How to Construct a Frequency Distribution Table with Examples

Raw data rarely tells a clear story on its own. When you have a long list of values — test scores, survey responses, sales figures — patterns stay hidden until you organize that data into a structure you can actually read. A frequency distribution table solves this problem by grouping values into categories and counting how often each one appears, turning a disorganized dataset into a clear, scannable summary.

Understanding how to build one is a foundational skill in statistics and data analysis. Whether you are working in Excel, R, or by hand, the process follows the same logical steps: define your classes, tally your counts, and calculate relative or cumulative frequencies as needed. The result is a table that makes comparisons easy, supports further analysis, and forms the basis for charts like histograms and frequency polygons.

What Is a Frequency Distribution Table?

A frequency distribution table is a structured summary that organizes raw data into groups — called classes or bins — and records how many times values fall within each group. Instead of scanning through hundreds of individual data points, you get a compact table that shows the shape and spread of your data at a glance.

Every frequency distribution table contains at least two core columns: the class intervals (the defined ranges of values) and the frequency (the count of observations in each range). Most tables also include additional columns for relative frequency (each class’s proportion of the total dataset, expressed as a percentage or decimal) and cumulative frequency (a running total of frequencies from the first class to the last).

Frequency distribution tables work with both quantitative data — such as heights, temperatures, or exam scores — and qualitative data, such as product categories or survey responses. When applied to quantitative data, the table groups values into equal-width intervals. When applied to qualitative data, each category simply becomes its own row.

The table below shows a basic example using student exam scores:

Class Interval	Frequency	Relative Frequency	Cumulative Frequency
50 – 59	3	10%	3
60 – 69	7	23%	10
70 – 79	10	33%	20
80 – 89	8	27%	28
90 – 99	2	7%	30
Total	30	100%	—

This single table replaces 30 individual data points and immediately reveals that most students scored in the 70–79 range, with performance tapering off toward the extremes.

When to Use a Frequency Distribution Table

Use a frequency distribution table when you want to:

• Summarize large datasets. When working with dozens, hundreds, or thousands of data points, a frequency table condenses everything into a readable format without losing the overall structure of the data.
• Identify the distribution shape. Grouping data into classes reveals whether values cluster in the middle, skew toward one end, or spread evenly — information that is difficult to extract from a raw list.
• Compare groups. Frequency tables make it straightforward to compare how two or more datasets are distributed across the same class intervals.
• Prepare data for charts. Histograms, frequency polygons, and ogive charts are all built directly from frequency distribution tables. Creating the table first makes charting faster and more accurate.
• Calculate descriptive statistics. Mean, median, mode, and standard deviation can all be estimated from a grouped frequency table, which is especially useful when you only have access to summarized data rather than the original values.

Avoid frequency distribution tables when:

• Your dataset is small (fewer than 20 values), where listing individual values is clearer and grouping adds no meaningful benefit.
• You need exact values preserved, since grouping data into classes means accepting some loss of precision.
• Your analysis requires every individual observation, such as in certain regression or time-series models.

Steps to Create a Frequency Distribution Table

Building a frequency distribution table follows a consistent process regardless of the dataset or software you use. The steps below use a worked example throughout: 30 student exam scores ranging from 52 to 98.

Dataset: 52, 55, 61, 63, 63, 67, 70, 71, 72, 74, 74, 75, 75, 76, 77, 78, 78, 79, 80, 81, 82, 83, 85, 86, 88, 89, 90, 92, 95, 98

Step 1: Find the Range

Subtract the minimum value from the maximum value to determine how spread out your data is.$$\text{Range} = \text{Maximum} – \text{Minimum} = 98 – 52 = 46$$

Step 2: Decide the Number of Classes

Choose how many groups (classes) your table will have. Most frequency tables use between 5 and 20 classes. A common guideline is Sturges’ formula:$$k = 1 + 3.322 \times \log_{10}(n)$$

Where $k$k is the number of classes and $n$n is the total number of observations.$$k = 1 + 3.322 \times \log_{10}(30) = 1 + 3.322 \times 1.477 \approx 5.9 \approx 6$$

For this dataset, 6 classes is a reasonable choice.

Step 3: Calculate the Class Width

Divide the range by the number of classes, then round up to a convenient whole number.$$\text{Class Width} = \frac{\text{Range}}{k} = \frac{46}{6} \approx 7.67 \rightarrow 8$$

Rounding up to 8 ensures all values are captured without gaps between classes.

Step 4: Set the Class Intervals

Start the first class at the minimum value (or a convenient number just below it) and add the class width to define each successive interval. Each class must be mutually exclusive — no value should fall into more than one class.

Class Interval
52 – 59
60 – 67
68 – 75
76 – 83
84 – 91
92 – 99

Step 5: Tally the Frequencies

Go through your dataset and count how many values fall within each class interval.

Class Interval	Tally	Frequency
52 – 59	|||	3
60 – 67	||||	4
68 – 75	|||| ||	7
76 – 83	|||| |||	8
84 – 91	||||	5
92 – 99	|||	3
Total		30

Step 6: Calculate Relative and Cumulative Frequencies

Relative frequency expresses each class count as a proportion of the total:$$\text{Relative Frequency} = \frac{\text{Class Frequency}}{\text{Total Observations}}$$

Cumulative frequency adds each class frequency to the sum of all previous classes.

Class Interval	Frequency	Relative Frequency	Cumulative Frequency
52 – 59	3	10.0%	3
60 – 67	4	13.3%	7
68 – 75	7	23.3%	14
76 – 83	8	26.7%	22
84 – 91	5	16.7%	27
92 – 99	3	10.0%	30
Total	30	100%	—

Step 7: Review and Verify

Before finalizing your table, run through these checks:

• All frequencies sum to the total number of observations (30 ✓)
• All relative frequencies sum to 100% (✓)
• No gaps or overlaps exist between class intervals (✓)
• The final cumulative frequency equals the total number of observations (30 ✓)

How to Create a Frequency Table in Excel

Excel offers two practical methods for building a frequency distribution table: the COUNTIFS function for full manual control, and the Analysis ToolPak for automated output. Both methods use the same exam score dataset from the previous section.

Method 1: Using COUNTIFS

The COUNTIFS function counts values that meet multiple criteria simultaneously, making it ideal for tallying observations within class intervals.

Step 1: Enter your data

Paste your raw data into column A (A2:A31 for 30 values, with a header in A1).

Step 2: Set up your class intervals

In column C, enter the lower bound of each class. In column D, enter the upper bound.

C	D
52	59
60	67
68	75
76	83
84	91
92	99

Step 3: Enter the COUNTIFS formula

In cell E2, enter:

=COUNTIFS($A$2:$A$31,">="&C2,$A$2:$A$31,"<="&D2)

Drag this formula down through E7 to populate all six class frequencies.

Step 4: Calculate relative frequency

In cell F2, enter:

=E2/SUM($E$2:$E$7)

Format column F as a percentage. Drag down through F7.

Step 5: Calculate cumulative frequency

In cell G2, enter:

=E2

In cell G3, enter:

=G2+E3

Drag G3 down through G7. The final value in G7 should equal 30.

Your completed table will look like this:

Class Interval	Frequency	Relative Frequency	Cumulative Frequency
52 – 59	3	10.0%	3
60 – 67	4	13.3%	7
68 – 75	7	23.3%	14
76 – 83	8	26.7%	22
84 – 91	5	16.7%	27
92 – 99	3	10.0%	30

Method 2: Using the Analysis ToolPak

The Analysis ToolPak is a built-in Excel add-in that generates a frequency table automatically from a defined set of bins.

Step 1: Enable the Analysis ToolPak

Go to File → Options → Add-ins. At the bottom of the window, set the Manage dropdown to Excel Add-ins and click Go. Check Analysis ToolPak and click OK.

Step 2: Enter your bin values

In a separate column, enter the upper bound of each class interval. These are your bin values:

Bin
59
67
75
83
91
99

Step 3: Run the Histogram tool

Go to Data → Data Analysis → Histogram and click OK. Configure the dialog as follows:

• Input Range: Select your raw data column (A2:A31)
• Bin Range: Select your bin column
• Output Range: Select a blank cell where you want the table to appear
• Check Cumulative Percentage if you want Excel to add that column automatically

Click OK. Excel generates a frequency table with a Bin column, Frequency column, and optional cumulative percentage column.

Step 4: Clean up the output

The ToolPak labels bins by their upper bound only (e.g., 59, 67). Manually replace these with your full interval labels (52–59, 60–67, etc.) for clarity.

Which Method Should You Use?

	COUNTIFS	Analysis ToolPak
Setup time	Moderate	Fast
Flexibility	High — formulas update when data changes	Low — static output, must rerun for new data
Relative frequency	Calculated manually	Not included by default
Best for	Live datasets that will change	Quick one-time summaries

For ongoing analysis where your data may be updated, COUNTIFS is the better choice. For a fast, one-time summary, the Analysis ToolPak gets the job done with minimal setup.

Advantages of Frequency Distribution Tables

FAQs