What does adding an outlier visualization to a canvas do?

Adding an outlier visualization to a canvas is primarily designed to highlight specific data points that deviate significantly from the rest of the dataset. These outliers often represent unique or interesting cases that may warrant further investigation, such as anomalies or errors in data collection, or they could indicate important insights. By bringing attention to these data points, users can make more informed decisions, identify trends, or deduce patterns that might not be visible through a standard analysis of the data.

This capability is crucial in data analysis, as it allows practitioners to focus on elements that could indicate underlying issues or opportunities. Understanding outliers also plays a significant role in statistical modeling, as their presence can significantly influence the results of analyses, skewing averages or misrepresenting the dataset's behavior. Therefore, the feature of outlier visualization empowers users to engage with their data more critically and comprehensively.