What Is a Histogram?

Written by Coursera Staff • Updated on

Discover histograms and their role in visualizing data. Understand when to opt for a histogram, and learn the basics of creating one yourself. Simplify the process of using this effective data representation tool.

[Featured image] A graphic of a histogram with green bars and a black distribution line on a white background

Histograms are powerful graphical representations used to show data’s frequency distribution. Histograms make it easy to display large amounts of data in a simple model, which makes them a great choice when you want to convey the distribution and patterns of your data to a general audience. 

At its core, a histogram represents a data set by dividing the data into ranges and then representing the data count within each of these ranges or “bins.” These bins essentially act as containers, and the resulting histogram displays how many data points fall into each bin through the height of the bin. 

Let’s explore this data visualization tool in more detail, including when you should use it, how to analyze histograms, and how to build one with your own data.

When to choose a histogram 

Knowing when to choose a histogram is just as important as knowing how to make one. Visually representing your data can be a powerful tool to convey your insights to a broad audience, validate your assumptions, and create insights based on your analysis. Knowing how to choose the right type of visualization can ensure you are set up for success and make appropriate choices during your analysis process. You might decide to use a histogram for the following scenarios:

You want to look at your data distribution.

Histograms offer a clear visual representation of your data's shape and abnormalities. They assist in understanding the distribution of data values across bins, making it easier to identify outliers and gain insights into the overall distribution pattern of your data.

You want to compare data sets.

When you have multiple data sets or groups to compare, histograms provide a visual means to observe their distributions side by side. It allows for quick comparisons and helps identify variations or similarities between groups.

You want to communicate insights to a general audience.

If you need to present data and insights to a non-technical audience or stakeholders, histograms offer a user-friendly way to convey information about data distributions and patterns. For example, if you were representing when a particular venue was the busiest, a histogram could show when the highest volume of people frequented a specific location in an easy-to-understand manner.

How to analyze a histogram

When analyzing a histogram, you will want to look at a few key characteristics. The following metrics will help you understand your data set better:

  • Distribution shape: You can gain significant insights into the data by the shape of the histogram. Common shapes include symmetric (bell-shaped or normal), skewed (left or right), multimodal (several peaks), and uniform distributions.

  • Central tendency: You can understand the central tendency of the data by looking at the central peak and concentration of bars in the histogram. Both often correspond to the mean, median, and mode.

  • Spread: You can infer the spread or variability of the data from the width of the distribution. When your histogram is wider, that shows a wider variability in your data. Conversely, a narrower spread represents less variability.

  • Outliers: You can identify unusual data points, known as outliers, as any values that fall outside the typical range represented by the histogram.

Pros and cons of using histograms

Like any tool, histograms have advantages and disadvantages that can help you decide whether they are the appropriate visualization tool to use in various circumstances. 

Pros of histograms

You can find several benefits to choosing histograms for your data needs. Some common advantages include:

  • Easy to represent large volumes of data: Histograms aggregate data into “bins,” which can convert large volumes of data into an easy-to-understand visual. 

  • Simple to construct: Histograms are user friendly, allowing individuals with varying levels of statistical expertise to understand the information. 

  • Easy to explore data: Histograms are a valuable tool for exploring and getting insights from data, which can aid in hypothesis testing and decision-making.

Cons of histograms

While their simplistic nature might be a pro to some, it can be a drawback for others. When deciding if histograms are suitable for your needs, consider these disadvantages:

  • Subjective: The appearance of a histogram can vary depending on the chosen bin width, which can introduce subjectivity into data interpretation.

  • Simplistic: While histograms provide a valuable overview of data distribution, they may not capture more complex relationships between variables or distributions that are not easily categorized.

  • Group-level data: Histogram data groups into ranges, so they don’t typically provide individual-level data.

How to build your own histogram

Creating a histogram for your data is simple if you know how to set it up. To practice, start with simple data so you can see how the visualization works. Follow along with these steps:

1. Collect and organize your data set of interest. 

Your data set can represent a wide range of variables, such as exam scores, temperature readings, or product prices. For practice purposes, you can create your own data set or use online metrics. Once you have your data, organize it in descending or ascending order.

2. Determine your axes. 

This range serves as the basis for defining the boundaries for your histogram’s bins. Label the axes of your histogram with the variable of interest (such as exam scores or temperature) on one axis and numbers to represent the count on the other. 

3. Select your bin width.

Next, choose an appropriate bin width or interval size. The selection of your bin width requires thoughtful consideration because it influences the interpretation of the data. Smaller bin widths provide finer detail, while larger bin widths show a broader overview of the data distribution.

4. Count your data points. 

Once you have your bin width, you will count how many data points you should include in each bin. Doing so allows you to group data values into their respective bins and tally the frequency for each bin.

5. Create your bars on the axes.

Draw a bar for each bin on either a horizontal or a vertical axis. The width of each bar represents the bin's width, while the height of the bar shows the count that falls within the range of the bin.

Want to learn how to create a histogram with Python? Watch this video from IBM:

Learn more on Coursera

You can keep learning about data visualizations with exciting courses on Coursera. For beginners, courses like Data Visualization and Dashboarding with R or Data Visualizations with Advanced Excel can help you explore more complex ways to visualize your data while learning at your own pace.

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