Skip to content

What is a Histogram and When to Use It

In this article, you will learn all about the Histogram chart.

What Does It Look Like?

A histogram looks similar to a bar or column chart, but there are a few main differences.

The first is that there is usually no gap between the bars of a histogram. Typically, the bars are only separated by a thin light colored border.

The second main difference is that the category axis contains numerical ranges rather than nominal categories.

For example, instead of listing the names of regions or people, you find a histogram showing ranges from one number to another.

How Do You Read a Histogram?

So now you know what a histogram looks like, but what do the bars and numbers mean? How do you read a chart like this?

Take a look at the histogram below for reference.

A histogram counts the number of times a referenced value falls within a specified range. This histogram above references a list of one-hundred unique customers and their ages.

The histogram counts how many customers fall within the age range of 20 to 30 and displays this number with the first bar shown in the chart.

The rest of the bars calculate the same way.

  • The second bar shows how many customers fall within the age range of 30 to 40.
  • The third bar shows how many customers fall within the age range of 40 to 50.
  • And so on and so forth.

As a result, you can see how many customers fall within each age range. Looking at the histogram, you can see that most customers are between the age range of 30 to 40, while only a few customers are between the ages of 70 to 80.

Histograms give you a clear picture of the distribution of values within your data.

When Do You Use a Histogram?

At this point, you understand that a histogram shows you the distribution of your numerical data. In other words, you can see how many values fall within each specified range.

But when or why should you how this type of information in the first place?

I’m sure many different scenarios call for a histogram, but I will give you two main instances to focus on. This way, we can keep things simple.

The first instance is rather simple. Create a histogram whenever you are specifically interested in seeing the data distribution.

For example, if you are interested in how many customers fall within specific age ranges, then create a histogram to show it.

The second scenario is not so obvious.

It’s a good idea to create a histogram to show the distribution whenever you calculate the average for a dataset.

But why?

Let’s say you have two sets of customers, and the average age for both datasets is 46.

Although the average age is the same, these two numbers can mean two very different things. To see what I mean, take a look a the two histograms below.

Now that you can see the distribution of the two datasets, you have a much better understanding of the two average numbers.

You can see the first average is equal to 46 because most of the customers are around that age.

However, the second average is only equal to 46 because half of the customers are much younger and half are much older.

Without the histograms and seeing the distribution of data, it would have been impossible to gain this understanding of the calculated averages.

So whenever you calculate the average for a dataset, think about including a histogram to go with it.

This Post Has 4 Comments

  1. This blog post was so helpful! When I learned about histograms in school, I think we were just taught that they were bar graphs with no gaps in-between the bars, and that’s it! After reading this, I feel like I finally understand what a histogram is. Thank you!

Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Back To Top