When written down in text, even the most intriguing statistical data can seem dense or dry. Therefore, many communicators and educators make use of graphs. These are powerful tools that help people better comprehend and visualize large volumes of data. There are various kinds of graphs available today, but bar graphs and histograms are much more popular than other types. This is because both of them come with several unique advantages.

In this article, I will provide an overview of bar graphs and histograms with examples. I will also help you understand certain important differences between the two of them.

**Bar graphs**

Bar graphs are used to represent data visually by using bars of varying lengths or heights. You can graph data either horizontally or vertically and enable viewers to compare various values and arrive at conclusions with ease and speed. Usually, a bar graph features an axis, label, bars, and scales. These represent different measurable values like percentages or amounts. You can use bar graphs to display all types of data – everything from job growth and yearly sales to crop yields and seasonal rainfall.

The bars on a bar graph are often colored identically, but occasionally different colors are used to differentiate between categories or groups to help viewers read and interpret the data better. Bar graphs come with two axes – a labeled X-axis (horizontal axis) and Y-axis (vertical axis). When you graph experimental data, you must graph the independent variable on the X-axis and the dependent variable on the Y-axis.

**Types of bar graphs**

Depending on the kind and complexity of the data they represent, bar graphs can take various forms. Simple bar graphs can consist of as little as two bars, such as graphs that represent the net profits of two rival companies. With the increasing complexity of the data, the graph tends to follow suit. It may even take the form of a stacked bar graph or a clustered or grouped bar graph.

Let us take a closer look at each of these types of bar graphs.

**Single bar graphs**

Single bar graphs display the discrete value of the item for every category you see on the opposing axis. For example, let’s consider a representation of the number of females in grades 10-12 for each of the years 2015 to 2020. You can represent the discrete value or actual number by a bar sized to scale and display the scale on the X-axis. On the other hand, the Y-axis will show the corresponding years. The longest bar on the graph will represent the year from 2015 to 2020 with the greatest number of females in grade 10, and vice versa.

**Grouped or clustered bar graphs**

Grouped or clustered bar graphs represent discrete values for multiple items that share the same category. Considering the above example, you can easily modify the graph by adding another value that features the number of males in grades 10-12. You can then group together the bars representing each gender by year, and color code them to clarify which bars represent the male and female values. With the help of this grouped bar graph, viewers will be able to easily compare the number of students enrolled in grades 10-12 both by gender and by year.

**Stacked bar graphs**

Stacked bar graphs have every bar divided into subparts representing the discrete values for items that form a portion of the entire group. In the above examples, students in grades 10-12 have been grouped together and represented by a single bar. You can divide this bar into subsections to represent the proportion of students in every grade. Like in the case of grouped bar graphs, you must use color-coding to make the graph readable.

**Benefits of bar graphs**

Given below are some important benefits of using bar graphs:

- You can use a bar chart with categorical or digital data.
- Bar graphs show every type of data in a frequency distribution.
- They include relative numbers or multi-category proportions.
- They allow you to sum up a wide collection of data visually.
- You can use them to estimate key values at a glance.
- They show outline proportions or close numbers.
- You can explain patterns better using wide visual data instead of tables.

**Downsides of bar graphs**

Despite their advantages, there are certain cons of bar graphs as well. I have listed them below.

- A bar diagram shows only the frequencies of the elements of a data set.
- Often, you will find yourself needing more details than what a bar graph has to offer.
- Bar graphs don’t reveal important conclusions, implications, trends, or triggers.

**Histograms**

Like bar graphs, a histogram or histograph is a kind of graph that finds widespread applications in statistics. Histograms help viewers visually interpret numerical data by displaying the number of data points that lie within a particular range of values. These ranges of values are known as bins or classes. Histograms also use a bar to display the frequency of the data that falls in every class. Higher bars indicate a greater frequency of data values in that bin, and vice versa.

A histogram is a specific bar graph-like representation of data that shows a range of outcomes into columns along the x-axis. The y-axis represents the number count or percentage of occurrences in the data for each column and can be used to reflect data distributions.

**An example of a histogram**

Let’s say you flip four coins and record the results. By using the appropriate binomial distribution table or directly calculating with the binomial formula, you discover that the probabilities of certain results are as follows:

- No heads are showing – 1/16
- One head is showing – 4/16
- Two heads are showing – 6/16
- Three heads are showing – 4/16
- Four heads are showing – 1/16

Using these results, you can construct a total of five classes, each of width one. These classes correspond to the number of possible heads – zero, one, two, three, and four. Right above each class, proceed to draw a vertical bar or rectangle. The heights of these bars correspond to the aforementioned probabilities mentioned for the experiment you performed.

**Histograms and probabilities**

The example I mentioned above demonstrates both the construction of a histogram and the fact that we can actually represent discrete probability distributions using one. In order to construct a histogram that represents a probability distribution, you must first select the appropriate classes. These should be outcomes obtained from a probability experiment, and every class should be one unit wide. The heights of the histogram’s bars represent the probabilities for each of these outcomes. In a histogram constructed in this manner, the areas of the bars indicate probabilities as well.

Because we can obtain probabilities from a histogram of this kind, there are two important conditions involved. The first condition is that we can only use nonnegative numbers for the scale that gives us the height of a particular bar of the histogram. The second condition is that because the probability equals the area, the areas of all the bars must add up to a total of one – equivalent to 100%.

**Other applications of histograms**

A histogram’s bars don’t necessarily have to represent probabilities alone. There are many other areas where histograms can come in handy. You can use a histogram to represent your data set whenever you want to compare the frequency of occurrence of quantitative data. These charts especially come in handy when the data available is in extremely huge ranges. For example, you can use them while conducting a survey of college students who park their vehicles outside the campus.

**Benefits of histograms**

Histograms have a number of unique benefits, which have been listed below:

- They make it easier to display data of various types and frequencies.
- They help us visualize the distribution of data easily.
- We can find out the median, distribution, and variations in data using a histogram.
- Histograms inform us about the skewness of the plotted data.
- We can also use histograms to predict the future performance of the process in question.
- As the intervals are evenly distributed, they are very consistent.
- They are useful in calculating the standard deviation of data.
- They are reader-friendly and easy to read and understand.

**Downsides of histograms**

There are a few cons associated with histograms, which I have mentioned below:

- We can only use continuous data while plotting a histogram.
- They aren’t handy for comparing two different kinds of data.
- As data is always categorized or grouped, its exact value isn’t used for plotting.
- The exact input of a histogram can’t be obtained from the graph unless plotted in a frequency distribution.
- People can easily manipulate them to support the desired result.
- It is easy to neglect the time difference in data while plotting a histogram.
- They aren’t suitable for comparing several different categories of data together.

**Histograms vs Bar graphs**

Also see: Types of Graphs and Charts

**At first glance, you will undoubtedly feel that histograms and bar graphs share a lot of similarities.** Both of them are charts that use vertical bars to represent data. The height of a bar indicates the relative frequency of the amount of data in the class. Therefore, the higher the bar, the higher the frequency of the data will be. Similarly, a lower bar indicates a lower frequency of the data. However, this is where the similarities between histograms and bar graphs end.

**The critical differences between histograms and bar graphs are related to the level of measurement of the data.**

We generally use bar graphs for data at the most nominal level of measurement. For example, we use them to measure the frequency of categorical data, and the classes for a bar graph are these categories. On the other hand, we use histograms for data at least at the ordinal level of measurement. Several ranges of values comprise the classes for a histogram.

A bar graph represents the relationship between two different variables, whereas a histogram represents only a single, continuous variable. The ordering of the bars is another crucial difference between bar graphs and histograms. In a bar graph, we usually rearrange the bars in order of decreasing height. However, you cannot mix the bars in a histogram. Instead, you must display them in the order that the classes occur.

**Conclusion**

In this article, I explained what bar graphs and histograms are and the key similarities and differences between them. Both of these charts play a significant role in the field of statistics. We use bar graphs to compare categories while histograms provide quantitative analysis, whereby data points are grouped into distinct intervals. Thus, we can use a bar chart to summarize a large amount of data in visual form and predict future process performance using a histogram.

Although both histograms and bar graphs have the horizontal X-axis and vertical Y-axis, they differ in terms of displaying the data. Bar charts have spaces because of the comparison of independent variables. On the other hand, histograms have connected bars because of the presentation of continuous dependent data. Thus, bar charts are ideal for categorized data, while histograms are better for constant numerical information.