Visualize the distribution of data with a classic stem-and-leaf display for hands-on exploratory analysis.
Last updated: March 2026
Enter numerical values to visualize
A stem-and-leaf plot is a simple graphical method for displaying the distribution of data. It splits each data value into two parts: the "stem" (leftmost digit(s)) and the "leaf" (rightmost digit). For example, the value 23 has stem 2 and leaf 3. This method preserves the original data values while showing the shape of the distribution.
Stem-and-leaf plots are particularly useful for small to medium datasets ({< 100 values) because they allow you to see both the overall pattern and individual data points simultaneously. They reveal clustering, gaps, outliers, and skewness quickly. Unlike histograms, stem-and-leaf plots don't lose information—you can reconstruct the exact dataset from the plot.
Common applications include quality control, exam score analysis, and any exploratory data analysis where preserving raw values matters.
Quiz Scores: Classroom Analysis
For 3-digit numbers like 145, you can use the first two digits (14) as the stem and the last digit (5) as the leaf. Just specify this in your key. The key is critical for avoiding confusion about the stem unit.
Yes. For example, 2.3 and 2.7 can have stem 2 and leaves 3, 7 (treating decimals as 0.3 and 0.7). Alternatively, multiply all values by 10 to convert to integers, then divide when interpreting.
A histogram shows frequency in bars (loses individual values), while stem-and-leaf shows actual values. Stem-and-leaf is better for small datasets where you want to see raw data; histograms are better for large datasets where trends matter more than specifics.
Include empty stems in the plot to show gaps. For example, if you have data in the 10s and 40s but nothing in the 20s or 30s, display stems 1, 2, 3, 4 with the middle ones having no leaves. This reveals clustering and outliers.
Ideally 10–100 data points. Fewer than 10 and the plot is too sparse; more than 100 and it becomes cluttered. For large datasets, use a histogram or grouped stem-and-leaf (e.g., splits like 1L for 10–14, 1H for 15–19) instead.
Yes! To compare two datasets, use the same stem for both but leaves go left (for group A) and right (for group B). This makes side-by-side distribution comparison easy and is popular in textbooks.
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