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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 / âˆšn**

where:

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

You can calculate the standard error of the mean for any dataset in Excel by using the following formula:

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

The following example demonstrates how to use this formula.

**Example: Standard Error in Excel**

Suppose we have the following dataset:

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

The standard error turns out to beÂ **2.0014**.

Note that the functionÂ **=STDEV()Â **calculates the sample mean, which is equivalent to the functionÂ **=STDEV.S()Â **in Excel.

Thus, we could have used the following formula to get the same results:

Once again the standard error turns out to be **2.0014**.

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

The standard error of the mean is simply 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 fromÂ **2.0014Â **toÂ **6.9783**. This is an indication 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.