ACTG 4P97 Accounting Analytics Final Exam Notes PDF

**ACTG 4P97 -- Accounting Analytics Final Exam Notes** **Chapter 1: Data Analytics for Accounting and Identifying the Questions** [Chapter Objectives] - Define data analytics - Understand why Data Analytics matters to business - Explain why Data Analytics matters to accountants - Descr...

**ACTG 4P97 -- Accounting Analytics Final Exam Notes** **Chapter 1: Data Analytics for Accounting and Identifying the Questions** [Chapter Objectives] - Define data analytics - Understand why Data Analytics matters to business - Explain why Data Analytics matters to accountants - Describe the Data Analytics Process using the IMPACT cycle - Describe the skills needed by accountants - Explain how to translate common business questions into fields and values [What are Data Analytics ] - ***Data Analytics*** is the process of evaluating data with the purpose of drawing conclusions to address business questions - **Effective Data Analytics** provides a way to search through large, structured data and unstructured data - ***Data Analytics*** involves the technologies, systems, practices, methodologies, databases, statistics, and applications used to analyze diverse business data to give organizations the information they need to make decisions - **Big Data** refers to datasets that are too large and complex to be analyzed traditionally - [Another way to describe big data, is the 4 V's]: - **Volume** Refers to size of data - **Velocity** Refers to speed of data processing - **Variety** Refers to number of types of data - **Veracity** Refers to underlying quality of the data [How Data Analytics Affects Business ] - [PwC's 18^th^ annual global CEO survey showed]: - **86%** of CEO's find it important to master digital technologies and emphasize the use of technology for a competitive advantage - **85%** put high value on Data Analytics - Data Analytics could generate up to \$2 trillion in value per year - Data Analytics could transform the manner in which companies run their businesses [How Data Analytics Affects Accounting] - Data analytics is expected to have dramatic effects on auditing and financial reporting, as well as taxation, and managerial accounting **Auditing** - [A recent Forbes Insights/KPMG report showed]: - Audits must better adopt technology - Technology will improve the quality, transparency, and accuracy of the audit - Auditors believe that audit data analytics will improve the overall audit quality provided by firms - Impact of Data Analytics on audit has provided Public Accounting firms with incentives to invest in technology and personnel to capture, organize, and analyze financial statement data to provide enhanced audits, expanded services and added value to their clients. - External auditors stay engaged beyond the audit because with the use of data analytics, auditors will be collecting and analyzing the company's data similar to the way a business analyst would help management make better decisions. - Data analytics improves the capabilities of auditors in testing for fraudulent transactions - Data analytics enables auditors to improve its risk assessment in both its substantive and detailed testing **Management Accounting** - Data analytics are most similar to management accounting - Management accountants 1. **Are asked questions by management** 2. **Find data to address those questions** 3. **Analyze the data** 4. **Report the results to management to aid in their decision making** - Description of management accountant's task and that of the data analyst appear to be quite similar - [Data analytics:] - Improves cost analysis - Allows for better decision-making - Allows for better forecasting, budgeting, production and sales **Financial Reporting and Financial Statement Analysis** - With the use of various estimates and valuations in financial accounting, its believed that employing Data analytics may substantially improve the quality of the estimates and valuations. - Data analytics can help accountants make better estimates of collectability, and write-downs - It can help managers better understand the business environment through social media - Can help analysts identify risks and opportunities through analysis of internet services **Tax** - Tax executives must develop sophisticated tax planning capabilities that assist the company with minimizing its taxes in a way to prepare or avoid for an audit - **Tax Planning** makes tax data analytics valuable as it helps tax staffs predict what will happen in the future **For Example: Tax data analytics may be used in the to predict the potential tax consequences of a potential international transaction, R&D investment, or proposed merger or acquisition in one of their most value-adding tasks** - Companies can develop sophisticated tax planning strategies through the use of data analytics - The organization understands tax tables and other tax data to aid compliance [The Data Analytics Process Using the IMPACT cycle ] - **Data Analytics** is a process to identify business questions and problems that can be addressed with data - We can describe the Data Analytics Process by using a model called the **IMPACT** cycle by Isson and Harriott **The Impact Model** - **I**dentify the questions - **M**aster the data - **P**erform the test plan - **A**ddress and refine results - **C**ommunicate insights - **T**rack outcomes **Step 1 -- Identify the Questions** - Begins with understanding a business problem that needs addressing - Questions can arise from any source - Having a specific question that is potentially answerable by Data Analytics is an important first step - [Additional attributes to consider might include the following]: - ***Audience --*** Who is the audience that will use the results of the analysis (internal auditor, CFO, financial analyst, tax professional) - ***Scope --*** Is the question too narrow or too broad? - ***Use --*** How will the results be used? Is it to identify risks? Is it to make data-driven business decisions? - **Examples of potential questions accountants might address using Data Analytics:** - **Are employees circumventing internal controls over payments?** - **Are there any suspicious travel and entertainment expenses?** - **Are our customers paying us in a timely manner?** - **How can errors be identified?** **Step 2 -- Master the Data (CH2)** - **Mastering the data** requires one to know what data are available and whether those data might be able to help address the business problem - We need to know everything about the data, including how to access, availability, reliability, frequency of updates, what time periods the data covers in order for it to address the problem at hand **Step 3 -- Perform Test Plan** - After mastering the data and after the data are ready, we are prepared for analysis - With the data ready to be analyzed we need to think about the right approach to the data to be able to answer the question - In **Data Analytics** we work to extract knowledge from the data to address questions and problems - Using available data, we see if we can identify a relationship between the **Dependent Variable** and **Independent Variable.** - [Provost and Fawcett detailed eight different approaches to Data Analytics]: - **Classification** -- Attempt to assign each unit in a population into a few categories - **Regression** -- Data approach used to predict a dependent variable value based on independent variable inputs using a statistical model - **Similarity Matching** -- Attempt to identify similar individuals based on data known about them - **For example -- A company may use similarity matching to find new customers that closely resemble their best customers** - **Clustering** -- Dividing individuals into groups in a useful and meaningful way - **For Example -- Clustering might be used to segment loyalty card customers into groups based on buying behavior** - **Co-occurrence grouping** -- Attempt to discover associations between individuals based on transactions involving them - **For Example -- Amazone may use this technique to sell another item to you by knowing what items are "Frequently Bought Together"** - **Profiling** -- Attempt to describe the behavior of an individual, group, or population by generating summary statistics about the data. Understanding the typical behavior through the use of summary statistics will help identify any abnormalities or outliers. Profiling can be used in accounting to identify fraud or transactions that warrant some additional investigation - **For example -- Mean, Median, Minimum, Maximum** - **Link Prediction** -- Attempt to predict connections between two data items - **For example -- Individual might have 22 mutual Facebook friends with me and we both attended Brigham Young University** - **Data Reduction** -- Data approach that reduces the amount of information that needs to be considered to focus on the most critical items **Step 4 -- Address and Refine Results** - After data has been analyzed in ***step 3*** the fourth step is to address and refine results - Identify issues with the analyses, possible issues and refine the model - Ask further questions - Explore the data - Rerun analyses **Step 5 -- Communicate Insights** - Communicate results through the use of executive summaries, static reports, digital dashboards, and data visualization. **Step 6 -- Track Outcomes** - Follow up on the results of the analysis [Data Analytical Skills and Tools Needed by Analytic -- Minded Accountants] - [Accountants need to be able to]: - Clearly articulate the business problem the company is facing - Communicate with the data scientists about specific data needs - Understand the underlying quality of the data - Draw appropriate conclusions based on the data presented and make timely recommendation's - Present results of findings to management in an accessible manner - [Skills analytic-minded accountants should have]: - **Data scrubbing and data preparation** Clean the data before analysis - **Data quality** Completeness, reliability or validity of the data - **Descriptive data analysis** Perform basic analysis to understand the quality of the data - **Data analysis through data manipulation** Demonstrate ability to sort, rearrange, and merge data to allow for improved analysis - **Data visualization and data reporting** Reports results of analysis in an accessible and user-friendly manner (use of charts) [Chapter 1 Summary ] - With data all around us, businesses and accountants are looking to Data Analytics to extract the value that the data might possess - Data analytics is changing the audit and the way that accountants look for risk. Now auditors can consider 100% of the transactions in their testing. It is helpful in finding the anomalous or unusual transactions. Data analytics is also changing the way financial accounting, managerial accounting, and taxes are done at a company - The **IMPACT** cycle is a means of doing Data Analytics that goes all the way from identifying the question, to mastering the data, to performing data analyses and communicating results. It is recursive in nature, suggesting that as questions are addressed, new important questions may emerge that can be addressed in a similar way - [Eight Data approaches address different wants of testing data]: 1. Classification 2. Regression 3. Similarity matching 4. Clustering 5. Co-occurrence grouping 6. Profiling 7. Link prediction 8. Data reduction - [Data analytic skills needed by analytical-minded accountants are specified and are consistent with the **IMPACT** cycle, including the following]: - Data scrubbing and data presentation - Data quality - Descriptive data analysis - Data analysis through data manipulation - Data visualization and data reporting **Chapter 2: Mastering the Data** [Chapter Objectives] - Understand available internal and external data sources and how data are organized in an accounting information system - Understand how data are stored in a relational database - Explain and apply extraction, transformation and loading (ETL) techniques to prepare the data for analysis - Describe the ethical considerations of data collection and data use [Overview] - As learned in chapter 1, Data analytics is a process and we follow an established data analytics model called the **IMPACT** cycle - **IMPACT** cycle begins with identifying business questions and problems that can be, at least partially, addressed with data - Once the problem is identified, the next step is **[MASTERING THE DATA (CH2)]** - **Mastering the Data** requires you to identify and obtain the data needed for solving the problem. - **Mastering the Data** requires strong understanding of what data are available to you and where they are sorted, along with the skills of ***extracting, transforming, and loading*** (**ETL**) the data in preparation for data analysis - [ETL process is made up of the following five steps:] 1. **Determine purpose and scope of the data** 2. **Obtain the data** 3. **Validate the data for completeness and integrity** 4. **Clean the data** 5. **Load the data in prep for data analysis** [How are Data Used and Stored in The Accounting Cycle ] - Before being able to identify and obtain the data, you must have a comfortable grasp on what data are available to you and where such data are stored **Internal and External Data Sources** - Data may come from a variety of sources, either internal or external - **Internal Data Sources** include accounting information system, supply chain management system, customer relationship management system, and human resource management system - **Enterprise Resource Planning (ERP)** is a category of business management software that integrates all the departments (Accounting, HR, Finance, Operations) of the business into one system - **Accounting Information System** is a system that records, processes, reports, and communicates the results of business transactions to provide financial and nonfinancial information for decision making purposes. - **Supply Chain Management (SCM) System** includes information on active vendors, the orders made to date, or demand schedules for what component of the final project is needed - **Customer Relationship Management (CRM) System** oversees all interactions with current and potential future customers with the goal of improving relationships. - **Human Resource Management (HRM) System** manages all interactions with current and potential employees **Accounting Data and Accounting Information Systems** - Most commonly, data are stored in either flat file of a database - The most common example of a flat file is a range of data in an Excel spreadsheet - **Flat File** is a means of maintaining all of the data you need in one place - Data can be found throughout various systems - In most cases, you need to know which tables and attributes contain the relevant data - **Unified Modeling Language** is one way to understand databases **Exhibit 2-2 Procure-to-Pay Database Schema (Simplified)** [Data and Relationships in a Relational Database ] - Structured Data should be stored in a **normalized** **relational database** - **Relational Database** is a means of storing data in order to ensure that the data are complete, not redundant, and to help enforce business rules. - Benefit of storing data in a normalized database outweighs the downside of having to export, validate, and sanitize the data every time we need to analyze information - Storing data in a normalized, relational database instead of a **flat file** ensures that data are complete, and not unnecessary - **[Relational Databases Ensure That Data:]** - **Completeness** Data are complete or include all data - **No Redundancy** Data aren't redundant, so they don't take up too much space - **Business rules enforcement** Follow business rules and internal controls - **Communication and integration of business processes** Aid communication and integration of business processes [Primary Keys, Foreign Keys, and Descriptive Attributes] - This is a brief overview of the different types of attributes in a table and how these attributes support the relationships between tables - Every column in a table must be both unique and relevant to the purpose of the table - [Three types of columns]: - **[Primary key]** Ensures each row in the table is unique, so it is often referred to a s a "unique identifier" - **For example: a student ID number is a unique identifier** - [One of the biggest differences between a flat file and a relational database] is simply how many tables there are when you request your data into a flat file, you receive one big table with a lot of redundancy - This may be ideal for analyzing data, when the data are stored in the database, each group of information is stored in separate table, then the tables are related to each other using a primary and foreign key relationship. - **Foreign key** Another type of attribute with the function of creating a relationship between two tables - **Descriptive Attributes** Exist in relational databased that are neither primary or foreign keys. These attributes provide business information but are not required to build a database - **For example, Company name, or employee address. Supplier Name is a critical piece of data when it comes to understanding the business process, but it is not necessary to build the data model** - **Composite Primary key** is a special case of a primary key that exists in linking tables. The composite primary key is made up of the two primary keys in the table that it is linking - As we can see Exhibit 2-3 has a lot of detail in it that requires two attributes to combine as a primary key [Data Dictionaries] - **Data Dictionary** is a centralized repository of descriptions for all of the data attributes of the datasets - [**Data Dictionaries** define what data are acceptable]: - For each attribute, we learn: - What type of key it is - What data are required - What data can be stored in it - How much data is stored - Creating and using a **Data Dictionary** is essential as it helps database administrators and analysts locate and understand various pieces of data [Extract, Transform, and Load (ETL) The Data] - Once you've understood the data via data dictionaries, you are prepared to request the data from the database manager or extract the data yourself - **ETL** process begins with identifying which data you need and is - [The requesting data is an iterative process involving 5 steps]: 1. **Determine the purpose and scope of the data request** 2. **Obtaining the data** 3. **Validating the data for completeness and integrity** 4. **Cleaning the data** 5. **Loading the data for analysis** **[EXTRACT]** - Determine exactly what's needed to answer the business questions at hand - Requesting the data involves the first two steps of the ETL process **Step 1: Determine the Purpose and Scope of the Data Request** - [Ask a few questions before beginning the process]: - What is the purpose of the data request? - What do you need the data to solve? - What business problem will it address? - Once the purpose of the data request is determined and scoped, the next step is to determine who to ask, and what is exactly needed and in what format (Excel, PDF....) **Step 2: Obtain the Data -- Questions** - How will data be requested and/or obtained? - Do you have access to the data yourself, or do you need to request a database administrator or the information systems department to provide the data for you? - If you need to request the data, is there a standard data request form that you should use? - Where are the data located in the financial or other related systems? - [There are a couple methods to obtain data]: - **Obtaining the Data via a Data Request** - Necessary to specify the format in which you would like to receive the data, it's often preferred to receive data in a flat file with the first row containing all of the column headings and each subsequent row filled with the data and the file row being a subtotal of the data - When receiving the data its crucial that you understand what the data is representing. If a ***Data Dictionary*** isn't available, it's good to speak with the database users to get a better understanding. - **Obtaining the Data Yourself** - In some cases, you will have direct access to a database or information system that holds all or some of the data you need - [If you have direct access to a data warehouse, you can use SQL and other tools to pull the data yourself]: 1. Identify the tables that contain the information you need 2. Identify which attributes, specifically, hold the information you need in each table 3. Identify how those tables are related to each other **[TRANSFORM ]** **Step 3: Validate the Data for Completeness and Integrity** - Anytime data is moved from one location to another its possible that there could be a loss of data in the transition, thus its critical to ensure that the extracted data are complete - Being able to validate the data successfully requires you to not only have the technical skills to perform the task, but also to know your data well - [The following four steps should be completed to validate the data after extraction]: 1. **Compare the number of records that you extracted to the number of records in the source database** Provides a quick snapshot on whether any data was skipped or mismatched 2. **Compare descriptive statistics for numeric fields** Calculating the minimums, maximums, averages and medians will ensure that the numerical data were extracted completely 3. **Validate Date/Time fields** Similar approach to calculating descriptive statistics 4. **Compare string limits for text fields** **Step 4: Cleaning the Data** - After validating the data, clean them as necessary to improve the quality of data and subsequent analysis - [Four items that data will need to be cleaned after extraction and validation]: 1. **Remove headings and subtotals** Possible that your data could contain headings or subtotals that are not useful for analysis 2. **Clean leading zeros and nonprintable characters** 3. **Format negative numbers** Negative sign in comparison to presenting negatives in parentheses 4. **Correct inconsistencies across data** - Watch out for bad quality data - [5 main data quality issues to consider when you evaluate data for the first time]: 1. **Dates** Most common issue revolve around the date because there are various ways it can be presented 2. **Numbers** Numbers can be misinterpreted, particularly if they are manually entered 3. **International Characters and encoding** Data that spans multiple countries may include special characters such as accents 4. **Languages and Measures** Similar to international characters, data elements can include a variety of words or measures that have the same meaning (Cheese, Fromage, Pounds, LBS) 5. **Human Error** Manual inputs have high probability of human error **[LOAD ]** **Step 5: Leading the Data for Data Analysis** - Finally, you can now import your data into the tool of your choice and expect the functions to work properly [Ethical Considerations of Data Collection and Use] - Mastering the data goes beyond the ETL Processes - Mastering the data consists of assurance that the data collection is secure but [also done with ethical practices] - Potential ethical issues include an individual's right to privacy and whether assurance is offered that certain data are not misused - **For example: is the individual about who data is collected about able to limit those who have access to her personal information, and how those data are shared and used?** - [Institute of Business Ethics suggests that companies consider the following six questions to allow a business to create value from data use and analysis]: 1. How does the company use data, and to what extent are they integrated into firm strategy 2. Does the company send a privacy notice to individuals when their personal data are collected? 3. Does the company assess the risks linked to the specific type of data the company uses? 4. Does the company have safeguards in place to mitigate the risks of data misuse? 5. Does the company have the appropriate tools to manage the risks of data misuse? 6. Does our company conduct appropriate due diligence when sharing with or acquiring data from third parties? [Chapter 2 Summary ] - First step in the **IMPACT** cycle is to identify the questions that you intend to answer through your data analysis project. Once a data analysis problem or questions has been identified, the next step in the **IMPACT** cycle is mastering the data, which can be broken down to mean obtaining the data needed and preparing it for analysis. - In order to obtain the right data, it's important to have a firm grasp of what data are available to you and how that information is stored - Data are often stored in a relational database, which helps to ensure that an organization's data are complete and to avoid redundancy. Relational Databases are made up of tables with uniquely identified records and are related through the usage of foreign keys - To obtain the data, you will either have access to extract the data yourself or you will need to request the data from a database administrator or the information systems team. If the latter is the case, you will complete a data request form, indicating exactly which data you need and why. - Once you have the data, they will need to be validated for completeness and integrity -- that is, you will need to ensure that all of the data you need were extracted, and that all data are correct. Sometimes when data are extracted, some formatting or sometimes even entire records will get lost, resulting in inaccuracies. Correcting the errors and cleaning the data is an integral step in mastering the data. **Chapter 3: Performing the Test Plan and Analyzing the Results** [Chapter Objectives] - Understand four categories of Data Analytics - Describe some descriptive analytic approaches, including summary statistics and data reduction - Explain the diagnostic approach to Data Analytics, including profiling and clustering - Understand predictive analytics, including regression and classification - Describe the use of prescriptive analytics, including machine learnings and AI [Performing the Test Plan] - Third step of the **IMPACT** cycle model is "**PERFORMING**" - In this step, different data analytics approaches help us understand what happened, why it happened, what we can expect to happen in the future, and what we should do based on what we expect will happen - These approaches help us address the business questions and provide information to support accounting and management decisions - Data Analytics approaches rely on a series of tasks and models that are used to understand data and gain insight into the underlying cause and effect of business decisions - [There are four main categories of Data Analytics]: - **Descriptive Analytics --** Procedures that summarize existing data to determine what has happened in the past - **For example:** **Summary statistics (count, max, min, average, median, standard deviation, variance)** - **Diagnostic Analytics** -- Procedures that explore the current data to determine why something happened the way it has, typically comparing it to something - **For example: They allow users to drill down into data and see how it compares to budgets, competitors, or trends. (Analyzing why sales dropped in a region)** - **Predictive Analytics** -- Procedures used to generate a model that can be used to determine what is likely to happen in future periods - **For example: Regression analysis, forecasting, classification, and other predictive modeling** - **Prescriptive Analytics** -- Procedures that model data to enable recommendations for what should be done in the future - **Descriptive and Diagnostic analytics** are usually paired together when you want to describe past data and then compare them to a benchmark to determine why the results are the way they are - **Predictive and Prescriptive analytics** pair well when you want to predict and outcome and then make a recommendation on how to follow up **Descriptive Analytics** - **Summary Statistics** describe a set of data in terms of their location **(mean, median)**, range **(standard deviation, minimum, maximum)**, shape **(quartile)**, and size **(count).** - **Data reduction or filtering** is used to reduce the number of observations to focus on relevant items **(i.e., highest cost, highest risk, largest impact, etc.).** Does this by taking a large set of data and reducing it to a smaller set that may be more helpful to identify critical information **Diagnostic Analytics** - **Profiling** identifies behavior of an individual, group or population by compiling summary statistics about the data and comparing the individuals to the population - **Clustering** helps identify groups of individuals that share common underlying characteristics - **Similarity matching** is a grouping technique used to identify similar individuals based on data known about them - **Co-occurrence grouping** discovers associations between individuals based on common events, such as transactions they are involved in **Predictive Analytics** - **Regression** predicts the numerical value of a dependent variable based on the slope and intersect of a line and the value of an independent variable - [**R**^**2**^]{.math.inline} indicates how closely the line fits to the data used to calculate the regression - **Classification** predicts a category for a new observation based on the manual identification of classes from previous observations **Prescriptive Analytics** - **Decision Support Systems** rule-based systems that gather data and recommend actions based on the input - **Machine learnings and artificial intelligence** learnings models or intelligent agents that adapt to new external data to recommend a course of action  [Descriptive Analytics ] - **Descriptive Analytics** help summarize what has happened in the past - **For example: a financial accountant would sum all of the sales transactions within a period to calculate the value for Sales Revenue that appears on the income statement** - An analyst would count the number of records in a data extract to ensure the data are complete before running a more complex analysis - An auditor would filter data to limit the scope to transactions that represent the highest risk. In all these cases, basic analysis provides an understanding of what has happened in the past to help decision makers achieve good results and correct poor results - [Two main approaches that are used by accountants today]: - Summary statistics - Data Reduction **Summary Statistics** - **Summary Statistics** describe the location, shape, and spread of a set of observations - Commonly include count, sum, minimum, maximum, mean, standard deviation, median, quartiles, correlation covariance, and frequency - Use of summary statistics helps the user understand what the data looks like **Data Reduction** - **Data Reduction** reduces the amount of detailed information being considered to focus on the most critical, interesting, or abnormal items (i.e., highest cost, highest risk) - Accomplishes this by filtering through a large set of data and reducing it to a smaller set that has the vast majority of the critical information of the larger set. - Data reduction approach is done primarily using ***[structured data]*** which is data that's stored in database or spreadsheets and readily searchable - [Data reduction involves the following steps]: - **Identify the attributes you would like to reduce or focus on** - **For example: an employee may commit fraud by creating a fictitious vendor and submitting fake invoices. Rather than evaluating every employee an auditor may be interested in the employees records only** - **Filter the results** - **Interpret the results** - **Follow up on results** [Diagnostic Analytics ] - **Diagnostic Analytics** provide insight into why things happened or how individual data values relate to the general population - Once data is summarized using descriptive techniques, we can drill into them and discover the numbers that are driving the outcome - Two common methods of diagnostic analytics include **profiling** and **cluster analysis** - Both of these methods provide insight into where a specific value lies relative to the rest of the sample or population **Profiling** - **Profiling** involves gaining an understanding of a behavior of an individual group, or population - **Profiling** is done primarily using **structured data** -- data that is stored in a database or spreadsheet and readily searchable - Profiling is used to discover patterns of behavior. In this example, the higher the Z-score (farther away from the mean), the more likely a customer will have a delayed shipment (Blue Circle) - [Data Profiling involves the following steps]: 1. Identify the objects or activity you want to profile 2. Determine the types of profiling you want to perform 3. Set boundaries or thresholds for the activity 4. Interpret the results and monitor the activity and/or generate a list of exceptions 5. Follow up on exceptions **Example of Profiling in Management Accounting** Management accounting relies heavily on diagnostic analytics in the planning and controlling process. Managers use profiling to compare variances from target ranges **Example of Profiling in Auditing** Profiling is also useful in continuous auditing. If we consider the dollar amount of each transaction, we can develop a Z-score by knowing the mean and standard deviation. An analysis of **Benford's Law** could also be used to assess a set of transactions. **Benford's Law** is the principle that in any large, randomly produced set of natural numbers, there is an expected distribution of the first, or leading, digit with 1 being the most common, 2 the next most, and down successively to the number 9. A bar graph illustrating Benford\'s law. - In a continuous audit, an auditor may use Benford's law to evaluate the frequency distribution of the first digits from a large set of numerical data **Cluster Analysis** - **Clustering** data approach works to identify groups of similar data elements and the underlying relationships of those groups - A data approach that attempts to divide individuals into groups in a useful or meaningful way - Cluster analysis works by calculating the minimum distance of all observations and groups those elements **Hypothesis Testing for Differences in Groups** - One way of uncovering causal relationships is to form hypotheses of what you expect will or will not occur - Begin by setting the Null Hypothesis and the alternative hypothesis - Test the P-value for statistical significance **The P-Value** - We describe findings as statistically significant by interpreting the P-Value of the statistical test - The *p-value* is compared to the alpha threshold - A result is statistically significant when the *p-value* is **LESS** than alpha - P-value \> alpha Fail to reject the null hypothesis - P-value \