Histogram vs. Bar Graph: What’s the Difference?

As corporations worldwide gather more data about their businesses and customers at an increasing rate, the ability to showcase these large data sets effectively to identify trends or relationships becomes paramount. Data visualizations allow you to tell the story behind your data by uncovering patterns and identifying outliers in complex data sets. They provide actionable insights and allow businesses to make timely, data-driven decisions.

Two important data visualizations to know are histograms and bar graphs, which look similar but differ in a few key ways. Read on to learn the definition and potential uses of each and their associated advantages and disadvantages. Understanding this information allows you to recognize their differences, such as the type of data utilized in each, their specific purposes, their formats, and how to order your information on each one.

What is a histogram?

Histograms show the number of instances of a particular factor or variable that fall within a certain range. Like bar graphs, histograms organize data by creating groups based on logical ranges. Within a group, each bar’s height correlates to the number of data points that fall within the range of the group. No gap exists between the bars displayed on the graph to show the numerical nature of histograms.

For example, if you wanted to show your town's average temperature each day in a calendar year, you would have your data grouped by 10 degrees at a time, 40 to 50 degrees, 50 to 60 degrees, 60 to 70 degrees, and so on. If a certain day averaged 62 degrees, this data point becomes added to the 60- to 70-degree grouping. The groupings of degrees appear on the x-axis of your graph, and the frequency of days within each grouping goes on the y-axis. Based on the height of each bar, you can read how many days fall within each temperature grouping and how they compare to one another.

Histograms showcase continuous data, which is a form of quantitative data consisting of any possible number. These numbers may have decimal points and represent a precise and accurate number. Additionally, continuous data may include the changes in measurement through multiple time periods rather than at one exact moment. An example of continuous data is recording each runner's time to cross the finish line during a race. Their finishing time can be an infinite number of exact numbers and include a decimal point. Another strong example is tracking the temperature outside each day.

What are histograms used for?

This data visualization offers an excellent option to showcase the distribution of a data set. A histogram shows you if your data set features any outliers and if a skew exists. Histograms effectively organize large data sets and allow you to comprehend trends within your data quickly because you can compare the frequency of data points in each grouping and observe the overall distribution. Organizations may use histograms to improve their decision-making processes.

Examples of the uses of histograms include businesses analyzing the rating each customer gives their products or services to determine how satisfied their customer base is. A cafe may create a histogram to determine the hours it receives the highest volume of traffic in its store to inform potential customers when it is busiest throughout the day. Investors and traders frequently utilize the moving average convergence divergence (MACD) histogram to help them receive buy or sell signals and track momentum.

Advantages of histograms

The advantages of histograms as a data visualization option include:

• Easy to read and create

• Applicable to many contexts and situations

• Able to handle large data sets

• Effectively displays distribution

• Showcases trends and outliers

Disadvantages of histograms

The disadvantages of histograms as a data visualization option include:

• Does not have the ability to compare more than one set of data at a time

• Too few or too many groupings may cloud the information shown in your graph

• Can be an over-simplification of data

What is a bar graph?

Bar graphs are a common data visualization used to compare a data set. The set-up of a bar graph allows a viewer to easily determine what groupings of data are bigger and by what quantity. Bar graphs feature categorical variables, meaning your data splits into multiple groupings. Each group of data receives its own bar on your bar graph.

For example, if you wanted to show the number of people in your class who choose chocolate, vanilla, or strawberry as their favorite ice cream flavor, each flavor would get its own bar. A distinguishing characteristic of bar graphs is that the bars don’t overlap or touch each other. A gap exists between each individual bar.

Bar graphs exclusively feature discrete data, not continuous data. This is one of the key differences between bar graphs and histograms. You can count discrete data in a defined period, which is not divisible. Unlike continuous data, this data can also be quantitative or qualitative.

An essential strategy for distinguishing discrete and continuous data is to count discrete data and measure continuous data. Another way to think of this is by time period. Discrete data pertains to a defined event or time period, and continuous data shows how data changes over time in multiple periods. Counting the number of people attending a particular baseball game or the number of products purchased from your store daily are examples of discrete data.

Various types of bar graphs exist for you to use depending on what data you intend to show. The common varieties of bar graphs include:

• Vertical

• Horizontal

• Clustered

• Stacked

• 100 percent stacked

• Waterfall

What are bar graphs used for?

Bar graphs are useful visualizations when graphing or analyzing categorical data. They may show percentages or numbers based on the particular data set used. The length of each bar within the chart represents how large a value is, creating an easy comparison for your audience. Bar graphs may be a great choice in any situation where you must divide data into multiple groupings.

Bar graphs have applications in a wide range of fields and occupations, such as tracking investments, creating forecasts, preparing a budget, and monitoring sales. For example, a small business owner may use a bar graph to track the revenue they receive from each product they sell on a monthly basis. A clothing store can create bar graphs to analyze the number of people who enter their store daily.

Advantages of bar graphs

As a data visualization, bar graphs have the following advantages:

• Simple design

• Easily comprehended by a variety of audiences

• Users can quickly compare data by the height of each bar

• Clearly highlights the key trends in a set of data

• Provides a clear, visual summary for potentially large quantities of data

Disadvantages of bar graphs

Bar graphs have the following disadvantages or drawbacks as a data visualization:

• May not provide a full explanation of patterns or trends in the data

• Viewers may misunderstand the information displayed

• Excessive numbers of bars could cause the chart to become overcrowded

• Confusion may result from failing to properly sort your data

• The causation of a trend or pattern in a set of data may not be evident

Getting started on Coursera

To explore histograms, bar graphs, or other visualizations in data analysis, check out Data Visualization, offered by the University of Illinois. This course teaches the fundamentals of data visualization, including the various uses for different types of visualizations, how to depict relationships, and how to turn large datasets into a dashboard through four modules.

Another relevant course worth checking out is IBM's Data Analysis and Visualization Foundations Specialization. With this four-course series, you can gain an in-depth understanding of basic data analysis and visualization skills used by successful data analysts today. You also have the ability to gain experience with performing data analysis with common tools used in the field, such as utilizing Microsoft Excel spreadsheets for analytics, building dashboards on Cognos, and more.