The Empirical Rule Calculator is a tool used to determine the expected distribution of data based on its standard deviation. The Empirical Rule, also known as the 68-95-99.7 rule, is a statistical rule of thumb that states that, for a normal distribution:

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The Empirical Rule, also known as the 68-95-99.7 rule, is a statistical rule of thumb that applies to normal distributions. For a normal distribution, approximately: 68% of the data falls within one standard deviation of the mean 95% of the data falls within two standard deviations of the mean 99.7% of the data falls within three standard deviations of the mean

## What is the empirical rule?

The empirical rule, also known as the 68-95-99.7 rule or the three-sigma rule, is a statistical principle that applies to data that follows a normal distribution. The rule states that:

- Approximately 68% of the data values lie within one standard deviation of the mean.
- Approximately 95% of the data values lie within two standard deviations of the mean.
- Approximately 99.7% of the data values lie within three standard deviations of the mean.

In other words, if a set of data follows a normal distribution, then most of the data values will be clustered around the mean, and the spread of the data will be predictable based on the standard deviation. The empirical rule is a useful tool for understanding and interpreting data, as it provides a simple way to estimate the percentage of data that falls within a certain range of values.

It is important to note that the empirical rule only applies to data that follows a normal distribution. If the data is skewed or has a different distribution, then the rule may not be applicable.

## How to use the Empirical rule?

To use the empirical rule, you need to have a set of data that follows a normal distribution. Here are the steps to use the empirical rule:

- Calculate the mean and standard deviation of the data set.
- Use the empirical rule to estimate the percentage of data that falls within one, two, and three standard deviations of the mean.
- Interpret the results based on the context of the data set.

For example, let’s say you have a data set of exam scores that follows a normal distribution, and the mean score is 75 with a standard deviation of 10. You can use the empirical rule to estimate the percentage of students who scored within certain ranges:

- Approximately 68% of students scored between 65 and 85 (one standard deviation from the mean).
- Approximately 95% of students scored between 55 and 95 (two standard deviations from the mean).
- Approximately 99.7% of students scored between 45 and 105 (three standard deviations from the mean).

Based on these estimates, you can interpret the results in the context of the data set. For example, you could say that most students scored within one standard deviation of the mean, and only a small percentage of students scored significantly above or below the mean. Alternatively, you could use the estimates to identify students who scored in the top or bottom 2.5% of the class, based on the two-standard-deviation range.