Do you know the difference between a bar graph and a histogram? If you think they’re just different names for the same thing, think again.

Many people are confused between what is a bar graph and what is a histogram. This can lead to misinterpretation of data and incorrect conclusions.

For instance, using a bar graph instead of a histogram can hide important information about the distribution of data, which can be critical for making informed decisions.

Hence, understanding the** **bar graph vs histogram differences is critical for anyone working with data, whether you’re a data analyst, a marketer, or just a curious person who loves numbers.

In this article, we will explore the key differences between bar graphs and histograms, including their definitions, characteristics, and use cases.

By the end of this article, you will have a clear understanding of bar graph vs histogram uses, and how to create them in your reporting software. Let’s get started!

Table of Contents

Toggle**What Is A Bar Graph**?

A bar graph is a type of chart that displays data using rectangular bars, where the length or height of each bar represents the value of the data.

Bar graphs are commonly used to compare discrete categories or groups, and are often used to display data in a simple and easy-to-understand way.

They are particularly useful for visualizing data that is not continuous, such as data that is measured in distinct categories or time periods.

**Types Of Bar Graphs**

There are several types of bar graphs, each suited for different types of data and purposes. Some of these include:

### Vertical Bar Graph:

This is the most common type of bar graph, where the bars are placed vertically along the y-axis.

### Horizontal Bar Graph:

The bars are placed horizontally along the x-axis.

It is used when you have long labels or categories that can be difficult to display vertically.

### Stacked Bar Graph:

The bars are divided into multiple segments, each representing a different category or sub-category of data.

It is used to show how the total amount is divided into different parts.

### Clustered Bar Graph:

Multiple bars are grouped for each category, making it easy to compare data across different groups.

### 100% Stacked Bar Graph:

Each bar represents the relative proportion of data within a category. It is useful for showing the percentage of data in each category.

Choosing the right type of bar graph depends on the kind of data you have and the insights you want to communicate.

## Characteristics of Bar Graphs

Bar graphs have several characteristics that make them useful for displaying certain types of data:

### Bars:

The bars in a bar graph can be either vertical or horizontal. Each bar represents a category or group of data, and the length or height of the bar corresponds to the value of the data.

### Axis:

Bar graphs typically have two axes that intersect at a right angle:

- The vertical y-axis (displays the values of the data)
- The horizontal x-axis (displays the categories or groups being compared)

### Scale:

The scale of a bar graph is determined by the range of the data being displayed.

The scale should be chosen carefully to ensure that the bars are proportional to the values of the data and that the graph is not misleading.

### Labels:

Bar graphs should always include clear labels for both the x-axis and the y-axis, as well as a title that accurately describes the data being displayed.

### Colors:

Colors can enhance bar graphs to help distinguish between different categories or groups of data.

However, it’s important to use colors sparingly to avoid overwhelming or misleading the viewer.

## What Is A Histogram?

Histograms are chart types that display the distribution of numerical data by dividing the data into intervals, or “bins,” and plotting the number of observations that fall into each bin.

They are useful for displaying continuous data, such as height, weight, or age, and can provide insights into the shape, central tendency, and spread of the data.

Histograms are a powerful tool for analyzing and visualizing numerical data and are commonly used in fields such as statistics, data science, and economics.

## Types Of Histograms

There are several types of histograms, each with its strengths and weaknesses.

The different types of Histograms are mentioned below.

### Simple Histogram:

The most basic type of histogram is where the frequency of data within each bin is represented by the height of the bars.

The bars are usually drawn adjacent to each other with no gap between them.

### Grouped Histogram:

It displays two or more sets of data on the same chart, with each set of data represented by a different color or pattern.

It is used for comparing the distributions of different data sets side-by-side.

### Cumulative Histogram:

It displays the cumulative frequency distribution of the data, where the height of each bar represents the number of observations that fall within the bin and all previous bins.

It is used for visualizing the proportion of observations that fall below a certain value.

### Frequency Polygon:

Instead of drawing bars, the data points are connected by a line.

It is used for displaying trends in the data, as well as comparing multiple data sets on the same chart.

### Kernel Density Plot:

It estimates the probability density function of the data.

It is used for displaying the shape of the data distribution, as well as identifying patterns and trends that may not be visible in a simple histogram.

The choice of which type to use depends on the type of data being analyzed and the insights that need to be communicated.

## Characteristics of Histogram

It is important to understand the characteristics of a histogram to accurately represent the data being analyzed and provide useful insights into the patterns and trends within it.

### Shape:

It can identify the nature of the distribution, such as whether it is symmetric, skewed, bimodal, or has other characteristics.

### Bins:

The number and width of the bins used in a histogram can affect the appearance of the chart and the insights that can be gained from it.

Choosing appropriate bin sizes is important for ensuring that the chart accurately represents the data.

### Range:

The range of the data being analyzed is important for determining the x-axis limits of the histogram, which should be selected to best represent the data without distorting the chart.

### Scale:

The scale of the y-axis can also affect the appearance of the histogram and the insights that can be gained from it.

Using an appropriate scale can help highlight data patterns and trends.

### Central Tendency:

They identify the central tendency of the data, such as the mean, median, or mode.

This can provide insights into the typical value of the data and the degree of variation around this value.

**Bar Graph Vs Histogram**

While both use bars to represent the data, there are key differences in the type of data they are used to represent and how they are constructed.

Here are some common **bar graph vs histogram** differences.

### Difference Use Case

Understanding the specific use cases for bar graphs and histograms is important for choosing the right chart type for your data and effectively communicating your insights to others.

**Bar Graphs:**

Here are some use cases of bar graphs.

**Comparing discrete values:**

They compare discrete values, such as the number of products sold by a company in a given year or the number of students in different grade levels.

**Showing trends over time:**

They show trends over time, such as the growth or decline of a particular industry or market.

**Comparing between groups:**

They can effectively compare values between different groups, such as the sales figures for different products or the performance of different teams.

**Histograms:**

Here are some use cases of histograms.

**Analyzing numerical data:**

They analyze numerical data, such as the ages of customers or the frequency of website visits.

**Identifying distribution patterns:**

They identify the distribution of the data being analyzed, such as whether it is symmetric, skewed, or bimodal.

**Identifying outliers:**

They identify outliers, which are observations that fall far outside the range of the majority of the data.

This can provide important insights into the nature of the data and any anomalies that need to be further investigated.

**4. Different Types Of Data**

It’s important to note that while both bar graphs and histograms use bars to represent data, the type of data being represented is typically different.

Here are the different types of data used in a bar graph vs histogram:

**Bar Graphs:**

**Categorical data:**

They represent categorical data, such as the number of customers who prefer a certain brand of product or the amount of revenue generated by different regions.

**Non-numeric data:**

They represent non-numeric data, such as the number of students enrolled in different academic programs or the percentage of a population that identifies with a certain political party.

**Histograms:**

**Continuous numerical data:**

They represent continuous numerical data, such as the height of a population or the time taken to complete a task.

**Discrete numerical data:**

They represent discrete numerical data, such as the number of cars sold in a month or the number of complaints received by a company.

**Bar Graph Vs Histogram**– Different Formatting Rules

Understanding the different formatting rules for bar graphs and histograms is important for creating effective and accurate visual representations of your data.

Here are some key formatting differences between the two.

**Reordering Bars Capabilities:**

**Bar Graph**

Bar reordering is possible. It can change the order in which bars appear. It can emphasize certain values or makes the graph more visually appealing.

It is important to ensure that the order is logical and makes sense based on the data being represented.

**Histogram**

Bars are ordered based on the range of values they represent with no capability to reorder them.

**Spacing Between Bars:**

**Bar Graph**

Spacing between bars can be adjusted to change the visual impact of the graph.

For example, increasing the spacing between bars can make the graph appear less cluttered while decreasing the spacing can emphasize the differences between values.

**Histogram**

The spacing between bars is typically consistent and is based on the range of values being represented.

Adjusting the spacing between bars in a histogram is not recommended, as it can distort the representation of the data.

**Bar Width Rule:**

**Bar Graph**

The width of the bars can be adjusted to change the visual impact of the graph.

For example, increasing the width of the bars can make the graph appear more substantial while decreasing the width can make the differences between values more apparent.

**Histogram**

The width of the bars is typically consistent and is based on the range of values being represented.

## What Is The Best Histogram And **Bar Graph Maker **For Your Needs?

DotNetReport is a great histogram or bar graph maker software that can help you with all kinds of data visualizations.

DotNet Report is an ad hoc reporting engine that enables users to produce meaningful reports themselves, without needing the assistance of developers or IT.

Some of DotNet Report’s top features include charts and dashboards, a report scheduler, multi-tenant client support, drill-down reports, and built-in filters.

DotNet Report’s user-friendly interface and powerful report creation tools make generating reports a smooth process.

Businesses can experience the benefits of DotNet Report by taking a live demo to see how it can revolutionize their reporting process.

## Conclusion

In conclusion, bar graphs and histograms are two commonly used tools for visualizing data.

While they share some similarities, such as using bars to represent data, they also have distinct differences that make them suitable for different purposes.

Bar graphs are typically used to compare discrete categories or show changes over time, while histograms are used to show the distribution of continuous data.

Understanding the bar graph vs histogram characteristics, formatting rules, and limitations can help researchers and analysts choose the best representation for their data and draw accurate conclusions from their analysis.

## FAQs

### What are the similarities between a bar graph and histogram?

Both bar graphs and histograms are used to visually represent data. They both use bars to represent the data, with the height or length of the bars representing the values being measured. Both graphs are used to identify trends, patterns, and relationships in data.

### What are three limitations of a histogram?

- Histograms are highly dependent on the binning or grouping of the data. If the bins are too large, important information may be lost, while too small bins can result in noisy data.
- Histograms do not show individual data points and do not provide a complete picture of the data. They only provide information on the distribution of data within certain ranges.
- Histograms are limited to one variable at a time and cannot display multiple variables on the same graph.

### What is one disadvantage of a histogram?

One disadvantage of a histogram is that it can be difficult to choose appropriate bin sizes that accurately represent the data without oversimplifying or obscuring important information.

Choosing the wrong bin size can result in a misleading representation of the data.