The mode is the most frequent observation in the data set, i.e., the value (number) that appears the most in the data set. It is possible that there may be more than one mode, or it may also be possible that there is no mode in a data set. Usually, it is calculated for categorical data (data belongs to a nominal or ordinal scale), but it is unnecessary.
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It can also be used for ordinal and ratio scales, but there should be some repeated values in the data set, or the data set can be classified. If any of the data points don’t have the same values (no repetition in data values), then the mode of that data set will not exist or may not be meaningful. A data set having more than one mode is called multimode or multimodal.
Example: Most Frequent Observation
Example 1: Consider the following data set showing the weight of a child at the age of 10 years: 33, 30, 23, 23, 32, 21, 23, 30, 30, 22, 25, 33, 23, 23, 25. We can find the most repeated value by tabulating the given data in the form of a frequency distribution table, whose first column is the weight of the child and the second column is the number of times the weight appears in the data, i.e., frequency of each weight in the first column.
| Weight of 10 year child | Frequency |
|---|---|
| 22 | 1 |
| 23 | 5 |
| 25 | 2 |
| 30 | 3 |
| 32 | 1 |
| 33 | 2 |
| Total | 15 |
From the above frequency distribution table, we can easily find the most repeated occurring observation (data point), which will be the mode of the data set, and it is 23, meaning that the majority of the 10-year-old children weigh 23kg. Note that for finding the mode, it is not necessary to make a frequency distribution table, but it helps in finding the mode quickly, and the frequency table can also be used in further calculations, such as percentage and cumulative percentage of each weight group.
Example: Most Repeated Gender
Example 2: Consider we have information about a person’s gender. Consider that $M$ stands for male and $F$ stands for Female. The sequence of the person’s gender noted is as follows: F, F, M, F, F, M, M, M, M, F, M, F, M, F, M, M, M, F, F, M. The frequency distribution table of gender is
| Weight of 10 year child | Frequency |
|---|---|
| Male | 11 |
| Female | 9 |
| Total | 25 |
The most repeated gender is male, showing that the most frequent or the majority of the people have the male gender in this dataset.
Mode can be found by simply sorting the data in ascending or descending order and then counting the frequent value without sorting the data, especially when the data contains a small number of observations, though it may be difficult to remember the number of times which observation occurs. Note that the mode is not affected by the extreme values (outliers or influential observations).
The mode is also a measure of central tendency, but it may not reflect the center of the data very well. For
- In the production process, a product can be classified as a defective or non-defective product.
- Student grades can be classified as A, B, C, D, etc.
- Gender of respondents
- Blood Group
Example: Most Repeated Value
Example 3: Consider the following data. 3, 4, 7, 11, 15, 20, 23, 22, 26, 33, 25, 13. There is no mode of this data as each value occurs once. By grouping this data in a useful and meaningful form, we can get the most repeated value of the data. For example, the grouped frequency table is
| Group | Values | Frequency |
|---|---|---|
| 0 to 9 | 3, 4, 7 | 3 |
| 10 to 19 | 11, 13, 15 | 3 |
| 20 to 29 | 20, 22, 23, 25, 26 | 5 |
| 30 to 39 | 33 | 1 |
| Total | 12 |
We cannot find the most Frequent value from this table, but we can say that “20 to 29” is the group in which most of the observations occur. We can say that this group contains the mode, which can be found by using the grouped formula.
Mode from the Bar Graph
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