Back to blog

December 11, 2025

Bar Graph vs Histogram

December 11, 2025

Bar graphs and histograms look similar but serve different purposes. This guide clarifies the key differences and when to use each - with practical examples and visual comparisons.

Bar ChartCategorical Data45A72B58C35DgapAny MetricOrder: flexibleHistogramContinuous Distribution0255075100no gaps between barsFrequencyContinuous rangeOrder: fixed

What is a Bar Graph?

A bar graph (or bar chart) displays categorical data using rectangular bars. Learn more about bar graphs. See all graph types. Each bar represents a distinct category, and its height (or width) shows the value for that category. The spaces between bars emphasize that categories are separate and unrelated.

Key Characteristics

A B C

Categorical Data

Shows discrete categories like product types, regions, or demographic groups. Each category is independent.

Separated Bars

Spaces between bars visually communicate that categories are distinct and don't form a continuous sequence.

Flexible Orientation

Can be vertical (column chart) or horizontal. Horizontal works better for long category names.

$ % #

Flexible Metrics

Values can represent frequency, percentage, revenue, or any measurable metric relevant to your categories.

What is a Histogram?

A histogram displays continuous numerical data by grouping values into ranges (called bins) and showing how frequently values fall into each range. Unlike bar graphs, histograms reveal patterns, distributions, and outliers in your dataset.

Key Characteristics

0 50 100 any value

Continuous Data

Displays numerical data that can take any value within a range (temperature, weight, age, time).

Touching Bars

Bars have no spaces between them, visually representing the continuous nature of the data.

Distribution Focus

Shows how data is distributed across a range, revealing patterns like normal distribution or skewness.

0-10 10-20 20-30

Uses Bins

Groups data into intervals (e.g., ages 0-10, 11-20). Bin size significantly affects the visualization.

Key Differences: Bar Graph vs Histogram

Although both use bars to represent data, bar graphs and histograms have fundamental differences that affect when and how you should use them:

Feature Bar Graph Histogram
Data Type Categorical Numerical (Continuous)
Bar Spacing Bars are separated Bars touch each other
X-Axis Categories (e.g., brands, countries) Intervals (e.g., age ranges)
Purpose Compare different groups Show data distribution
Example Use Comparing sales of different products Analyzing test score distributions

When to Use a Bar Graph

Choosing the right visualization isn't just preference - it's critical for accurate interpretation. Data visualization experts maintain strict guidelines because the wrong choice can lead to misleading conclusions.

Use a Bar Graph When:

  • You want to compare distinct categories (not continuous ranges)
  • Your data represents qualitative variables (brand names, countries, customer types)
  • You need a chart that's easy to interpret for a broad audience
  • You're highlighting differences between unrelated groups rather than showing a continuous spectrum

Example: Comparing Car Manufacturer Revenue

If you want to compare the revenue of five different car manufacturers, a bar graph is the perfect choice. Each manufacturer is a separate category with no inherent relationship to the others, making separated bars the ideal visual representation.

Bar graph example comparing car manufacturer revenue by brand
Bar graph comparing car manufacturer revenue by brand

Best Practices for Bar Graphs

A B C

Clear Labels

Use concise X-axis labels. For lengthy labels, switch to horizontal orientation.

Limit Categories

Aim for 6-8 categories maximum to avoid clutter and maintain readability.

Contrasting Colors

Use distinct colors for different categories. Check our guide on color use.

Logical Sorting

Arrange bars by value (ascending/descending) or logical category order for easier comparison.

When to Use a Histogram

Use a Histogram When:

  • You're analyzing data distribution rather than comparing individual values
  • Your data is continuous numerical (age, temperature, income, time)
  • You need to identify patterns like normal distribution, skewness, or outliers
  • You want to reveal the underlying shape and characteristics of your dataset

Example: Tree Height Distribution

A histogram is ideal for analyzing the height distribution of pine and oak trees in a forest. By grouping tree heights into intervals, you can easily compare distributions and identify patterns - like whether oak trees tend to be taller than pine trees, or if height distributions are similar.

Histogram example showing tree height distribution for pine and oak trees
Histogram of tree height distribution for pine and oak trees

Best Practices for Histograms

Choose Appropriate Bin Sizes

Too many bins create fragmentation; too few oversimplify. Experiment to find the right balance.

Ensure Bars Touch

Histogram bars should have no gaps to reflect continuous data. Set bar distance to zero.

0 10 20 30

Label Ranges Clearly

Draw bars between X-axis labels to show ranges visually. Use labels like "0-10" if needed.

Highlight Trends

Use shading, color, or annotations to emphasize peaks, gaps, or outliers.

0 5 10

Maintain Consistent Scale

Y-axis should start at zero with equal intervals to ensure accurate visual representation.

Frequently Asked Questions

Is it wrong to use a bar graph to display numerical ranges?

While not technically "wrong," it's suboptimal and can lead to misinterpretation. The issue is clarity, not correctness.

Bar graphs include spaces between bars that visually communicate categorical separation. When applied to continuous numerical data, these spaces contradict the continuous nature of the data. For example, displaying income brackets like "$0-$25K" and "$25K-$50K" with separated bars suggests these are entirely different groups, when in reality someone earning $24,999 is much closer to someone earning $25,001 than the visual spacing implies.

Data visualization has established conventions that experienced readers expect. When they see separated bars, they interpret categorical data. Breaking this convention creates unnecessary cognitive friction.

Bottom line: Use histograms for numerical ranges. If you must use a bar graph, clearly label your deviation from convention with explicit annotations.

What are other options for comparing distributions?

When comparing multiple distributions, several visualization options exist beyond histograms:

Violin Plots

Combine box plots and density plots, showing both summary statistics and distribution shapes.

Density Plots

Smoothed versions of histograms showing probability density.

Box Plots

Compact summaries showing median, quartiles, and outliers.

Overlaid Histograms

Semi-transparent overlapping histograms for 2-3 distributions.

Choose based on context: For 2-3 distributions use overlaid histograms or density plots; for many distributions use box plots; for complex shapes use violin plots.

How do I choose the right bin size for a histogram?

Bin size significantly impacts how your histogram looks and what patterns it reveals. There's no single "correct" bin size - it depends on your data and goals.

Too few bins: Oversimplifies the data, hiding important patterns and variations.

Too many bins: Creates noise and makes it hard to see the overall distribution shape.

Just right: Reveals the true shape of your data while remaining easy to interpret.

The square root of your sample size is a common starting point (e.g., 100 data points = ~10 bins). Sturges' formula and the Freedman-Diaconis rule are more sophisticated approaches.

Best practice: Experiment with different bin sizes and choose the one that best reveals the story in your data without overfitting noise.

What about stacked and grouped bar charts?

Stacked and grouped bar charts are variations of bar graphs that allow you to show additional dimensions of categorical data:

Stacked Bar Charts

Segments within each bar show sub-categories. Good for showing part-to-whole relationships and total values.

Grouped Bar Charts

Bars placed side by side for each category. Better for comparing individual values across sub-categories.

Both remain categorical visualizations - they're still bar graphs, just with added complexity. Don't confuse them with histograms.

Why do some histograms have gaps between bars?

If you see gaps in what's labeled as a histogram, it's likely one of these situations:

  • Software default: Some charting tools add gaps by default. In Excel and Google Sheets, set "gap width" to 0%. For a comparison of charting tools in Google Slides, see Think-cell vs Chartbuddy vs Vizzlo.
  • Empty bins: No data points fell into certain ranges. This is legitimate and reveals important information about your data.
  • Misclassified chart: It might actually be a bar graph mislabeled as a histogram.
  • Discrete data: When showing counts of discrete integers, small gaps can help distinguish individual values.

For true continuous data, histogram bars should touch unless there are genuinely empty bins in your data range.

Create professional bar graphs in Google Slides - Install Chartbuddy for free.

Get Started for Free

No credit card required · Chrome & Edge