Frequency Distribution Calculator

A frequency distribution is a summary of how often each value (or range of values) appears in a dataset. It organizes raw data into a table showing each value or class interval alongside its count, making patterns and distributions easy to see at a glance.

An ungrouped frequency distribution lists each unique exact value and its count — best for small datasets or discrete data. A grouped frequency distribution divides the data range into class intervals (bins) and counts how many values fall into each interval, which is more practical for large or continuous datasets.

Relative frequency is the proportion of observations belonging to a given value or class, expressed as a percentage. It is calculated by dividing the frequency of that class by the total number of observations and multiplying by 100. Relative frequencies help you compare distributions of different sizes.

Cumulative frequency is the running total of frequencies up to and including a given value or class. It tells you how many observations fall at or below a particular point in the dataset. The last cumulative frequency always equals the total number of data points.

A common guideline is to use between 5 and 10 classes. Too few classes hide important patterns, while too many make the table cluttered. Sturges' rule suggests using k ≈ 1 + 3.322 × log₁₀(n) classes, where n is the number of data points.

Type or paste your numbers into the dataset field, separated by commas, spaces, or line breaks. For example: 3, 7, 11, 13, 17. Mixed separators are handled automatically. The calculator ignores any non-numeric entries.

The mode is the value (or class midpoint) that appears most frequently in your dataset. In a frequency table, it is simply the row with the highest frequency. A dataset can have one mode (unimodal), two modes (bimodal), or more.

Frequency distributions are useful whenever you want to understand how data values are spread across a range — for example, analyzing exam scores, survey responses, sales figures, or measurement data. They are a foundational step in descriptive statistics before building histograms or computing summary statistics.