You’d make a histogram instead of a bar chart when your data is numerical and continuous, like heights, temperatures, or income levels, rather than a set of distinct categories like favorite colors or campus locations. The two charts look similar but answer fundamentally different questions: a histogram shows how numerical data is distributed across a range, while a bar chart shows how many things fall into separate, unrelated categories.
The Core Difference: Numbers vs. Categories
Bar charts display categorical variables. Think of things you could label but not meaningfully average: college campuses, car brands, pizza toppings, job titles. Each bar represents a distinct group, and the height tells you a count or percentage. A Penn State enrollment bar chart, for example, might show 40,639 students at University Park, 27,100 at Commonwealth Campuses, and 8,360 at World Campus. These categories have no numerical relationship to each other. You could rearrange the bars in any order and the chart would still make sense.
Histograms display quantitative data, almost always continuous. Continuous data can take on infinite possible values within a range: someone’s commute time could be 22.3 minutes or 22.31 minutes or 22.317 minutes. Plotting every individual value on an axis would be impossible, so histograms group the data into ranges called bins. A bin might cover 20 to 25 minutes, and the bar’s height tells you how many observations landed in that range. The bins sit side by side with no gaps because the scale is continuous, one range flows directly into the next.
That’s why histograms have no spaces between bars and bar charts do. The gaps in a bar chart signal that each category is independent. The touching bars in a histogram signal that you’re looking at a single continuous scale sliced into intervals.
What Histograms Reveal That Bar Charts Can’t
The real reason to choose a histogram is that it lets you see the shape of your data’s distribution. When you plot a few hundred test scores into bins, patterns emerge that no bar chart could show.
Symmetry and skewness. A histogram immediately shows whether data clusters evenly around a center or leans to one side. Income data, for instance, typically skews right: most people earn in a lower range, with a long tail stretching toward high earners. Skewness like this is invisible in a bar chart because bar charts don’t have a continuous scale. The National Institute of Standards and Technology describes skewness as “the lack of symmetry” in a dataset, and calls the histogram “an effective graphical technique” for spotting it.
Spread and outliers. Histograms also reveal whether your data is tightly packed or spread out, and whether extreme values exist far from the center. Datasets with heavy tails (lots of unusually high or low values) look distinctly different from datasets where everything clusters near the middle. You can spot these patterns at a glance in a histogram, while a table of numbers might hide them entirely.
Multiple peaks. Sometimes data that looks like one group is actually two. If you histogram the heights of a mixed adult population, you might see two humps, one around average female height and one around average male height. This kind of bimodal pattern tells you something important about the underlying data that a simple average would mask.
How Bins Shape the Story
Because histograms group continuous data into intervals, the number and width of bins you choose affects what you see. Too few bins and the distribution looks oversimplified, hiding meaningful variation. Too many bins and the chart becomes noisy, with random spikes everywhere. Most statistical software uses a formula to pick a reasonable default. Sturges’ rule, published in 1926, was the first such formula and is still widely built into tools today. It calculates the number of bins based on the size of your dataset.
You don’t need to memorize binning formulas, but you should know that changing the bin width can change the visual impression. If a histogram looks oddly jagged or suspiciously smooth, adjusting the bins often clarifies the picture. This is a consideration that simply doesn’t exist with bar charts, where each category is predetermined by the data itself.
Two Ways to Read a Histogram
In a standard frequency histogram, the height of each bar tells you the count of observations in that bin. If the bar over “60–70 kg” reaches 45 on the y-axis, 45 people in the dataset weigh between 60 and 70 kilograms.
In a density histogram, the area of each bar (not the height) represents the percentage of data in that bin. The height instead reflects how concentrated the data is within that interval. This distinction matters when bins aren’t all the same width, because a tall, narrow bar and a short, wide bar could represent the same proportion of data. Most introductory uses stick with equal-width bins and frequency counts, where height and area tell you roughly the same thing.
When a Bar Chart Is the Right Call
Bar charts are the correct choice for nominal data (categories with no inherent order, like campus names or product types) and ordinal data (categories with a meaningful order but no measurable distance between them, like class standing: first-year, sophomore, junior, senior). You could rank those class standings, but the “distance” between sophomore and junior isn’t a number you can calculate. That makes it categorical, not continuous.
A common mistake is forcing categorical data into a histogram or continuous data into a bar chart. If your x-axis labels are names or groups, use a bar chart. If your x-axis is a number line that you’re dividing into ranges, use a histogram. Survey responses on a 1-to-5 scale sit in a gray area: they’re technically ordinal, but with few enough values that a bar chart showing each rating (1, 2, 3, 4, 5) usually works better than binning them.
Quick Decision Guide
- Your data is measurable on a continuous scale (time, weight, temperature, dollars): use a histogram.
- Your data falls into named groups (regions, brands, species, departments): use a bar chart.
- You want to see the shape of a distribution (where values cluster, how spread out they are, whether outliers exist): use a histogram.
- You want to compare counts across categories (which product sold most, which campus has the highest enrollment): use a bar chart.
- The order of bars on the x-axis matters mathematically (you can’t rearrange them without breaking the logic): that’s a histogram.
- You could sort the bars alphabetically, by size, or randomly and the chart still makes sense: that’s a bar chart.
The choice between the two isn’t aesthetic. It reflects the nature of your data. Getting it right means your reader sees the pattern you’re actually trying to communicate.