Which of the following statements regarding hierarchical clustering is true?

The chosen answer is accurate as hierarchical clustering techniques have specific behaviors when handling data points, particularly outliers. When the method detects extreme values or outliers, it may indeed lead to situations where these outliers do not get assigned to any cluster. This often happens because the distance measures used to form clusters may place them too far from other points, resulting in a scenario where the algorithm decides to isolate such outliers rather than incorporate them into a cluster with similar, non-outlier data points.

This characteristic of hierarchical clustering emphasizes the sensitivity of the method to the data distribution, especially when extreme values are present.

The other options pose limitations or incorrect assertions about hierarchical clustering. The dendrogram produced by hierarchical clustering is indeed versatile; it allows the analyst to visualize the clusters and decide on various cluster numbers by cutting the dendrogram at different heights. The method's sensitivity means it may not be robust against small changes in data, particularly with outlier influence, and importantly, hierarchical clustering usually aims to include all data points into distinct clusters, making it less accommodating for aggressive outlier behaviors.