How To Make A Histogram From A Frequency Table? Key Facts

A histogram is a graph that shows how often different values appear in a set of data. To make one from a frequency table, you plot the class intervals on the horizontal axis and the frequencies on the vertical axis, then draw bars for each interval. The key is that the bars touch each other, because the data is continuous and there are no gaps between the categories.

What Is a Frequency Table and How Does It Feed a Histogram?

A frequency table organizes raw data into groups, called class intervals, and counts how many data points fall into each group. For example, if you recorded the heights of 30 people, you might group them into intervals like 60–64 inches, 65–69 inches, and 70–74 inches. The count for each group is the frequency.

The frequency table is the direct source for the histogram. Each row in the table becomes one bar on the graph. The class interval defines the width of the bar, and the frequency defines its height. No calculation is needed to convert the table into the chart — you are simply drawing what the table already says.

Histograms are commonly used in healthcare, quality control, and education because they reveal the shape of a data set at a glance. You can see whether most values cluster in the middle, spread out evenly, or skew to one side.

How To Make A Histogram From A Frequency Table: Step by Step

Start by confirming your frequency table has two columns: one for the class intervals and one for the frequencies. The intervals must be consecutive and not overlap. For example, 0–9, 10–19, 20–29 works. Overlapping intervals like 0–10 and 10–20 do not work because a value of 10 would fall into both groups.

Draw a horizontal axis and label it with the variable you measured, such as height, time, or temperature. Mark the class intervals along this axis in order. The intervals should be evenly spaced if your table uses equal-width intervals, which is the standard approach.

Draw a vertical axis and label it “Frequency.” The scale on this axis should start at zero and go slightly above your highest frequency. For a frequency of 15, a scale of 0 to 18 or 0 to 20 works well.

For each class interval, draw a bar. The bar’s width spans the full interval, and its height reaches the frequency for that interval. The bars must touch each other with no gaps. This is the main visual difference between a histogram and a bar chart. A bar chart has spaces between bars because the categories are separate, like colors or brands. A histogram has no spaces because the data is continuous.

Label both axes clearly and give the chart a title that describes the data. A complete histogram lets a reader understand the distribution without needing the original data.

Choosing the Right Number of Class Intervals

The number of intervals you choose affects how the histogram looks. Too few intervals and the chart is too blocky, hiding important detail. Too many intervals and the chart becomes noisy, with many bars of similar height and no clear pattern.

A common guideline is to use between 5 and 15 intervals, depending on how many data points you have. A small data set of 20 values might only need 5 or 6 intervals. A large data set of 200 values can support 10 to 15 intervals without becoming cluttered.

There are formal rules for calculating interval width, such as Sturges’ formula, but for most practical purposes, a simple approach works. Divide the range of your data — the highest value minus the lowest value — by the number of intervals you want. Round up to a convenient number. If your data ranges from 50 to 90 and you want 8 intervals, the range is 40, and 40 divided by 8 is 5. So your intervals would be 50–54, 55–59, and so on.

The goal is a chart that communicates the shape of the data clearly. If the histogram looks misleading, adjusting the interval width is a legitimate step. Just be aware that changing the intervals changes the visual story, so choose intervals before drawing conclusions.

Common Mistakes to Avoid

The most frequent error is leaving gaps between bars. This happens when people confuse a histogram with a bar chart. In a histogram, the bars touch because the data is continuous. A value of 64 and a value of 65 belong to neighboring intervals, and the boundary between them is not a real separation in the data.

Another mistake is using intervals of unequal width without adjusting the bar heights. If one interval is twice as wide as the others, its bar should be half as tall for the same frequency, otherwise the visual area of the bar overstates the count. Most basic frequency tables use equal-width intervals to avoid this issue entirely.

Mislabeling the axes is also common. The horizontal axis must show the actual measurement scale, not just the interval numbers. The vertical axis must be labeled “Frequency” with a clear scale. A histogram without axis labels is not interpretable by anyone else.

Finally, do not use a histogram for categorical data. If your data consists of categories like blood types or yes/no responses, a bar chart is the correct choice. A histogram is only appropriate for numerical, continuous data.

Reading a Histogram: What the Shape Tells You

Once the histogram is drawn, the shape of the bars reveals important information about the data. A symmetrical, bell-shaped histogram suggests the data follows a normal distribution, where most values cluster around the center and taper off at both ends.

A histogram skewed to the right has a long tail on the right side, meaning a few values are much higher than the majority. This is common in income data and hospital length-of-stay data. A histogram skewed to the left has a long tail on the left, meaning a few values are much lower than the rest.

A bimodal histogram has two distinct peaks. This often indicates that the data combines two different groups. For example, heights of adult men and women plotted together might show two peaks, one for each sex. This is a useful insight that is hard to see in a raw frequency table.

If all bars are roughly the same height, the data is uniformly distributed, meaning values are spread evenly across the range. Each of these patterns has different implications depending on the context, and recognizing them is one of the main reasons to use a histogram.

Histogram vs. Bar Chart: Knowing the Difference

These two charts look similar but answer different questions. A bar chart compares separate categories, such as average blood pressure by age group or sales by region. The bars do not touch because the categories are distinct and the order can be rearranged without losing meaning.

A histogram displays the distribution of a single continuous variable, such as blood pressure readings or patient ages. The bars touch because the intervals are consecutive parts of one continuous scale. The order of the bars cannot be rearranged — the intervals must stay in numerical order.

This distinction matters in practice. Using a bar chart for continuous data hides the distribution shape. Using a histogram for categorical data creates false gaps or false continuity that does not exist in the data. Choosing the right chart is the first step to communicating your data honestly.

Using Spreadsheet Software to Build a Histogram

Most people will not draw a histogram by hand. Spreadsheet programs like Microsoft Excel and Google Sheets can build one automatically from a frequency table, but the process differs slightly from a standard chart.

In Excel, you can use the built-in histogram chart type. Select your frequency table, go to the Insert tab, and choose the histogram chart. Excel will generate the chart, but you may need to adjust the bin width — the size of the intervals — to match your table. Excel sometimes groups data differently than your table does, so check that the bars match your frequencies.

In Google Sheets, the process requires creating a column chart and then removing the gaps between bars. Select your data, insert a column chart, then go to the customization options and set the gap width to zero. This turns the column chart into a histogram visually.

Whichever tool you use, verify the final chart against your frequency table. The heights of the bars must match the frequencies exactly. If they do not, the software has grouped the data differently, and you need to adjust the settings.