What Type of Data Is Best for a Histogram?


Heather Bennett

When it comes to visualizing data, histograms are a powerful tool that can provide valuable insights. A histogram is a graphical representation of the distribution of a dataset.

But what type of data is best suited for a histogram? Let’s explore different scenarios where histograms can be effectively utilized.

Continuous Data

Continuous data is best visualized using histograms. This type of data includes measurements that can take on any value within a certain range.

For example, if you’re analyzing the heights of individuals, the height values can range from very short to very tall. By dividing the range into intervals called bins and plotting the frequency or count of observations in each bin, you get a clear picture of how the data is distributed.


Let’s say you collected data on the amount of time it takes for customers to complete a task on your website. You have recorded times ranging from 10 seconds to 60 seconds. By creating a histogram with appropriately chosen bins (e.g., 0-10 seconds, 10-20 seconds, and so on), you can observe which time intervals are most common and identify any patterns or outliers.

Categorical Data

Categorical data, on the other hand, is better suited for bar charts rather than histograms. Categorical variables represent discrete values that fall into specific categories or groups. Each category has its own frequency count or percentage representation.


Suppose you’re analyzing survey responses where participants rated their satisfaction levels with your product on a scale from 1 to 5 (1 being highly dissatisfied and 5 being highly satisfied). Here, you would create a bar chart with categories representing each rating (1, 2, 3, 4, and 5) on the x-axis and the frequency or percentage of respondents who chose each rating on the y-axis. This visual representation helps you understand the distribution of satisfaction levels among your customers.

Discrete Data

Discrete data is best represented using bar charts or histograms, depending on the nature of the data. Discrete variables can only take specific values, often integers or whole numbers.


Imagine you’re analyzing the number of products sold by a company in a month. The sales data consists of whole numbers (e., 0, 1, 2, 3) representing the number of items sold. By creating a histogram with bins representing each possible value, you can visualize how frequently each value occurs and detect any patterns or outliers in the sales distribution.


In conclusion, histograms are best suited for visualizing continuous data where intervals or bins can be defined to represent ranges. Categorical data is better represented using bar charts where each category represents a discrete value.

Discrete data can be effectively visualized using either histograms or bar charts depending on specific circumstances. By choosing the appropriate visualization technique based on your dataset’s characteristics and goals, you can gain valuable insights and make informed decisions.

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