Last updated on June 18th, 2025
The mode is a measure of central tendency. The mode of grouped data is the most frequently occurring value within a dataset, which is grouped into class intervals. Since individual data points are not available, the mode is estimated using the modal class. This method provides an appropriate mode, offering insights into the most common data range. We will learn more about the mode of grouped data in the topic below.
The mode of grouped data is the most frequently occurring value within class intervals, estimated using interpolation. It lies in the modal class and is calculated using the following formula:
Mode = L + ((f1 - f0) / (2f1 - f0 - f2)) x h
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To find the mode of grouped data, we must follow the following steps:
Step 1: Find the modal class, which is the class interval with the highest frequency.
Step 2: To find the modal class, we should calculate the difference between the upper and the lower limit
Step 3: Use the mode formula:
Mode = L + ((f1 - f0) / (2f1 - f0 - f2)) x h
The formula for mode of grouped data is given below:
Mode = L + ((f1 - f0) / (2f1 - f0 - f2)) x h
Where,
L is the lower limit of the modal class
h is the size of the class interval
f1 is the frequency of the modal class
f0 is the frequency of the class preceding the modal class
f2 is the frequency of the class succeeding the modal class
To derive the formula for mode of grouped data, we must follow the following steps:
Step 1: Define the Key Variables:
Consider a frequency distribution table with the following variables:
L = Lower boundary of the modal class
f0 = frequency of the class before the modal class
f1 = frequency of the modal class
f2 = frequency of the class after the modal class
h = class width.
Step 2: Understanding the Frequency Change Around the Mode:
Since the mode is the most frequently occurring value, it must be inside the modal class, where the highest frequency occurs. However, within this class, we assume the frequency increases linearly from the previous class and then decreases towards the next class.
Step 3: Assumption of Linear Interpolation:
Using the proportional reasoning within the modal class, we assume:
Mode - Lh = (f1 - f0) / (f1 - f0) + (f1 - f2)
Rearranging to solve for mode:
Mode = L + ((f1 - f0) / (2f1 - f0 - f2)) x h
Step 4: Explanation of Each Term:
L = Lower boundary of the modal class
(f1 - f0) / (2f1 - f0 - f2) = Proportion of class width that adjusts for the differences in frequencies.
h = the width of the class interval.
The mode of grouped data have numerous applications across various fields. Let us explore how the mode of grouped data is used in different areas:
We use the mode of grouped data in business and marketing, where retailers analyze sales data to determine the most frequently purchased products. Companies use mode to identify the common customer preferences. Stores use mode to track the time intervals with the highest footfall to optimize staffing.
We use the mode of grouped data in education, where schools and universities use mode to determine the most frequent grade range, the mode also helps teachers in finding the most frequent attendance range.
In healthcare and medicine, medical researchers use mode to determine the age group is most affected by a disease. Pharmaceutical companies use mode to track the most frequently prescribed drug doses for specific conditions
Students tend to make some mistakes while solving problems related to mode of grouped data. Let us now see the different types of mistakes students make while solving problems related to mode of grouped data and their solutions:
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For the given distribution below, find the mode:
Class Interval |
Frequency |
10-20 |
5 |
20-30 |
8 |
30-40 |
12 |
40-50 |
7 |
The mode is approximately 34.44.
Identify the values:
Modal class: 30-40 (highest frequency, f1 = 12)
L = 30
f0 = 8 (frequency for 20-30)
f2 = 7 (frequency for 40-50)
h = 10
Mode = 30 + (12 - 82(12) - 8 - 7) x 10
= 30 +(424 - 15) x 10
= 30 + (49) x 10
30 + 4.44 = 34.44.
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Jaipreet Kour Wazir is a data wizard with over 5 years of expertise in simplifying complex data concepts. From crunching numbers to crafting insightful visualizations, she turns raw data into compelling stories. Her journey from analytics to education ref
: She compares datasets to puzzle games—the more you play with them, the clearer the picture becomes!