Frequency Distribution Table Calculator
Frequency Distribution Table Calculator
Enter a list of numbers to instantly build its frequency distribution table — a sorted list of every unique value in your dataset, how many times each one occurs, and what percentage of the total that represents.
The absolute frequency column shows the raw count for each value, while the relative frequency column expresses that same count as a percentage of the total, making it easy to compare distributions built from different sample sizes. This tool works on ungrouped data — counting occurrences of each exact value — which is the right approach for categorical or discrete data with a manageable number of distinct values; continuous data with many unique values is usually better summarized with grouped bins or a histogram instead.
- Absolute frequency: the raw count of how many times each unique value appears in the dataset.
- Relative frequency: that count expressed as a percentage of the total number of data points, making it easy to compare distributions of different sizes.
- Ungrouped data only: this counts occurrences of each exact value — for continuous data better suited to bins/class intervals (like ages grouped into 10-year ranges), you'd need a grouped frequency table instead.
Why would I want a frequency table instead of just looking at the raw data?
A frequency table reveals the shape of your data at a glance — which values are common, which are rare, and roughly how the data clusters — far faster than scanning an unsorted list.
What if my data is continuous (like heights or weights) rather than discrete counts?
An ungrouped frequency table (like this one) works best for discrete or repeated values — for continuous data, you'd typically group values into ranges (bins) first, which this specific tool doesn't do.
Frequency Distribution Table Calculator
Enter a list of numbers to instantly build its frequency distribution table — a sorted list of every unique value in your dataset, how many times each one occurs, and what percentage of the total that represents.
The absolute frequency column shows the raw count for each value, while the relative frequency column expresses that same count as a percentage of the total, making it easy to compare distributions built from different sample sizes. This tool works on ungrouped data — counting occurrences of each exact value — which is the right approach for categorical or discrete data with a manageable number of distinct values; continuous data with many unique values is usually better summarized with grouped bins or a histogram instead.
- Absolute frequency: the raw count of how many times each unique value appears in the dataset.
- Relative frequency: that count expressed as a percentage of the total number of data points, making it easy to compare distributions of different sizes.
- Ungrouped data only: this counts occurrences of each exact value — for continuous data better suited to bins/class intervals (like ages grouped into 10-year ranges), you'd need a grouped frequency table instead.
Why would I want a frequency table instead of just looking at the raw data?
A frequency table reveals the shape of your data at a glance — which values are common, which are rare, and roughly how the data clusters — far faster than scanning an unsorted list.
What if my data is continuous (like heights or weights) rather than discrete counts?
An ungrouped frequency table (like this one) works best for discrete or repeated values — for continuous data, you'd typically group values into ranges (bins) first, which this specific tool doesn't do.