5 Key Differences Between Bar Charts and Histograms: When and Why to Use Each!
When analyzing data, two of the most common visual displays are bar charts and histograms. At first glance, they may seem very similar—both use vertical bars to represent frequencies. However, bar charts and histograms are used to depict different data types in distinct ways. Their unique structures, spacing, axes, and quantitative or categorical emphases suit different analytic purposes.
Understanding when to use a bar chart versus a histogram leads to more impactful data visualization. This article will answer your question to what is the difference between a bar chart and a histogram.
What is a Bar Chart?
A bar chart is a visual display of data using rectangular bars of different heights. The bars represent different categories of data, and the height of each bar represents the quantity or frequency of items in that category. Bar charts allow viewers to grasp comparisons across categories quickly. They work exceptionally well when comparing nominal or ordinal categories that have numbers associated with them.
Examples of Bar Charts
Here are two examples of data presented in bar chart form:
Sales Revenue by Region
This bar chart compares company sales performance across geographic regions. The height of each bar represents the total revenue for that region. Viewers can easily see which regions generate the highest and lowest revenue.
Movie Popularity by Genre
This bar chart displays the number of movie releases in 2020 by genre. The entertainment genre released the most movies, while documentaries had the fewest. The varying bar heights make these genre differences easy to visualize.
Benefits of Bar Graph
1. Quick Visual Comparison
Bar charts allow viewers to compare differences across categories rapidly. The simple visual design makes relationships very clear.
2. Display a Variety of Data
While bar heights show frequency or quantity, the bars can represent other statistics, too, like averages, percentages, sums, or precise measured values.
3. Easy to Understand
The visual format spells out category differences clearly for a broad audience. Simple glances show higher and lower, more and less, biggest and smallest.
4. Flexible and Customizable
Bar charts work for small or very large data sets across any categories. Visuals can be customized through colors, layouts, labels, and styling.
5. Reveal Trends Well
When updated with new data points, bars make it easy to spot positive and negative trends across categories over time. The simplest visual cues show growth, decline, and variability.
6. Facilitate Data-Driven Decisions
The straightforward comparisons bar charts enable allow leaders and teams to make data-backed decisions on category performance they might otherwise miss.
What is a Histogram?
A histogram displays the distribution of numerical data within equal intervals. It groups numbers into ranges or bins and shows how many counts fall into each bin using the height of each bar.
Unlike bar charts, which compare categorical data, histograms show continuous quantitative data that can take any numeric value within a range. The columns are placed side-by-side with no gaps between them.
Histograms reveal information about the shape and spread of data that bar charts do not show. They illuminate clustering and help determine central tendency measures like mean, median, and mode.
Examples of Histograms
Here are two examples of data distribution presented in histogram form:
Exam Score Distribution
This histogram divides exam scores into letter grade bins from F to A. The height of each column shows how many students scored within that grade range. Most scores fall in the B and C ranges.
Height Distribution in a Class
This histogram groups students’ heights into 5-cm intervals. The tallest column shows the most common height range is 160-165 cm. The histogram shape indicates a symmetrical, bell-shaped distribution around the average height.
Benefits of Hitogram
1. Reveal Distribution Shape
The column widths and heights clearly showcase distribution shapes like normal, bimodal, skewed, etc. This aids analysis.
2. Identify Outliers
Histogram spreads make it easy to pinpoint outlier high or low values isolated at fringes of distributions. These atypical points warrant attention.
3. Find Central Tendency
Visual inspection highlights means, modes and other central tendencies essential for summarizing masses of data simply.
4. Uncover Clusters
Histograms make clusters and gaps sticking out through visual examination straightforward – no complex methods required.
5. Assess Spread and Variability
One glance at histogram spans enables overall variability assessments across data sets using visual intuition alone.
6. Enable Statistical Modeling
Shapes shown provide assumption checks for many statistical models, aiding proper model selection and results validity confirmation.
Key Differences Between Bar Charts and Histograms
Difference 1: Data Type
Bar charts are used to compare categorical data that can be grouped into qualitative categories, such as product types, age groups, colors, genres, or geographic regions. Each bar represents one distinct category, and the height of the bar corresponds to the frequency or quantity of items falling into that group.
Histograms, however, display distributions of quantitative data measured on a numerical scale, like exam scores, income ranges, or product dimensions. The columns of a histogram divide the complete range of observed values into equal-width intervals or bins. Column height shows how many counts from the full data set landed within each interval. Histograms bring light patterns and shape characteristics across an underlying measurement continuum that bar charts do not reveal.
Difference 2: Axis Interpretation
For bar charts, the vertical axis represents the amount related to items in each qualitative group—whether a raw count, percentage, average, or any metric the creator chooses to compare across categories.
The vertical axis on a histogram has a special meaning—it always represents frequency counts for the number of data observations located within defined measurement limits. Comparing column heights on this axis allows viewers to understand overall data distribution tendencies and centers.
Difference 3: Bar Spacing
Every qualitative category receives its own bar in bar charts, separated from other categories by equal gaps. This spacing clarifies that the groups are discrete elements that do not bleed into one another. For example, a bar chart comparing sales by world region would have individual bars for Asia, Europe, Africa, Australia, and the Americas spaced apart.
With histograms, there is no spacing between adjacent columns since they reflect an underlying continuity of possible measurement values. Dividing distributions into range intervals helps reveal shapes and patterns more clearly. Removing spacing emphasises that a histogram shows part of an ongoing distribution rather than distinct groups.
Difference 4: Frequency Representation
Bar chart bars can represent absolute frequencies, relative frequencies like percentages, or even pure quantities unconnected to frequencies. For example, a bar chart could show raw sales numbers, market share percentages, average order values, or highest order values across regions.
Histogram columns must always display absolute or relative frequencies showing how many data observations fall within each defined measurement interval. Comparing frequencies reveals distribution shapes that other metrics could mask.
Difference 5: Purpose and Use Cases
The main purpose of bar charts is to compare amounts related to distinct categorical groups. They enable statements like “Category A has a bigger share than Category B.” Histograms describe distribution shapes, centers, and spreads within a single variable’s range. Assessments based on histograms include “The distribution is right-skewed” or “The median is higher than the mean.”
Bar chart users leverage them to contrast performance across qualitative groupings like age brackets, country of origin, or product names. Histogram users employ them to pinpoint central tendencies and patterns within a single numerical variable like IQ scores, pollution levels, or customer purchase frequency. The appropriate choice depends on analysis goals and available data characteristics.
Conclusion
Bar charts present discrete side-by-side categorical comparisons, while histograms display continuous quantitative value distributions across equal incremental groupings. Their different data types, axis meanings, bar spacings, frequency emphases, and applications make them complementary tools for analyzing data rather than interchangeable options. Consider what you want the visualization to communicate when deciding between using a bar chart or histogram.
