Examine the center and spread of the distribution. Assess how the sample size may affect the appearance of the stemandleaf plot.
For each row, the number in the "stem" (the middle column) represents the first digit (or digits) of the sample values. The "leaf unit" at the top of the plot indicates which decimal place the leaf values represent.
The first row of the stemandleaf plot of Wait times has a stem value of 8 and contains the leaf values 0, 2, and 3. The leaf unit is 1. Thus, the first row of the plot represents sample values of approximately 80, 82, and 83.
The following stemandleaf plot shows customer wait times. The median is in the row that has values between 95 and 99. The values range from 80 to 119.
 

Investigate any surprising or undesirable characteristics. For example, the stemandleaf plot of customer wait times showed a wider that expected spread. An investigation revealed that a software update caused instability and delays.
The sample size can affect the appearance of the graph. The sample size is displayed at the top of the stemandleaf plot. In the previous example, the sample size is 50 (N=50).
Because a stemandleaf plot represents each data value, it is best when the sample size is less than approximately 50. If the sample is greater than 50, the data points on the plot may extend too far, and the distribution may be difficult to assess. If you have more than 50 data points, consider using Boxplot or Histogram.
Skewed data and multimodal data indicate that data may be nonnormal. Outliers may indicate other conditions in your 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. Often, skewness is easiest to detect with a histogram or a boxplot.
The following stemandleaf plots are skewed. The stemandleaf plot with rightskewed data shows wait times. Most of the wait times are relatively short, and only a few wait times are long. The stemandleaf plot with leftskewed 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 stemandleaf plot, isolated values at the ends identify possible outliers. For example, on the following stemandleaf plot, the last value at the bottom of this plot could be an outlier.
Try to identify the cause of any outliers. Correct any dataentry errors or measurement errors. Consider removing data values that are associated with abnormal, onetime events (special causes). Then, repeat the analysis.
Multimodal data have multiple peaks, also called modes. Multimodal data often indicate that important variables are not yet accounted for.
If you have additional information that allows you to classify the observations into groups, you can create a group variable with this information. Then, you can create the graph with groups to determine whether the group variable accounts for the peaks in the data.