When comparing box plots, what is one important aspect to remember?

When comparing box plots, it is crucial to understand that the context of the data is essential. Box plots provide a visual summary of the data distribution, highlighting the median, quartiles, and potential outliers. However, these plots are representations of datasets that may vary significantly in their context — such as different populations, timeframes, or conditions under which the data were collected. Without considering the context, it can be misleading to interpret the results, as box plots may not tell the whole story about the data.

For example, if two box plots represent different groups, say the heights of students in two different schools, knowing about the demographics and characteristics of those schools can shed light on the differences you observe in the plots. Ignoring the context could lead to incorrect conclusions about the data's implications or significance.

In contrast to the other aspects mentioned, outliers are indeed represented in box plots; the interquartile range (IQR) plays a vital role in comparing spreads; and box plots can be used for non-normally distributed data as well. These points further emphasize why recognizing the context is fundamental when analyzing and comparing box plots.