Data visualization is an important part of data analysis. Charts and graphs allow us to visually represent data to spot trends, patterns, and outliers. Two of the most common types of charts used are bar graphs and histograms. At first glance, they may look similar, but some key differences set them apart.
In this article, we will explain what bar graphs and histograms are, provide examples of where each is commonly used, highlight the advantages of each, and outline the six main differences between bar graph vs histogram.
What is a Bar Graph?
A bar graph displays categorical data using rectangular bars to show comparisons between categories of data. Bar graphs compare the magnitude of change between categories and illustrate the size of one category relative to the others.
Advantages of Using Bar Graphs
- Allow easy visual comparison between categories: Bar graphs make it simple to assess categories against each other visually. The rectangular bars instantly showcase size relationships, making it straightforward to spot which categories are larger or smaller.
- Clearly illustrate magnitude and size relationships: The lengths of the rectangular bars plainly depict the magnitude and size relationships between categories. Through bar height analysis alone, you can instantly recognize which category is the largest, next largest, middle-sized, smallest, and so on.
- Intuitive display suited for categorical data: Humans intuitively understand lengths as representing size and magnitude. Leveraging rectangular bars to indicate counts and percentages within people-friendly categories creates an intuitive feeling chart perfect for categorical data analysis.
- Grab viewer attention through comparisons: Bar graphs shine when it comes to pulling viewer attention through categorical comparisons. The instantly recognizable elongated rectangles draw the eye to the action of mentally comparing between categories.
- Convey insights through visual inspection: Since numerical calculations are not required to compare bar heights to realize insights, bar chart analysis happens primarily through visual inspection. This enables faster comprehension of categorical size relationships.
What is a Histogram?
A histogram shows the frequency distribution of numeric data divided into intervals called bins. It illustrates how often each interval or bin of values occurs in the dataset.
Advantages of Using Histograms
- Reveal the shape of data distribution: Histograms instantly showcase the shape and spread of a data distribution. Peaks, valleys, gaps, and outliers are visually highlighted through the frequency aggregation into bins.
- Visualize concentration and variability: The histogram bars visually communicate where data points concentrate, reveal gaps in the distribution, and display variability/dispersion patterns across numeric ranges.
- Identify central tendency: Shaped histogram distributions show characteristics like the mean, median, and mode that stand out, enabling intuitive identification of data set centers and skewing.
- Facilitate quantitative analysis: The frequency-based display enables quantitative analysis of distribution patterns, not just visual comparison. Statistical insights integrate cleanly with histograms.
- Simplify complex data: Fitting many data points into understandable frequency ranges helps simplify the interpretation of highly variable or noisy numerical data sets by highlighting meaningful patterns.
Key Differences Between Bar Graphs and Histograms
A. Data Type
Bar graphs are designed for categorical data, which places each data point into a category. Some examples of categorical data include gender, political affiliation, country of origin, product type, etc. The bars in a bar graph correspond to the count or percentage for each discrete category.
On the other hand, histograms visualize numerical data, where the data points are measurements that can be described numerically. Some examples include age, height, test scores, income level, etc. Since the data is numeric instead of placed in distinct categories, histograms focus more on the frequency distribution and patterns within the measured data ranges rather than counts within categories.
B. Bar Spacing
In a bar graph, the spacing between the rectangular bars is equal. This is because a bar graph presents data for distinct categories that do not represent numerical ranges. No relationship exists between one category and the next, so even spacing visually separates the categories.
However, in a histogram, the width of the bars and the spacing between them is proportional to the numerical ranges that make up the underlying data. Wider bars and bar spacing correspond to larger differences between the defined bin ranges for that data distribution. The bars and spacing reflect the relationship between the numeric values rather than being evenly spaced indicators of unrelated categories.
C. Representation
Since bar graphs depict categorical data, the height of each rectangular bar represents the count or percentage within each discrete category. Comparing bar heights allows viewers to see how category sizes relate but does not indicate anything about underlying data distribution.
Histograms rely on dividing numerical values into “bins” or ranges at set intervals. The rectangular bars then represent the frequency of values occurring within each binned numeric range rather than counts of pre-existing categories. So, while bar graphs represent category counts, histograms depict frequencies of values within a distribution curve for numeric measurements.
D. Axis Labeling
For bar graphs, the x-axis is labeled with the categories being compared, while the y-axis represents the measurement scale for either counts or percentages of those categories.
Histograms label the x-axis with the numeric value ranges into which values are aggregated through the binning process. The y-axis denotes the frequency scale—how often values in the dataset occur within those numeric bins along the distribution.
E. Frequency Distribution
Bar charts do not aim to show any inherent distribution to the data categories. No data frequencies or patterns are being visualized. The focus is only on comparing the count and magnitudes across the chosen categories.
However, the main goal of a histogram is visualizing the frequency distribution. The bars aggregated based on value ranges display how often each measurement range occurs to reveal peaks, variability, shape, and any outliers in the distribution of the numeric data. This allows conclusions about patterns within the data rather than just seeing category sizes.
F. Usage Context
Due to only visualizing categorical data counts without any distribution, bar charts enable comparing across discrete categories and gauging the relative magnitude of those categories. The comparison itself drives any conclusions.
Histograms serve analytical purposes of viewing frequency distributions for numeric data sets. Shapes, peaks, spread, gaps, and outliers reveal insights about the variability, concentration, and central tendency attributes that characterize the data. Quantitative analysis of patterns, not just comparisons, drives conclusions with histograms.
Practical Implications of Choosing the Right Graph
The intended analytical purpose should determine whether a bar graph or histogram is appropriate for your data:
Bar graphs excel at making categorical comparisons. Choosing a histogram when you have discrete categories would not utilize the full potential of visualizing frequency distributions.
Histograms help reveal insights about data variability, shape, and outliers that bar charts cannot provide. Using a simple bar graph when you have meaningful numerical data would only depict some available data intelligence.
Therefore, bar graphs are built for categorical data and histograms for depicting numeric distributions. Using the wrong graph would lead to less impactful data visualization and could fail to bring out key data insights. Consider the data type and end analytical goals when deciding between these two graphical techniques.
Conclusion
Bar graphs and histograms are used for varying purposes. Bar graphs present categorical data for comparing across data categories, while histograms visualize numeric data distributions. Key distinguishing factors between bar graphs vs. histograms include allowed data types, bar spacing approaches, representation of frequencies vs counts, axis scales used, conveying distribution shapes, and overall analytical context. Being aware of these differences will allow you to select the graph best suited for the story your data has to tell.