The interquartile range, or IQR, focuses on the distance between the first and third quartiles, so it describes the spread of the middle 50% of observations. The full range compares only the minimum and maximum. Considering both measures helps distinguish broad overall extent from the variability experienced by the central portion of the dataset.
Unequal spacing among the five values can suggest asymmetry. For example, a longer distance from the median toward one extreme than toward the other may indicate that observations extend farther on that side. Comparing the lower and upper portions of the summary provides a quick indication of skewness before examining the dataset in greater detail.
A minimum or maximum that lies far from the central quartile values may signal an unusually small or large observation. Such values can substantially enlarge the overall range without changing the middle 50% by the same amount. Examining the extremes alongside the quartiles helps identify potential unusual values and interpret the dataset’s shape more carefully.
Place the corresponding values from each summary side by side: compare medians for typical location, IQRs for middle-data variability, and minimum-to-maximum spans for overall extent. Differences in these positions can show that datasets have similar centers but different spread, or similar variability but different centers, making comparisons more precise than relying on a single average.
First arrange the observations in order, because the summary depends on their positions within the dataset. Then identify the two endpoints, the median, and the first and third quartiles using the selected quartile procedure. Finally, record the five values together and calculate the IQR by subtracting the first quartile from the third quartile.
The five values provide the key locations for a box-and-whisker plot: the quartiles define the box, the median marks its internal center line, and the minimum and maximum establish the outer span represented by the whiskers. The resulting graphic communicates center, spread, range, and possible skewness quickly, supporting exploratory data analysis and clear statistical reporting.