Histograms and bar graphs are visual representations that data analysts, business professionals, and researchers use to uncover complex patterns and trends among dataset variables. Users rely on data visualization tools to create histogram and bar graph charts. Their similar appearance belies their differences.
What is the difference between histograms and bar graphs?
A histogram shows the frequency distribution of numerical or quantitative data, and a bar graph or bar chart compares categorical or discrete variables. Histograms and bar graphs differ based on data representation, analysis styles, and bar shapes.
Histograms display continuous data such as temperature, income levels, age, height, weight, time, and rainfall amount. Bar graphs are ideal for presenting discrete data, such as company market shares, product sales, demographic data, student grade ranges, and employee productivity levels.
The table below represents some of the most notable differences between histograms and bar graphs.
| |
Histogram |
Bar graph |
| Definition |
A graphical representation that divides continuous data into intervals to represent the data distribution within a numerical dataset |
A graphical visualization in which each bar represents the frequency of discrete data among different categories |
| Represents |
Continuous or non-discrete data |
Categorical or discrete data |
| X-axis |
Reveals pre-defined intervals or bins among continuous variables in a dataset |
Shows all discrete categories present in a dataset
|
| Y-axis |
Plots frequency of continuous data points within each pre-defined bin |
Represents the frequency of discrete data in each category within a dataset |
| Space between bars |
Absent because the bars are adjacent to each other
|
Present as all bars must have equal spacing among them for easy comparison |
| Bar width |
Varies depending on data distribution |
Remains equal for all bars in a bar graph |
| Examples |
Temperature, income levels, age, height and weight, time |
Company market shares, product sales, demographic data, student grades |
Ideal for
|
Visually indicating data distribution, dispersion, and central tendencies in a dataset |
Analyzing trends and patterns based on data frequencies across categories |
| Applicable for |
Exploratory data analysis and hypothesis testing |
Market research, social science studies, business analytics |
Explore how data visualization translates raw data into appealing visual insights.
