## Introduction

**Histograms** are a useful tool in data visualization because they show, at a glance, the distribution of values within a given range. By plotting the frequency of each value on the x-axis and the relative number of those values on the y-axis, histograms allow for quick identification of *clusters, gaps and trends*.

Histograms can be easily generated in many software applications and when created correctly can provide meaningful insights into your data. This guide will describe how to draw a histogram using **Microsoft Excel** and offers some *tips for best practices* when working with your data.

## What is a Histogram?

**A histogram** is a chart that shows the frequency of data within a given range. Data can be grouped into different intervals or “bins” and the frequency of each bin can be represented by height on the chart. It is a useful tool for visualizing and summarizing large datasets, and it is frequently used in business, engineering, and financial analysis.

Histograms are very similar in structure to bar graphs. Both graphs display categories on one axis and a numerical value on the other axis, but while bar graphs take categorical data (such as class grades or measurements) as input, histograms take continuous data (such as test scores or prices).

Histograms are usually used to analyze variables from one sample or population, such as:

- Distribution of ages in a company
- Sales over a certain period
- Hourly temperature readings
- Test scores of students
- Lengths of different tracts of land

A histogram provides information about the shape, central tendency, and spread of data by displaying the following:

- The
**number or frequency**of individuals or observations that fall into each bin - The
**distribution range** - The
**mean**(average) value - The
**median**(the middle point when numbers are arranged from smallest to largest) **Mode**(the most common observation)

## Steps to Draw a Histogram

**Creating a histogram** can be a great way to visualize data. A histogram can show you the *distribution or frequency of values* in a dataset. It is a powerful tool for getting insights from the data.

In this article, we will give you the steps on **how to draw a histogram**. We will walk you through each step, from gathering the data to plotting the histogram. Let’s get started:

- Gather the data
- Organize the data
- Determine the bin size
- Draw the histogram

### Gather the data

In order to draw a histogram, you first need to gather the data that you want to represent in the graph. This could mean counting the frequency of values in a set or taking a survey of people’s opinions. You can use existing data, such as from a published dataset or research article, or collect your own data by surveying people, observing behavior or tests and experiments.

Once you have gathered your data, make sure you set aside some time to **organize it**. You want to be able to visually represent the different types of information with your histogram and if your data is not organized properly it will be difficult to interpret your results accurately. It is important that each item within a set of data is assigned its own value – this makes it easy for us to identify the trends and similarities that exist between sets.

Organizing the data will involve ordering them along an axis such as numerical order (if using numbers) or alphabetically (if using words). Additionally, include any **units of measurement** you used when collecting the data because they will make it easier for those viewing the histogram to understand what each piece of information stands for when placed together next on one axis. Once your data is organized and ready for use, you can move onto the next step which is creating **bins** in which each point on our graph will be placed under.

### Determine the number of classes

Before you can complete your histogram, you need to determine the number of classes you will use on the x-axis. Rather than trying to fit all of the data points into a manageable chart, break up the x-axis into **subgroups**. You may have to adjust your class number if the data looks too cluttered when your first attempt is completed.

You will also need to decide on an interval or range for each class. The interval size should be consistent throughout the chart and potential intervals might include:

- one-unit (
*1,2,3*) - two-unit (
*2-4, 4-6*) - five unit (
*5-10*) - ten unit (
*10 – 20*)

Once you decide on an interval size, draw out and label classes in agreement with your data’s lowest and highest values on a graph paper grid before continuing with other steps.

### Find the class limits

Drawing a histogram starts with the data that has already been collected and organized. **Class limits** represent the upper and lower values of each group called a class interval, which is commonly referred to as bins. The class limit must include all possible values, such as those past the highest and lowest values of your data set, so that no value is left out.

To determine the upper and lower class limits, start by taking the *minimum value* in your data set and subtract **0.5** from it; this will be the lower class limit of your first category or bin. Then take that number and add the size of the bin—the size is determined by you—and add **0.5** to it; this will give you our upper class limit for that bin. Take that upper class limit from our previous step and repeat it twice more (subtracting **0.5** from it for a new lower class limit then adding **0.5** for an upper). This same process should be repeated for all categories or bins until you have covered all portions of your data set including any outliers beyond min/max. This will ensure all numbers in your data are included in a bin or category on the chart so nothing is left out.

### Find the class width

The first step in creating a histogram is to determine the **class width**, which is the difference between the upper and lower boundaries of the groups, or classes. This will provide an indication of how precise your data should be. For example, if you have survey data broken down into two categories – *yes and no* – then you would use a class width of 1 (no different than a standard bar graph). On the other hand, if you are analyzing information that comes with decimal points or decimal numbers, then you might want to use a more specific class width such as **0.1, 0.2 or 0.5**.

It’s also important to consider how many classes you will need in order to effectively illustrate your data. Generally speaking, it’s best to begin with **six classes** that are evenly distributed across your data set – too few classes and your information won’t be accurately depicted; too many classes and it becomes hard to interpret what’s actually happening in your graph.

Once you have determined the appropriate class width for your histogram and decided on how many classes you need, it’s time to select appropriate **upper and lower limits** for each class so that all of your values can be grouped effectively into their respective areas (bars). Upper limits should include the *highest value from each set*; lower limits should include the *lowest value from each set* – this ensures that all of your data points will always fall within their respective bounds.

### Draw the histogram

When you are ready to draw the histogram, you need to follow some simple steps:

**Gather your data**– Collect all of the data from a survey, experiment, or other research method that you would like to represent with a histogram. It’s important to make sure your data is organized and accurate before proceeding.**Draw the axes**– On a graph paper or blank sheet of paper, draw two axes that intersect at a 90-degree angle in order for the graph to be properly scaled and readable. Label your axes and indicate parameters such as quantity minimums and maximums. Make sure that your axes correctly reflect the range of values in your data set so they will fit into their specified spaces.**Plot frequencies**– After inserting all of the required marks on each axis (usually along the major units), begin plotting relative frequency along the vertical axis. This can be done by simply counting how many times an element appears in your dataset – plotting this relative frequency against its corresponding x value will create one bar on the histogram.**Fill bars/highlight intervals**– Continue graphing as described above until you have successfully plotted all elements from your data set – now take time adding colors (to help identify different frequencies) or shading bars depending on their frequency; this gives viewers an easier understanding of where most of the measurements fall in comparison to others and provides better overall readability for those exploring it further down the road.

## Conclusion

Once the data has been analyzed and organized, the next step is to create a **histogram**. A histogram uses vertical bars of different heights to represent data and can be used to illustrate the distribution and frequency of different values within a population. The key elements necessary to draw a histogram include *class width, class boundaries, frequencies per class, and total frequencies*.

To construct a histogram effectively, determine the size of the intervals or classes by evaluating how many observations are present in each bin or group. A good rule of thumb is that classes should not have fewer than five observations but no more than 15. After determining the sizes for each interval or class, tally up the frequency of each observation within each interval or class. Then use this frequency data to draw a bar graph using the interval’s boundaries as X-axis labels. Finally, when drawing your graph add titles for both axes (the X-axis should be titled **‘Classes’**) as well as a title for your entire chart.

After completing these steps you will have constructed an accurate and informative histogram that allows for easy analysis of your data set.