The **standard error of the mean** is a way to measure how spread out values are in a dataset. It is calculated as:

**Standard error = s / √n**

where:

**s**: sample standard deviation**n**: sample size

We can calculate the standard error of the mean for a given dataset in Google Sheets by using the following formula:

=STDEV.S(range of values) / SQRT(COUNT(range of values))

The following example demonstrates how to use this formula.

**Example: Standard Error in Google Sheets**

Suppose we have the following dataset:

The following formula shows how to calculate the standard error of the mean for this dataset:

The standard error of the mean turns out to be **2.0014**.

**How to Interpret the Standard Error of the Mean**

The standard error of the mean is a measure of how spread out values are around the mean. There are two things to keep in mind when interpreting the standard error of the mean:

**1. The larger the standard error of the mean, the more spread out values are around the mean in a dataset.**

To illustrate this, consider if we change the last value in the previous dataset to a much larger number:

Notice how the standard error jumps from **2.0014 **to **6.9783**. This tells us that the values in this dataset are more spread out around the mean compared to the previous dataset.

**2. As the sample size increases, the standard error of the mean tends to decrease.**

To illustrate this, consider the standard error of the mean for the following two datasets:

The second dataset is simply the first dataset repeated twice. Thus, the two datasets have the same mean but the second dataset has a larger sample size so it has a smaller standard error.