Last updated on June 5th, 2025
Assume your school offers five different extracurricular activities for students. To identify the most frequently chosen activity, we use a measure of central tendency known as the mode. There are various real-life applications for utilizing the value of mode, such as detecting consumer preferences or understanding public interests.
The mode, also known as modal value, indicates the most frequent value in a dataset. This number provides an approximate estimate of which elements in a data collection appear most frequently. Mean, median, and mode are three related but different measures of central tendency.
Mode = L + ((fm- f1)/(2fm- f1- f2)) × h
Here:
L = Lower limit of the modal class
h = Class interval size
fₘ = Frequency of the modal class
f₁ = Frequency of the class before the modal class
f₂ = Frequency of the class after the modal class
The modal class represents the data interval with the highest frequency.
There are different types of modes depending on the number of modal solutions. We will now see the different types of modes:
If the dataset has only one mode, it is called unimodal.
For example: In set A= {2, 3, 3, 5, 6, 6, 6, 7}, the mode is 6.
If there are two modes present in the given data, it is called bimodal.
For example: In set B = {3, 3, 3, 5, 7, 7, 7, 4}, the modes are 3 and 7 because both numbers are repeated three times.
If there are exactly three modes in a dataset, it is called trimodal.
For example: In set C = {3, 3, 3, 5, 5, 5, 6, 7, 7, 7, 4}, the modes are 3, 5, and 7 as each of these numbers are repeated three times.
A dataset can contain multiple modes and if it has more than three modes, it is known as multimodal.
For example: if set A= {1, 2, 2, 2, 3 4, 4, 4, 5, 6, 6, 6, 8, 8, 8,}, the data set has four modes: 2, 4, 6, 8 as these numbers are repeated the same number of times.
The Merits and Demerits of Mode are discussed below:
Merits | Demerits |
Mode is when a data value repeats more often than the others in a dataset. | Since there can be multiple modes in a set, it might be difficult to determine them. |
It is resistant to outliers | There are several methods that give different results. |
Visually represented using graphs | We cannot find the total value of a dataset using mode alone. |
It is quantitive in nature which means it can be obtained by counting and measuring | Ideal only for large datasets. |
To calculate the mode of ungrouped data,
Understanding the differences between mean, median, and mode enables solving the problems related to them easily. The key differences are listed below:
Definition | Calculation | Application | |
Mean | The calculated average of any dataset. | The sum of all the terms by the total number of their count. | The dominant tendency derived is sensitive to outliers. |
Median | The middle value is when the dataset is arranged in an increasing or decreasing order. | Arrange the numbers in an appropriate order and then find the middle number. | The dominant tendency derived is not sensitive to outliers. |
Mode | The most frequently appeared value in a dataset. | Determine the value that occurs frequently in the set. | The central tendency derived helps in determining the most frequently occurring in the set. |
The mode is the most frequently used in real-life applications such as businesses, social media, etc. Let’s understand the top uses of mode in day-to-day life.
Businesses use mode quite often to understand which product is in demand. The mode techniques help in understanding the on-demand product, any increase in production rate, etc.
In fashion stores, the retailers will stock up on the most sold sizes, colors, or styles of fashion clothing by using the mode to maximize their sales.
Social media influencers use mode to decide what type of content they should post on social media by analyzing their likes, shares, and comments over their mode value.
In many countries, the mode is used to analyze which time is the most frequently traveled, to understand the most crowded time of buses, trains, or any public transports.
Students make mistakes when determining the mode, which can be resolved by understanding the concept and the errors they make. Let’s look at some common mistakes and the ways to avoid them:
Determine the mode of the numbers: 5, 5, 5, 6, 8, 8, 9, 8, 6
Mode in the given dataset is 5 and 8 (Bimodal).
Count the number of times each number appear in the set:
Now, we determine the mode
The numbers 5 and 8 occur frequently (3 times each) in the dataset.
We have two numbers of the same frequency, so we conclude that this dataset is bimodal.
A company records the number of customers who purchased their products each day in a week. Find the mode of the dataset: 7, 5, 7, 8, 8, 8, 3.
The mode of the given dataset is 8.
Count the number of times each number appears in the set:
Now, we determine the mode
The number 8 occurs frequently (3 times) in the dataset.
Since 8 is the mode in the given dataset, we conclude that this dataset is unimodal.
A restaurant recorded the number of burgers sold in 5 days: 45, 40, 55, 70, 80
8 is the mode in the given dataset.
Count the number of times each number appears in the set:
Now, we determine the mode
Since each number in the dataset occurs only once, we conclude that there is no mode in the given dataset.
A survey was conducted among students to find their favorite color. The results were: Red, Blue, Peach, Peach, Yellow, Lavender, and Lavender. What is the most popular color (mode)?
Mode: Peach and Lavender; the given dataset is bimodal.
Count the number of times each color appears in the set:
Now, we determine the mode
Here, the colors peach and lavender appear the most (2 times). Therefore, the dataset is bimodal.
A teacher recorded the test scores of students: 88, 90, 56, 84, 97, 88, 84, 93, 90, 89 Find the mode.
Mode: 88, 90, and 84; Since there are three modes, the dataset is trimodal.
Count the number of times each score appears in the set:
Now, we determine the mode
Here, the numbers 88, 90, and 84 appear the most (2 times each). Therefore, the dataset is trimodal.
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!