert  260  or other action is initiated by the tax data analysis program  216 . Thus, the analysis is transparent to the preparer  215 . 
     However, as shown in  FIG. 9B , if the charitable contribution amount  404   b  is $100 (represented by vertical arrow), e.g., due to a typographical or other error by the preparer when it was intended to enter $1,000 instead, then this amount falls outside of the selected range of 1,000 to 3,000 such that this charitable contribution amount  404   b  is determined by the tax data analysis program  216  to be suspect data  213   s.    
     Referring to  FIG. 9C , in the case of suspect data  213   s , the tax data analysis program  216  may generate a pop-up window message  902  alerting the preparer  215  of the suspect data  123   s  with specificity such that the preparer  125  is aware of the particular data that is suspect and/or the form and line number of the suspect data  123   s . The message  902  may ask whether the entered amount  404   b  is correct or for the preparer  125  to verify the amount. Further, the message  902  may inform the preparer  125  that the charitable contribution amount  404   b  that was entered is $100, but most taxpayers having a similar AGI have a charitable contribution between 1,000 and 3,000. Embodiments may involve various types of messages  902  that may be displayed at various times during preparation of the electronic tax return  214 . According to one embodiment, the message  902  is generated and displayed in real-time during preparation of the electronic tax return  214  such that the suspect data  123   s  is identified while the preparer  125  is on the same interview screen  410  (e.g., as shown in  FIG. 9C ). According to another embodiment, suspect data  123   s  issues can be compiled in a report that is displayed to the preparer  125  during a statistics check, which may follow completion of the electronic tax return  124  that is separate from and considers different data compared to the standard final review and audit check ( FIGS. 13A-D ). 
     Referring to  FIGS. 10A-C , a similar analysis can be performed on the mortgage interest data. Referring to  FIG. 10A , if the mortgage interest amount is $9,000, then this falls within the selected statistical range of 6,000 to 15,000 such that this mortgage interest amount is not suspect or questionable, and no alert or other action is initiated by the tax data analysis program. Thus, the analysis is transparent to the preparer. 
     However, as shown in  FIG. 10B , if the mortgage amount is $3265 (e.g., due to a data entry or import error), then this falls outside of the selected range of 6,000 to 15,000 such that this mortgage interest amount  404   c  is determined by the tax data analysis program to be suspect data  123   c . In this case, the tax data analysis program  116  may generate a pop-up window message  902  (as described with reference to  FIG. 9C ) alerting the preparer  125  of the suspect data  123   s  with specificity such that the preparer  125  is aware of the potential error. 
     In an alternative embodiment, as shown in  FIG. 10C , the field  414   c  containing the suspect mortgage interest data may be highlighted or emphasized  1002  (e.g. by shape, color, effect, etc.) to inform the preparer that this data is suspect  123   s . According to one embodiment, the field  414   c  or suspect data  123   s  can be highlighted or emphasized  1002  in real-time during preparation of the electronic tax return  124  such that the suspect data  123   s  is identified while the preparer  125  is on the same interview screen  410   c  (as shown in  FIG. 10C ). According to another embodiment, the field or suspect data can be highlighted  1002  during a statistics check following completion of the electronic tax return  124  that is separate from and considers different data compared to the standard final review and audit check ( FIGS. 13A-D ). 
     Referring to  FIGS. 11A-B  a similar analysis can be performed on the retirement contribution. Referring to  FIG. 11A , if the retirement contribution is $8,000, then this falls within the selected statistical range of 5,000 to 9,000 such that this retirement contribution is not suspect or questionable, and no alert  260  or other action is initiated by the tax data analysis program  216 . Thus, the analysis is transparent to the preparer  215 . 
     However, as shown in  FIG. 11B , if the retirement contribution amount  404   d  entered is $80,000 (e.g. due to a typographical or keyboard malfunction), then this falls outside of the selected range of 5,000 to 9,000 such that this retirement contribution amount  404   d  is determined by the tax data analysis program  126  to be suspect data  123   s . In this case, the tax data analysis program  126  may generate a pop-up window message  902  (as described with reference to  FIG. 9C ) or highlight or emphasize  1002  the field  414   d  containing the suspect data  123  (as described with reference to  FIG. 10C ) to alert the preparer of the suspect data  123   s.    
       FIG. 12  generally illustrates components of a computing device  1200  that may be utilized to execute embodiments and that includes a memory  1210 , account processing program instructions  1212 , a processor or controller  1220  to execute account processing program instructions  1212 , a network or communications interface  1230 , e.g., for communications with a network or interconnect  1240  between such components. The memory  1210  may be or include one or more of cache, RAM, ROM, SRAM, DRAM, RDRAM, EEPROM and other types of volatile or non-volatile memory capable of storing data. The processor unit  1220  may be or include multiple processors, a single threaded processor, a multi-threaded processor, a multi-core processor, or other type of processor capable of processing data. Depending on the particular system component (e.g., whether the component is a computer or a hand held mobile communications device), the interconnect  1240  may include a system bus, LDT, PCI, ISA, or other types of buses, and the communications or network interface may, for example, be an Ethernet interface, a Frame Relay interface, or other interface. The network interface  1230  may be configured to enable a system component to communicate with other system components across a network which may be a wireless or various other networks. It should be noted that one or more components of computing device  1200  may be located remotely and accessed via a network. Accordingly, the system configuration provided in  FIG. 12  is provided to generally illustrate how embodiments may be configured and implemented. 
     Method embodiments may also be embodied in, or readable from, a computer-readable medium or carrier, e.g., one or more of the fixed and/or removable data storage data devices and/or data communications devices connected to a computer. Carriers may be, for example, magnetic storage medium, optical storage medium and magneto-optical storage medium. Examples of carriers include, but are not limited to, a floppy diskette, a memory stick or a flash drive, CD-R, CD-RW, CD-ROM, DVD-R, DVD-RW, or other carrier now known or later developed capable of storing data. The processor  1220  performs steps or executes program instructions  1212  within memory  1210  and/or embodied on the carrier to implement method embodiments. 
     Although particular embodiments have been shown and described, it should be understood that the above discussion is not intended to limit the scope of these embodiments. While embodiments and variations of the many aspects of the invention have been disclosed and described herein, such disclosure is provided for purposes of explanation and illustration only. Thus, various changes and modifications may be made without departing from the scope of the claims. 
     For example, while certain embodiments described above involve entry of a type of income, e.g., AGI, and selection of statistics of types of deductions, e.g., charitable contributions, mortgage interest and retirement contributions, embodiments may also involve entry of a first type of income and selection of statistics of other types of income (e.g., interest income, dividend income, rental income, etc.) so that data of these other types of income can be compared to statistics selected based upon the first type of income. Further, embodiments may also involve entry of a first type of deduction and selection of statistics of other types of deductions so that data of these other types of deductions can be compared to statistics selected based upon the first type of deduction. Further, embodiments may also involve entry of a first type of income and selection of statistics of at least one other type of income and at least one type of deduction so that data of these at least one type of income and deduction can be compared to statistics selected based upon the first type of income. Additionally, embodiments may also involve entry of a first type of deduction and selection of statistics of at least one other type of income and at least one type of deduction so that data of these at least one type of income and deduction can be compared to statistics selected based upon the first type of deduction. Further, embodiments may involve analysis of data within the same or different interview screens and within the same or different forms as the type of data that was utilized to select statistics. Further, the type of data that is utilized to select statistics of other types of data may be in various sections of an electronic tax return including the beginning, middle and end portions of the electronic tax return. Embodiments may also involve tax credits and other types or categories of data and combinations of two or more of income, deductions and tax credits. 
     Moreover, while embodiments are described with reference to financial documents including electronic tax returns, embodiments may also involve other electronic financial documents of business or corporate entities that are utilized in financial calculations, which may or may not include tax calculations. For example, a “corporate or business entity” may be a US or foreign corporate or business entities such as Inc./Corp., LC, LLC, PLLC, S, PC, LP, LLP, LLLP, sole proprietorship, non-profits, etc., and embodiments may apply to analyze and verify electronic financial data within financial documents including, but not limited to, tax returns, auditor reports, balance sheets, statements of retained earnings, income statements, cash flow statements and other corporate or business electronic financial documents having electronic financial data. 
     Further, embodiments may be implemented within a tax preparation program, or the tax preparation program and tax data analysis programs may work together. 
     Additionally, embodiments may involve a type of quantifiable data being linked to one other type of data or multiple types of data of the same or different category. 
     Where methods and steps described above indicate certain events occurring in certain order, those of ordinary skill in the art having the benefit of this disclosure would recognize that the ordering of certain steps may be modified and that such modifications are in accordance with the variations of the invention. Additionally, certain of the steps may be performed concurrently in a parallel process when possible, as well as performed sequentially. Thus, the particular sequence of method steps is not intended to be limiting and is provided for ease of explanation. For example, upon entry of the first quantifiable numeric tax return data utilized in a tax calculation, statistics related to that data may be retrieved in response to entry of the first data or later upon entry of second data to be analyzed. 
     Accordingly, embodiments are intended to exemplify alternatives, modifications, and equivalents that may fall within the scope of the claims.