Bar graph vs. histogram: How to distinguish and use them correctly

Data visualization is a powerful tool for interpreting complex information, and two commonly used methods are bar graphs and histograms. While they may look similar at first glance, they serve different purposes and convey different types of information. Understanding the distinctions between these chart types is essential for correctly analyzing and interpreting data. In this guide, we’ll explore the fundamental differences between bar graphs and histograms, when to use each type, and how to read and interpret them effectively. We’ll also provide practical examples using Highcharts to implement both visualization types in your projects.

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Understanding bar graphs

Bar graphs (also called bar charts) represent categorical data with rectangular bars whose heights or lengths are proportional to the values they represent. Each bar typically corresponds to a specific category, making bar graphs excellent for comparing discrete, unrelated groups or categories.

Bar graphs have several distinctive characteristics:

Categorical data representation: Bar graphs display data across different categories, such as countries, products, or time periods treated as separate entities.
Discrete comparison: The bars represent discrete values, allowing for direct comparison between different categories.
Spacing between bars: There is typically spacing between the bars to emphasize that they represent distinct categories.
Versatility: Bar graphs can display single sets of data or multiple sets (grouped or stacked) for more complex comparisons.

When to use bar graphs

Scenario	Example
Comparing categorical values	Sales figures across different regions or countries
Displaying survey results	Customer satisfaction ratings where respondents select from predefined options
Tracking categorical changes over time	Quarterly sales figures over several years
Ranking data	Countries ranked by GDP or companies by market capitalization

How to read a bar graph

Step	Description
Identify the axes	In vertical bar graphs, categories are on the x-axis and values on the y-axis. In horizontal bar graphs, this is flipped.
Check the scale	Note whether the value axis starts at zero or another value, as this affects visual comparisons.
Compare the bars	Taller or longer bars represent higher values. Direct visual comparison shows which categories have higher or lower values.
Look for patterns	Analyze for significant differences between categories, outliers, or unexpected values.

Understanding histograms

Unlike bar graphs, histograms are specifically designed to show the distribution of a continuous variable. They group data into bins (intervals) and display the frequency or count of observations that fall within each bin. Histograms are powerful tools for understanding the shape, central tendency, and spread of a data distribution.

Histograms have several unique features:

Continuous data representation: Histograms display the distribution of a single continuous variable, showing how data points are spread across a range of values.
Adjacent bars: The bars in a histogram are typically adjacent to each other without gaps, reflecting the continuous nature of the data.
Equal width bins: In most histograms, the bins have equal width, though variable-width bins can be used in specific cases.
Distribution insights: Histograms reveal patterns in data distribution, such as normal (bell curve), skewed, bimodal, or uniform distributions.

When to use histograms

Scenario	Example
Analyzing continuous data distribution	Heights, weights, test scores, or measurement values
Identifying outliers and data spread	Finding unusual patterns or values that deviate from the main distribution
Comparing actual vs. theoretical distributions	Determining if manufacturing outputs follow expected normal distributions
Understanding central tendency and variability	Visualizing where most data points are concentrated and how widely they’re spread

How to read a histogram

Step	Description
Identify the variable and bins	Determine what continuous variable is being measured and how data is divided into bins or intervals.
Examine the shape	Look for patterns like normal (bell-shaped), uniform, skewed, or bimodal (two peaks) distributions.
Locate central tendency	The highest bars show where most data points are concentrated, indicating the mean, median, or mode.
Assess the spread	The width of the distribution indicates data variability. Wider spread suggests greater variability.
Consider bin width	Bin width affects histogram appearance. Wider bins smooth distributions; narrower bins show more detail.

Key differences between bar graphs and histograms

Now that we’ve explored both chart types individually, let’s directly compare them:

Data type: Bar graphs represent categorical data (discrete categories), while histograms represent the distribution of a continuous variable.

Bar spacing: Bar graphs typically have spaces between bars to emphasize distinct categories. Histograms have adjacent bars without spaces, reflecting continuous data.

X-axis interpretation: In a bar graph, the x-axis represents different categories with no inherent numerical relationship. In a histogram, the x-axis represents the range of a continuous variable divided into bins.

Purpose: Bar graphs are primarily used for comparing values across different categories. Histograms are used to understand the distribution of a single continuous variable.

Creating a bar chart with Highcharts

This bar chart creates a horizontal bar chart comparing the population of the five most populated countries. The horizontal orientation makes it easy to read country names, and data labels show the exact population values on each bar. The tooltip provides additional context by appending “millions” to the values when hovering over bars.

Let’s look at how to implement this basic bar graph using Highcharts.

1. Load the required files and create a container to hold the chart

<script src="https://code.highcharts.com/highcharts.js"></script>
<script src="https://code.highcharts.com/modules/exporting.js"></script>
<script src="https://code.highcharts.com/modules/export-data.js"></script>
<script src="https://code.highcharts.com/modules/accessibility.js"></script>

<figure class="highcharts-figure">
    <div id="container"></div>
    <p class="highcharts-description">
        Bar chart showing the population of the top 5 most populated countries.
        This chart demonstrates the use of a categorical axis on the y-axis.
    </p>
</figure>

2. Add some CSS to control the dimensions of the container

.highcharts-figure {
    min-width: 310px;
    max-width: 800px;
    margin: 1em auto;
}

#container {
    height: 400px;
}

3. Implement the JavaScript

Highcharts.chart('container', {
    chart: {
        type: 'bar'
    },
    title: {
        text: 'Population by Country',
        align: 'left'
    },
    subtitle: {
        text: 'Source: World Population Review',
        align: 'left'
    },
    xAxis: {
        categories: ['India', 'China', 'United States', 'Indonesia', 'Pakistan'],
        title: {
            text: null
        },
        gridLineWidth: 1,
        lineWidth: 0
    },
    yAxis: {
        min: 0,
        title: {
            text: 'Population (millions)',
            align: 'high'
        },
        labels: {
            overflow: 'justify'
        },
        gridLineWidth: 0
    },
    tooltip: {
        valueSuffix: ' millions'
    },
    plotOptions: {
        bar: {
            borderRadius: '50%',
            dataLabels: {
                enabled: true
            },
            groupPadding: 0.1
        }
    },
    legend: {
        layout: 'vertical',
        align: 'right',
        verticalAlign: 'top',
        x: -40,
        y: 80,
        floating: true,
        borderWidth: 1,
        backgroundColor:
            Highcharts.defaultOptions.legend.backgroundColor || '#FFFFFF',
        shadow: true
    },
    credits: {
        enabled: false
    },
    series: [{
        name: 'Population',
        data: [1417.2, 1412.2, 334.9, 275.5, 235.8]
    }]
});

Creating a histogram with Highcharts

This histogram shows the distribution of student heights. The chart displays both the raw data points as a scatter plot and a histogram showing the frequency distribution of heights grouped into bins. The histogram automatically divides the data into appropriate bins, and the bars are adjacent to each other, reflecting the continuous nature of the height data. The dual axes support both the scatter plot (raw data) and the histogram view.

Now, let’s examine how to create this histogram using Highcharts to visualize the distribution of a continuous dataset.

1. Load the required files and create a container to hold the chart

<script src="https://code.highcharts.com/highcharts.js"></script>
<script src="https://code.highcharts.com/modules/histogram-bellcurve.js"></script>
<script src="https://code.highcharts.com/modules/exporting.js"></script>
<script src="https://code.highcharts.com/modules/export-data.js"></script>
<script src="https://code.highcharts.com/modules/accessibility.js"></script>

<figure class="highcharts-figure">
    <div id="container"></div>
    <p class="highcharts-description">
        Demonstrating a histogram with a bell curve computed automatically.
        This chart shows the distribution of heights from a sample of students,
        with a bell curve showing the normal data distribution.
    </p>
</figure>

2. Add some CSS to control the dimensions of the container

.highcharts-figure {
    min-width: 310px;
    max-width: 800px;
    margin: 1em auto;
}

#container {
    height: 400px;
}

3. Implement the JavaScript

Highcharts.chart('container', {
    title: {
        text: 'Distribution of Student Heights',
        align: 'left'
    },
    xAxis: [{
        title: { text: 'Height (cm)' },
        alignTicks: false
    }, {
        title: { text: 'Histogram' },
        alignTicks: false,
        opposite: true
    }],

    yAxis: [{
        title: { text: 'Data' }
    }, {
        title: { text: 'Histogram' },
        opposite: true
    }],

    plotOptions: {
        histogram: {
            accessibility: {
                point: {
                    valueDescriptionFormat: '{index}. {point.x:.3f} to {point.x2:.3f}, {point.y}.'
                }
            }
        }
    },

    series: [{
        name: 'Height',
        type: 'histogram',
        xAxis: 1,
        yAxis: 1,
        baseSeries: 's1',
        zIndex: -1
    }, {
        name: 'Data',
        type: 'scatter',
        data: [163, 167, 170, 172, 173, 174, 175, 175, 176, 177, 177, 178, 178, 179, 180, 181, 182, 183, 184, 185, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194],
        id: 's1',
        marker: {
            radius: 1.5
        }
    }]
});

Choosing appropriate bin widths for histograms

Selecting the optimal bin width for a histogram is crucial as it significantly impacts how data patterns are revealed or obscured. Too few bins (wide width) can oversimplify the distribution and hide important details, while too many bins (narrow width) can create excessive noise and make patterns difficult to discern. Several methods can guide this decision: the Sturges’ formula (k = 1 + 3.322 log₁₀n, where n is sample size) provides a traditional starting point; the Freedman-Diaconis rule (bin width = 2 × IQR × n⁻¹/³) accounts for data variability and outliers; and Scott’s rule (bin width = 3.5 × σ × n⁻¹/³) works well for normally distributed data. For practical implementations, consider your audience and purpose—data exploration might benefit from interactive tools that allow users to adjust bin widths dynamically. Many visualization libraries, including Highcharts, offer automatic bin width calculation, but understanding these principles enables you to override defaults when necessary to better highlight the specific aspects of your data distribution that matter most.

Common mistakes when interpreting charts

While both chart types are valuable visualization tools, there are several common mistakes that can lead to misinterpretation:

Confusing bar graphs with histograms: Remember that bar graphs compare distinct categories, while histograms show the distribution of a continuous variable.

Misinterpreting the axes: In bar graphs, the category axis has no numerical interpretation, while in histograms, the x-axis represents ranges of a continuous variable.

Ignoring the scale: If the value axis doesn’t start at zero, the visual differences between bars can be exaggerated.

Overlooking bin width in histograms: The choice of bin width can significantly affect the appearance of a histogram and may hide or reveal important patterns.

Assuming correlation implies causation: Observed patterns do not necessarily imply causation. Additional analysis is often needed to establish causal relationships.

Advanced techniques

Once you’re comfortable with basic charts, you can explore more advanced techniques:

Grouped and stacked bar graphs: For comparing multiple variables across categories, grouped bars (side by side) or stacked bars (on top of each other) provide more complex insights.

Error bars: Adding error bars to a bar graph can indicate the variability or uncertainty in the data, especially useful in scientific analyses.

Overlaid distributions: In histograms, overlaying multiple distributions can facilitate direct comparison between different datasets or groups.

Kernel density estimation: Adding a smooth KDE curve to a histogram can help visualize the underlying probability distribution.

Interactive features: Implementing interactive elements like zooming, filtering, or hovering for additional information can enhance user experience and data exploration.

Conclusion

Bar graphs and histograms are fundamental data visualization tools with distinct purposes. Bar graphs excel at comparing values across different categories, making them ideal for discrete, categorical data. Histograms are specialized for visualizing the distribution of continuous variables, revealing patterns about the data’s shape and spread.

Understanding the differences between these chart types is crucial for selecting the appropriate visualization for your data and correctly interpreting the results. By correctly applying these techniques using Highcharts, you can create clear, informative, and visually appealing representations of your data.

Whether you’re analyzing survey responses, comparing sales figures, or examining test scores, mastering the use and interpretation of these visualization tools will enhance your ability to extract meaningful insights from your data and communicate those insights effectively.