# Empirical Rule Calculator

Created by Rita Rain
Reviewed by Bogna Szyk and Jack Bowater
Last updated: Nov 10, 2022

The empirical rule calculator (also a 68 95 99 rule calculator) is a tool for finding the ranges that are 1 standard deviation, 2 standard deviations, and 3 standard deviations from the mean, in which you'll find 68, 95, and 99.7% of the normally distributed data respectively. In the text below, you'll find the definition of the empirical rule, the formula for the empirical rule, and an example of how to use the empirical rule.

If you're into statistics, you may want to read about some related concepts in our other tools, such as the Z-score calculator or the point estimate calculator.

## What is the empirical rule?

The empirical rule is a statistical rule (also called the three-sigma rule or the 68-95-99.7 rule) that states that, for normally distributed data, almost all of the data will fall within three standard deviations on either side of the mean.

More specifically, you'll find:

• 68% of data within 1 standard deviation
• 95% of data within 2 standard deviations
• 99.7% of data within 3 standard deviations

Let's explain the concepts used in this definition:

Standard deviation is a measure of spread; it tells how much the data varies from the average, i.e., how diverse the dataset is. The smaller value, the more narrow the range of data is. Our standard deviation calculator expands on this description.

Normal distribution is a distribution that is symmetric about the mean, with data near the mean being more frequent in occurrence than data far from the mean. In graphical form, normal distributions appear as a bell-shaped curve, as you can see below:

Of course, you can learn more by visiting the normal distribution calculator.

## The empirical rule - formula

The algorithm below explains how to use the empirical rule:

1. Calculate the mean of your values:
$\mu = \frac{\sum x_i}{n}$

Where:

• $\sum$ - Sum;
• $x_i$ - Each individual value from your data; and
• $n$ - The number of samples.

You can also make your life easier by simply using the average calculator.

1. Calculate the standard deviation:
$\sigma = \sqrt{\frac{\sum (x_i - \mu)^2}{n-1}}$
1. Apply the empirical rule formula:
• 68% of data falls within 1 standard deviation from the mean - that means between $\mu - \sigma$ and $\mu + \sigma$.

• 95% of data falls within 2 standard deviations from the mean - between $\mu - 2\sigma$ and $\mu + 2\sigma$.

• 99.7% of data falls within 3 standard deviations from the mean - between $\mu - 3\sigma$ and $\mu + 3\sigma$.

Enter the mean and standard deviation into the empirical rule calculator, and it will output the intervals for you.

## An example of how to use the empirical rule

Intelligence quotient (IQ) scores are normally distributed with the mean of 100 and the standard deviation equal to 15. Let's have a look at the maths behind the 68 95 99 rule calculator:

1. Mean: $\mu = 100$

2. Standard deviation: $\sigma = 15$

3. Empirical rule formula:

$\mu - \sigma = 100 - 15 = 85$
$\mu + \sigma = 100 + 15 = 115$

68% of people have an IQ between 85 and 115.
$\mu - 2\sigma = 100 - 2 \cdot 15 = 70$
$\mu + 2\sigma = 100 + 2 \cdot 15 = 130$

95% of people have an IQ between 70 and 130.

$\mu - 3\sigma = 100 - 3 \cdot 15 = 55$
$\mu + 3\sigma = 100 + 3 \cdot 15 = 145$

99.7% of people have an IQ between 55 and 145.

For quicker and easier calculations, input the mean and standard deviation into this empirical rule calculator, and watch as it does the rest for you.

## Where is the empirical rule used?

The rule is widely used in empirical research, such as when calculating the probability of a certain piece of data occurring or for forecasting outcomes when not all data is available. It gives insight into the characteristics of a population without the need to test everyone and helps to determine whether a given data set is normally distributed. It is also used to find outliers – results that differ significantly from others - which may be the result of experimental errors.

Rita Rain
Mean
Standard deviation
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