What Type of Data Is Used to Construct a Histogram?


Scott Campbell

When it comes to constructing a histogram, it is important to understand the type of data that is used. A histogram is a graphical representation of data that allows us to visualize the distribution of a dataset. It consists of bars where the height represents the frequency or relative frequency of each interval or bin.

Types of Data

Before we dive into the specifics of what type of data is used to construct a histogram, let’s first understand the different types of data:

  • Nominal Data: This type of data consists of categories or labels without any specific order or ranking. Examples include colors, names, and categories.
  • Ordinal Data: This type of data has categories with a specific order or ranking.

    Examples include ratings, survey responses (e.g., Likert scale), and educational levels (e., elementary, high school, college).

  • Interval Data: Interval data not only has an order but also has equal intervals between values. However, there is no true zero point. Examples include temperature measured in Celsius or Fahrenheit and years (e., 2000, 2010).
  • Ratio Data: Ratio data has all the properties of interval data along with a true zero point. This means that ratios are meaningful. Examples include weight, height, time in seconds.

Data for Histograms

In most cases, histograms are constructed using either interval or ratio data due to their quantitative nature. These types of data allow us to calculate frequencies and create meaningful intervals for our histogram.

To construct a histogram using interval or ratio data, follow these steps:

Step 1: Collect and Organize your Data

Start by collecting the data you want to analyze. Once you have your dataset, organize it in ascending or descending order depending on your preference. This step is crucial as it sets the foundation for creating intervals.

Step 2: Determine the Number of Intervals

Next, decide on the number of intervals you want to divide your data into. The number of intervals depends on factors such as the size of your dataset and desired level of detail in your histogram. A general rule of thumb is to use between 5 and 15 intervals, but feel free to adjust this based on your specific needs.

Step 3: Calculate Interval Width

To calculate the width of each interval, subtract the minimum value from the maximum value and divide it by the number of intervals determined in Step 2. Round up or down to ensure a whole number. This will help create evenly sized intervals.

Step 4: Create Intervals

Using the interval width calculated in Step 3, create your intervals by starting with the minimum value and adding the interval width successively until you reach the maximum value. Each interval represents a range of values that will be used for constructing bars in our histogram.

Step 5: Count Frequencies

To construct a histogram, we need to count how many values fall within each interval. Scan through your dataset and count how many values fall within each interval range created in Step 4. Keep track of these frequencies as they will determine the height of each bar in our histogram.


In conclusion, histograms are constructed using either interval or ratio data due to their quantitative nature. By organizing our data into intervals and counting frequencies within each interval, we can create a visual representation that helps us understand the distribution of our dataset. Understanding the type of data used and following the steps outlined above will ensure accurate and informative histograms.

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