Histogram vs Bar Graph: 5 Key Differences You Must Know!
Data visualization is an important part of data analysis. Charts and graphs allow us to interpret and compare data easily. Two of the most common types of charts used are histograms and bar graphs. Though they may look similar at first glance, there are some key differences between the two that you need to know.
In this article, we’ll define histograms and bar graphs, discuss their common uses and advantages, and outline the main differences between the two types of plots. Understanding the key distinctions between histogram vs bar graph will help you select the most appropriate chart for your particular data set and analysis needs.
What is a Histogram?
A histogram is a graphical display that aggregates numeric data into preset bins or ranges. It shows the frequency distribution of a continuous variable – how often values fall into various ranges. For example, a histogram may have age groups like 0-20, 20-40, 40-60, and so on on the x-axis. The y-axis would then show the number of people in each age range. Wider bars or bins indicate a higher frequency of the values in that range.
Common Uses of Histograms
Some common uses of histograms include:
1. Histograms are used to display the overall distribution shape and spread of continuous numerical data values like exam scores, customer purchase amounts, particle sizes from experiments, etc. The visual bins help identify aspects like normal distribution vs skewness, presence of gaps/clustering, position of outliers, etc, more clearly.
2. Histograms enable easy comparison between the distribution variation of a metric across two or more groups or segments. For example, test scores can be effectively analysed for distribution shape differences between males and females students through histogram examination.
3. Determining optimal bin sizes for aggregating values is key to ensuring histograms highlight the desired patterns and insights. By adjusting bin widths, attention can be focused on distribution aspects that align with analysis objectives, such as high-level shape or narrow variations.
4. Histograms help analyse the root causes behind unexpected distribution shapes or new patterns in data generated from production processes or scientific experiments. Any emerging skewness, unexpected gaps, presence of outliers, etc., can indicate technical issues or external factors that influence further investigation.
Advantages of Histograms
The advantages of the histogram are:
1. Effectively Summarizes Large Datasets
One of the biggest benefits of histograms is that they can easily represent large volumes of numeric data in a simple visual format. Rather than trying to plot and display thousands of individual data points on a graph, the histogram summarizes all the values into frequency distributions across logical bins or ranges. This makes it easy to analyze the overall pattern.
2. Highlights Distribution Shape and Spread
The histogram immediately draws attention to the shape and spread of the numeric values’ distribution. You can clearly see aspects like normal distribution, skewness, gaps, peaks, the presence and position of outliers, etc. The shape also shows whether data is symmetrical or clustered around a central value.
3. Assists Determination of Optimal Bin Sizes
A histogram’s choice of bin sizes allows you to highlight essential patterns critical for analysis objectives. Wider bins show the overall distribution, while narrower bins can reveal more minute yet relevant patterns. The flexibility of binning helps focus attention on desired aspects.
4. Enables Root Cause Analysis
Any unexpected shape, gaps, clustering, skewness, etc., evident in a histogram facilitates drilling down into factors driving that distribution imbalance or unexpected pattern. This way, histograms help identify root causes and further investigative angles.
5. Easy Identification of Trends and Exceptions
Another useful aspect of histograms is the ability to spot any deviations from the expected distribution very easily. Unexpected peaks, dips, or gaps reveal segments that warrant further analysis to understand the causes of such data behaviour.
What is a Bar Graph?
A bar graph displays categorical data using rectangular bars to show frequencies or values associated with nominal or ordinal categories.
For example, a vertical bar chart may depict population by country with bars of differing heights based on the population count. It can also show sales revenue performance over the years or across regions using horizontal bars.
Common Uses of Bar Graphs
Bar charts are commonly used for:
- Bar graphs allow easy visual comparison of metric values across discrete categories like product units sold across different regions, monthly website visitors, revenue by segments etc. The ability to contrast quantities is useful for data-backed decisions.
- They can effectively visualize categorical data that is nominal like survey response percentages, market share distribution across brands, student grade allocations etc. The different bar lengths cater to analyzing non-numeric data splits.
- Bar charts help trace performance trends over chronological periods for important business metrics like sales growth, profitability, customer acquisitions etc. year-over-year or month-over-month. Spotting ups and downs is simpler.
- Sudden bar spikes or short bars on bar graphs attract attention to data points that are outliers from the norm or exceptions to expected patterns. This helps direct focus to investigating the reasons behind anomalies.
Advantages of Bar Graphs
1. Facilitates Quick Visual Comparison
The core benefit of bar graphs is enabling fast and intuitive relative comparisons between discrete categories or time periods. The ability to quickly contrast performance metrics across products, regions, campaigns, etc., is useful for data-driven decisions.
2. Easy to Comprehend Format
A bar graph is one of the most fundamental and commonly used data visualization formats. The simplicity of rectangular bars representing values rather than points and complex axes means even less statistically inclined audiences can interpret insights easily.
3. Space Efficient Layout
When visualizing categorical data across multiple items or groups, a bar chart offers a compact way to represent all the categories on the X-axis while plotting the respective values through bar height. This saves space while listing all relevant categories.
4. Flexibility in Orientation
Bar graphs offer flexibility to toggle between horizontal and vertical orientations. Vertically aligned bars help compare items or categories at a point, while horizontal alignment enables tracing trends of a single item across periods or categories.
5. Draws Attention to Exceptions
Variations in bar lengths owing to high or low values allow easy identification of exceptions, controversies or areas warranting attention for positive or negative reasons. The visual extremes steer the focus to relevant aspects.
Key Differences Between Histograms and Bar Graphs
Here are the fundamental differences between histogram vs bar graph:
Definition and Purpose
Histograms show the distribution of continuous data using ranges or bins. The objective is to visualize the distribution shape, outliers, etc. Bar graphs display individual values of categorical/nominal data. The objective is to compare values across items or categories.
Data Type Representation
Histograms work exclusively with numerical data that spans a wide range of values. Bar charts are used when data is non-numeric (ordinal categories or names). Values are discrete for every column.
Structure and Appearance
Histogram bars are adjacent to reflect a grouping of data ranges. Depending on bin sizes, bars can be of differing widths. Bar graphs are separate with gaps in between. All columns are usually equal widths placed separately along the axis.
Axis Interpretation
The histogram’s x-axis shows numerical ranges like ages, scores, etc. The Y-axis plots frequencies. On the other hand, the bar chart’s x-axis has descriptive categories like countries, products, etc. The difference between bar graphs and histograms explains that both have discrete values.
Use Cases and Applications
Histograms help examine the distributions, patterns and causes behind process variability. Bar charts enable comparison between categories or over time. They help contrast performance, etc.
Conclusion
Histograms and bar charts make data analysis easier through visual representation. Now that you know the vital differences in their working, you can make an informed choice between the two. Histograms work well to assess distribution patterns in continuous data sets. Bar charts enable easy comparisons across discrete categories or time periods. The objectives behind your data visualization will help determine which chart types better serve that need.
