What is the Difference Between Frequency and Relative Frequency?

The main difference between frequency and relative frequency lies in the way they represent the occurrences of a specific value or category in a dataset. Here's a breakdown of the differences:

• Frequency: Frequency refers to the number of times a particular value or category appears in a dataset. It is a count of the occurrences of that value or category. For example, if the value 2 appears 4 times in a dataset, the frequency of the value 2 is 4.
• Relative Frequency: Relative frequency is the proportion or percentage of times a specific value or category appears in a dataset. It is calculated by dividing the frequency of a value or category by the total number of data points in the dataset. For example, if the value 2 has a frequency of 4 and there are 10 data points in the dataset, the relative frequency of the value 2 is 0.4 or 40%.

In summary:

• Frequency represents the actual count of occurrences of a value or category in a dataset.
• Relative frequency represents the proportion or percentage of occurrences of a value or category in a dataset relative to the total number of data points.

Both frequency and relative frequency are essential in understanding the distribution of values or categories in a dataset and can be used to analyze the data more effectively.

Comparative Table: Frequency vs Relative Frequency

Here is a table that highlights the differences between frequency and relative frequency:

In summary, frequency is the absolute number of times a value or category appears in a dataset, while relative frequency represents the proportion or percentage of times a specific value or category appears in the dataset. To calculate frequency, you simply count the number of occurrences of a value or category in the dataset. To calculate relative frequency, you first calculate the frequency of a value or category, then divide it by the total number of data points in the dataset.