# How to Do Standard Deviation in Excel

Despite its intimidating name, standard deviation is really not that complicated to calculate. In fact, with the right tools, you can easily find the standard deviation of a set of data in just a few clicks. And the best part? Excel can do it all for you. Trust me, as someone who was once math-phobic, even I can handle this one.

The first step in calculating standard deviation in Excel is, of course, having a set of data to work with. This can come in the form of numbers, text, or even dates. Make sure all of your data is inputted correctly and consistently, or else your calculations will be off.

## Select the Range of Data

After inputting your data, select the entire range of numbers you want to calculate standard deviation for. This can be done by clicking and dragging your cursor over the range or by clicking the first cell and holding down the shift key while clicking the last cell in the range. Easy enough, right?

## Apply the Standard Deviation Formula

Now for the fun part. In order to calculate the standard deviation of your data, you'll need to use the standard deviation formula. Don't worry, though, you don't have to actually know the formula by heart - Excel will take care of that for you. All you need to do is enter the formula in the correct cell.

The formula for standard deviation in Excel is:

`=STDEV.S(range of cells)`

So if your range of cells was A1 to A10, for example, you would enter:

`=STDEV.S(A1:A10)`

You'll know you've done it correctly if you see a number pop up in the cell you've entered the formula in. Congratulations, you've just calculated the standard deviation of your data!

## What Does Standard Deviation Mean?

Now that you've calculated your standard deviation, you might be wondering what it actually means. Essentially, standard deviation measures the amount of variation or dispersion within your data. The higher the standard deviation, the more spread out your data is. In contrast, a lower standard deviation means your data is more tightly clustered around the mean.