Interpret the key results for Boxplot

Complete the following steps to interpret a boxplot.

Step 1: Assess the key characteristics

Examine the center and spread of the distribution. Assess how the sample size may affect the appearance of the boxplot.

Center and spread

Examine the following elements to learn more about the center and spread of your sample data.
The median is represented by the line in the box. The median is a common measure of the center of your data.
Interquartile range box
The interquartile range box represents the middle 50% of the data.
The whiskers extend from either side of the box. The whiskers represent the ranges for the bottom 25% and the top 25% of the data values, excluding outliers.

Hold the pointer over the boxplot to display a tooltip that shows these statistics. For example, the following boxplot of the heights of students shows that the median height is 69. Most students have a height that is between 66 and 72, but some students have heights that are as low as 61 and as high as 75.

Investigate any surprising or undesirable characteristics on the boxplot. For example, a boxplot may show that the median length of wood boards is much lower than the target length of 8 feet.

Sample size (N)

The sample size can affect the appearance of the graph. For example, although the following boxplots seem quite different, both of them were created using randomly selected samples of data from the same population.

N = 15
N = 500

A boxplot works best when the sample size is at least 20. If the sample size is too small, the quartiles and outliers shown by the boxplot may not be meaningful. If the sample size is less than 20, consider using Individual Value Plot.

Step 2: Look for indicators of nonnormal or unusual data

Skewed data indicate that data may be nonnormal. Outliers may indicate other conditions in your data.

Skewed data

When data are skewed, the majority of the data are located on the high or low side of the graph. Skewness indicates that the data may not be normally distributed.

The following boxplots are skewed. The boxplot with right-skewed data shows wait times. Most of the wait times are relatively short, and only a few wait times are long. The boxplot with left-skewed data shows failure time data. A few items fail immediately and many more items fail later.


Some analyses assume that your data come from a normal distribution. If your data are skewed (nonnormal), read the data considerations topic for the analysis to make sure that you can use data that are not normal.


Outliers, which are data values that are far away from other data values, can strongly affect your results. Often, outliers are easiest to identify on a boxplot. On a boxplot, outliers are identified by asterisks (*).


Hold the pointer over the outlier to identify the data point.

Try to identify the cause of any outliers. Correct any data-entry errors or measurement errors. Consider removing data values that are associated with abnormal, one-time events (special causes). Then, repeat the analysis.

Step 3: Assess and compare groups

If your boxplot has groups, assess and compare the center and spread of groups.


Look for differences between the centers of the groups. For example, the following boxplot shows the thickness of wire from four suppliers. The median thicknesses for some groups seem to be different.


Look for differences between the spreads of the groups. For example, the following boxplot shows the fill weights of cereal boxes from four production lines. The median weights of the groups of cereal boxes are similar, but the weights of some groups are more variable than others.
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