Histogram vs. Bar Graph: Understanding the Differences and Choosing the Right One

Bar graphs and histograms are frequently confused visualization types that serve different purposes. This guide will clarify when to use a bar graph vs. a histogram, providing clear explanations, practical examples, and visual comparisons to help you make the right choice every time.

What is a Bar Graph?

A bar graph (or bar chart) is a graphical representation of categorical data using rectangular bars. Each bar's height (or width, in horizontal bar graphs) represents the value of that category.

Characteristics of a Bar Graph

• Categorical Data Representation: A bar graph shows data in discrete categories (e.g., product types, regions, age groups). This categorical nature distinguishes bar graphs from other chart types.

• Bars are separated: Each category is distinct, and spaces between bars emphasize this separation. This visual spacing communicates the discreteness of the data.

• Can Be Vertical or Horizontal: Bars can be arranged vertically (column chart) or horizontally. Horizontal arrangements work well when category names are lengthy.

• Flexible Scaling: Values can represent frequency (how often a category appears) or another measurable metric (percentage, revenue, etc.).

What is a Histogram?

A histogram is a graphical representation of continuous numerical data. Instead of categories, a histogram groups numbers into ranges (bins) and shows how frequently values appear in each range. Histograms reveal patterns, outliers, and the overall shape of data distributions.

Characteristics of a Histogram

• Numerical Data Representation: A histogram displays continuous data (e.g., temperature, weight, age). Continuous data can take any value within a range.

• Bars Touch Each Other: Since the data is continuous, bars have no spaces between them. This unbroken sequence visually communicates the continuity of the data.

• Focuses on Distribution: Histograms show how data is distributed across a range, making them valuable for identifying normal distributions, skewness, and bimodal patterns.

• Uses Intervals (Bins): Histograms group data into ranges (e.g., ages 0–10, 11–20, 21–30). The bin size significantly affects how the data story is told.

Key Differences Bar Graph vs Histogram - summary

Although bar graphs and histograms both use bars to represent data, they have several key differences.

How to use a bar graph

Selecting the appropriate visualization type isn't just a matter of preference—it's a critical decision that affects how accurately your audience interprets the data. Data visualization experts maintain strict guidelines about these distinctions because misrepresentation can lead to incorrect conclusions.

Use a Bar Graph When:

• You want to compare distinct categories.

• Your data represents qualitative variables (e.g., brand names, countries, customer types).

• You need a chart that is easy to interpret for a broad audience.

• You're highlighting differences between unrelated groups rather than showing a continuous spectrum.

Example:

If you want to compare the revenue of five different car manufacturers, a bar graph would be the best choice. Each manufacturer is a separate category, and their sales figures can be displayed clearly.

Best practices for Bar Graphs:

• Use clear and concise labels: Ensure X-axis labels are easy to read. If labels are lengthy, use a horizontal bar graph.

• Limit the number of bars: Too many bars create clutter. Aim for 6-8 categories for best readability.

• Apply contrasting colors: Use distinct colors for different categories, but avoid excessive color use. For more info on color contrast, check out our blog post on color use.

• Sort bars logically: Arrange bars in ascending, descending, or category-specific order to improve readability.

• Consider cognitive load: Limiting bar graphs to 5-7 categories maximizes retention and understanding.

How to use a histogram

Use a Histogram When:

• You are analyzing data distribution rather than individual values.

• Your data is continuous numerical data (e.g., age, temperature, income).

• You need to identify patterns, such as normal distribution, skewness, or outliers.

• You want to reveal the underlying shape and characteristics of your dataset.

Example:

A histogram is ideal for analyzing the height distribution of pine and oak trees in a forest. It groups tree heights into intervals, helping identify how the distribution of oak tree height differs from the distribution of pine tree height.This visualization makes it easy to see height patterns and variations across the tree population.

Best practices for Histograms:

• Choose appropriate bin sizes: Too many bins create a fragmented view, while too few bins oversimplify data.

• Ensure bars touch: Unlike bar graphs, histograms should have no gaps between bars to reflect continuous data. Many graph editors allow you to set the distance between the bars.

• Draw the bars between the X-axis labels: A histogram bar is supposed to show the frequency of a given range. The best visual indication of this is using a bar drawn between two axis labels. If this is not possible, use a single label below the bar that shows the range.

• Highlight trends using color and annotations: Use shading or arrows to emphasize peaks, gaps, or outliers.

• Use a density curve if needed: Adding a smooth curve over the histogram can help reveal trends and normal distributions.

• Maintain a consistent scale: Ensure your Y-axis starts at zero and uses equal intervals for accuracy.

• Consider bar width proportionality: If using unequal bin sizes, the area of each bar (not just the height) should be proportional to the frequency.

FAQ

1. Is it wrong, strictly speaking, to use a bar graph to display numerical ranges?

While it's not technically "wrong" to use a bar graph to display numerical ranges, it's certainly not the optimal choice and can lead to misinterpretation. The issue isn't one of correctness but of clarity and convention.

Bar graphs, by design, include spaces between bars that visually communicate categorical separation. When applied to continuous numerical data, these spaces create a visual disconnect that contradicts the continuous nature of the data. This design characteristic can lead viewers to misinterpret relationships between adjacent ranges.

For example, if you're displaying income brackets using a bar graph with categories like "$0-$25K," "$25K-$50K," etc., the spaces between bars suggest these are entirely separate groups rather than adjacent points on a continuous spectrum. A histogram, with its touching bars, more accurately conveys that someone earning $24,999 is much closer to someone earning $25,001 than the separated bars would suggest.

Additionally, data visualization has established conventions that experienced readers expect. When experts see separated bars, they automatically interpret the data as categorical. Breaking this convention without clear explanation can create unnecessary cognitive friction.

That said, there are limited scenarios where a bar graph might be used for numerical ranges—for instance, when you want to emphasize the discreteness of particular groupings for analysis purposes. However, in these cases, it's advisable to clearly indicate this deviation from convention through explicit labeling or annotations.

2. What are other options for comparing distributions?

When comparing multiple distributions, several visualization options exist, each with distinct advantages depending on your specific needs:

Violin Plots: These combine the benefits of box plots and density plots, showing both summary statistics and distribution shapes. Particularly useful when distributions aren't normal or have multiple modes.

Density Plots: Smoothed versions of histograms that show the probability density function of the distribution. These work exceptionally well for comparing multiple distributions as they eliminate the bin-edge issues of histograms and create smooth, overlapping lines that clearly show distribution shapes.

Box Plots: For comparing many distributions simultaneously, box plots provide a compact summary showing median, quartiles, and outliers. They excel at comparing central tendency and spread across multiple groups but don't show the full shape of distributions.

Overlaid Histograms: When comparing two or three distributions, semi-transparent overlaid histograms can effectively show differences. The transparency allows viewers to see where distributions overlap and diverge. However, this approach becomes cluttered with more than three distributions.

The best choice depends on your specific context:

• For comparing just 2-3 distributions in detail: overlaid histograms or density plots
• For comparing many distributions simultaneously: box plots or ridgeline plots
• For distributions with complex shapes: violin plots
• For precise quantile comparison: CDF plots

Further reading

If you're looking for a tool to easily create both bar graphs and histograms, check out ChartBuddy.io/blog, a powerful charting solution designed to make data visualization simple and effective.