Outlier analysis is a method where abnormal data points are identified to remove inaccuracies that may highly and negatively affect data analysis results and conclusions (Bush, 2020). The process should be performed before the start of any data analysis method. When outliers are tested for and removed, it allows the analyst to effectively draw conclusions from integral data. Techniques that are used to identify outliers include z-score testing, graphing, and sorting, among others. In addition to these evaluation methods, the identification of outliers can also help identify errors in data collection, coding, and data entry. For example, if data collected requires specified data points during a specific time period, for lets say, 3 months, and data is only generated during the first two months, stops for two weeks, and then regenerates the following week, depending on the analysis, it can cause variations or extreme results. Invalid outliers and the processes involved in the creation of outliers should be evaluated to ensure data integrity. The Centers for Medicare and Medicaid Services utilizes a payment standardization method for Medicare Part D, which is specific to prescription drugs (Part D Payment Standardization, 2020). Part D programs negotiate with pharmaceutical companies directly to ensure standard costs are applied for specific drugs no matter which company sells them, no which packaging it comes in. If it has the same clinical dosage and strength, and for a specif