How does IQR improve perspectives in data analysis?

The interquartile range (IQR) is a statistical measure that quantifies data dispersion by focusing on the middle 50% of a dataset. By excluding outliers, it provides a clearer view of the variability and distribution within a dataset. This is particularly useful in data analysis because outliers can significantly skew results and lead to misinterpretations.

When calculating the IQR, the first (Q1) and third quartiles (Q3) are determined, and the IQR is calculated as Q3 - Q1. This range effectively captures the central tendency of the data while minimizing the influence of extreme values. As a result, analysts can gain better insights into the bulk of the data without the distortion caused by outliers, leading to more accurate conclusions and better decision-making.

Other perspectives on this question could include recognizing that including all outliers would provide a skewed understanding of the dataset (as seen in one option), and focusing only on the maximum value ignores other significant aspects of the data distribution. The IQR, by its design, emphasizes a balanced range that better represents the core data characteristics.