The present disclosure is directed toward systems and methods for identifying contributing factors associated with a metric anomaly. One or more embodiments described herein identify contributing factors based on statistical analysis and machine learning. Additionally, one or more embodiments identify sub-factors associated with each contributing factor. In one or more embodiments, the systems and methods provide an interactive display that enables a user to select a particular anomaly for further analysis. The interactive display also provides additional interfaces through which the user can view informational displays that illustrate the factors that caused the particular anomaly and how those factors correlate with each other.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. In a digital medium environment for performing a statistical analysis of web traffic data, a method for determining statistically significant contributors to a metric anomaly comprising: receiving a selection of an anomaly from a display of a trend associated with a particular metric; identifying a plurality of factors that contributed to the selected anomaly within the trend associated with the particular metric; determining, by at least one processor, a statistical significance for each of the plurality of factors that contributed to the selected anomaly within the trend associated with the particular metric; and presenting the most statistically significant factors that contributed to the selected anomaly. 2. The method as recited in claim 1, wherein receiving a selection of an anomaly from a display of a trend of related events comprises: displaying a range that illustrates how the particular metric is expected to perform during a period of time; overlaying the trend associated with the particular metric on the range; receiving a selection of a point within the overlaid trend associated with the particular metric that exists outside the displayed range. 3. The method as recited in claim 1, wherein identifying a plurality of factors that contributed to the selected anomaly within the trend associated with the particular metric comprises: receiving a selection of a period of time; querying a plurality of data associated with the particular metric within the selected period of time from a central repository; analyzing the plurality of data for the plurality of factors. 4. The method as recited in claim 3, wherein determining a statistical significance for each of the plurality of factors that contributed to the selected anomaly within the trend associated with the particular metric comprises: calculating a score for each of the plurality of factors that indicates the statistical significance of each of the plurality of factors; normalizing the score for each of the plurality of factors such that all the calculated scores are directly comparable. 5. The method as recited in claim 4, wherein normalizing the score for each of the plurality of factors comprises normalizing the score for each of the plurality of factors between −1 and 1. 6. The method as recited in claim 1, wherein presenting the most statistically significant factors that contributed to the selected anomaly comprises: ordering the plurality of factors based on the statistical significance of each of the plurality of factors; presenting the ordered plurality of factors such that the most statistically significant factors are presented first. 7. The method as recited in claim 1, further comprising presenting a display of a plurality of sub-factors associated with the presented factors, wherein the display of the sub-factors comprises an illustration of how each sub-factor associated with a particular factor contributed to the statistical significance of the particular factor. 8. The method as recited in claim 7, wherein the illustration of how each sub-factor associated with a particular factor contributed to the statistical significance of the particular factor comprises a hierarchical tree diagram, wherein each sub-factor associated with the particular factor is assigned a box, and the size of the box corresponds to how much the sub-factor contributed to the statistical significance of the particular factor. 9. The method as recited in claim 7, wherein the illustration of how each sub-factor associated with a particular factor contributed to the statistical significance of the particular factor comprises a scatter plot, wherein each sub-factor is associated with a plot point on the scatter plot, and each plot point is positioned within the scatter plot so as to indicate how much the sub-factor associated with the plot point contributed to the statistical significance of the particular factor. 10. In a digital medium environment for performing a statistical analysis of web traffic data, a method for determining statistically significant contributors to a metric anomaly comprising: receiving a selection of a metric from a display of metrics associated with user actions across a website; presenting a trend associated with the selected metric over a given period of time; receiving a selection of an anomaly within the displayed trend associated with the selected metric, wherein the anomaly represents an increase or decrease in user actions associated with the selected metric; identifying a plurality of factors that contributed to the selected anomaly within the trend associated with the selected metric; determining a statistical significance for each of the plurality of factors that contributed to the selected anomaly within the trend associated with the selected metric; and presenting the most statistically significant factors that contributed to the selected anomaly. 11. The method as recited in claim 10, wherein receiving a selection of the anomaly within the displayed trend associated with the selected metric comprises: identifying a range that illustrates an expected number of user actions associated with the selected metric; overlaying the trend associated with the selected metric on the identified range; receiving a selection of a point within the overlaid trend associated with the selected metric that exists outside the displayed range. 12. The method as recited in claim 11, wherein identifying a plurality of factors that contributed to the selected anomaly within the trend associated with the selected metric comprises; identifying a sample data group from a central repository representative of a complete data group associated with the metric; processing the sample data group to identify the most relevant portions of the complete data group; processing the most relevant portions of the complete data group to identify the plurality of factors. 13. The method as recited in claim 12, wherein determining a statistical significance for each of the plurality of factors that contributed to the selected anomaly within the trend associated with the selected metric comprises: calculating a score for each of the plurality of factors that indicates the statistical significance of each of the plurality of factors, wherein the statistical significance of each of the plurality of factors relates to how strongly each of the plurality of factors contributed to the increase or decrease in user actions associated with the selected metric; normalizing the score for each of the plurality of factors such that all the calculated scores are directly comparable. 14. The method as recited in claim 13, wherein normalizing the score for each of the plurality of factors such that all the calculated scores are directly comparable comprises normalizing the score for each of the plurality of factors between −1 and 1. 15. The method as recited in claim 14, wherein presenting the most statistically significant factors that contributed to the selected anomaly comprises: ordering the plurality of factors based on the statistical significance of each of the plurality of factors; presenting the ordered plurality of factors such that the most statistically significant factors are presented first. 16. The method as recited in claim 15, further comprising presenting a total number of user actions attributable to each of the plurality of factors. 17. A system comprising: at least one processor; and at least one non-transitory computer-readable storage medium storing instructions thereon that, when executed by the at least one processor, cause the system to: identify one or more anomalies associated with user actions across a website; identify a plurality of factors that contributed to a particular anomaly of the one or more anomalies, wherein the particular anomaly is associated with a particular metric; and determine a statistical significance for each of the plurality of factors that contributed to the particular anomaly associated with the particular metric. 18. The system as recited in claim 17, wherein identifying a plurality of factors that contributed to the anomaly within the trend associated with the particular metric comprises: querying a plurality of data associated with the particular metric within a period of time from a central repository; and analyzing the plurality of data for the plurality of factors. 19. The system as recited in claim 18, wherein determining a statistical significance for each of the plurality of factors that contributed to the selected anomaly within the trend associated with the particular metric comprises: calculating a score for each of the plurality of factors that indicates the statistical significance of each of the plurality of factors; and normalizing the score for each of the plurality of factors such that all the calculated scores are directly comparable. 20. The system as recited in claim 19, further comprising ranking the plurality of factors based on the normalized score associated with each of the plurality of factors, wherein the normalized score is between −1 and 1.