Relative frequency in statistics is the proportion of times a value or event occurs compared to the total number of observations in the dataset.

What is relative frequency in simple terms?

Relative frequency tells you:

“Out of all the data I have, what fraction (or percentage) belongs to this category?”

If an event happens fff times in a dataset of nnn total observations, then

Relative frequency=fn\text{Relative frequency}=\frac{f}{n}Relative frequency=nf​

This can be written as a fraction, decimal, or percentage (for example, 0.25 or 25%).

Formal definition and formula

  • Absolute frequency : How many times a value appears (just the raw count).
  • Relative frequency : That count divided by the total number of observations (a proportion).

Formula:

Relative frequency of a value=frequency of that valuetotal number of observations\text{Relative frequency of a value}=\frac{\text{frequency of that value}}{\text{total number of observations}}Relative frequency of a value=total number of observationsfrequency of that value​

You will also see it written as:

  • Relative frequency=f/n\text{Relative frequency}=f/nRelative frequency=f/n
  • “Subgroup count / total count.”

Quick example

Imagine you survey 20 people about their favorite drink and get:

  • Coffee: 8 people
  • Tea: 6 people
  • Juice: 4 people
  • Water: 2 people

Total people n=20n=20n=20. Relative frequencies:

  • Coffee: 8/20=0.4=40%8/20=0.4=40%8/20=0.4=40%
  • Tea: 6/20=0.3=30%6/20=0.3=30%6/20=0.3=30%
  • Juice: 4/20=0.2=20%4/20=0.2=20%4/20=0.2=20%
  • Water: 2/20=0.1=10%2/20=0.1=10%2/20=0.1=10%

Here, “40% like coffee” is a relative frequency statement.

Relative frequency vs frequency

[7][5] [5] [3][1][9] [9][5]
Concept What it is Example
Frequency (absolute) Raw count of how many times a value occurs.“8 people chose coffee.”
Relative frequency Frequency divided by total; a proportion/percentage.“40% of people chose coffee (8 out of 20).”
Relative frequency is especially useful when you want to **compare groups of different sizes** , because proportions are comparable while raw counts are not.

Relative frequency distribution

A relative frequency distribution is a table or chart listing each value (or class/interval) in a dataset along with its relative frequency.

Typical steps:

  1. List each distinct value or class.
  2. Count how many times each appears (frequency).
  1. Divide each frequency by the total number of observations to get relative frequencies.
  1. Optionally convert to percentages and visualize with a bar chart, histogram, or pie chart.

These tables make it easy to see how the data is distributed and are often used as the basis for graphs such as histograms and pie charts.

Why relative frequency matters (and a quick “story”)

Suppose two streamers are comparing how often viewers choose different game genres on their channels:

  • Streamer A: 100 viewers, 50 choose action games.
  • Streamer B: 1,000 viewers, 200 choose action games.

Raw frequencies:

  • A: 50 action fans
  • B: 200 action fans

But relative frequencies:

  • A: 50/100=0.50=50%50/100=0.50=50%50/100=0.50=50%
  • B: 200/1000=0.20=20%200/1000=0.20=20%200/1000=0.20=20%

Even though B has more absolute action fans, A has a higher relative frequency of action fans in their audience. That gives a more fair comparison between their communities.

Key takeaways

  • Relative frequency = “part / whole” for a category in your data.
  • It is calculated as f/nf/nf/n, where fff is the category’s frequency and nnn is the total number of observations.
  • It is usually expressed as a fraction, decimal, or percentage.
  • It is crucial for comparing data across different sample sizes and for building relative frequency tables and graphs.

Information gathered from public forums or data available on the internet and portrayed here.