How to Calculate Standard Deviation in Google Sheets
Standard deviation is a useful statistical measure that helps quantify the amount of variation or dispersion within a dataset. In this article, we will discuss how to calculate standard deviation in Google Sheets, a popular spreadsheet program used by many for organizing and analyzing data.
Prerequisites
Before diving into the calculation, ensure that you have a dataset entered in Google Sheets. For this tutorial, let’s say you’ve got a list of numbers in cells A1 to A10.
Step 1: Identify the formula
Google Sheets has built-in formulas to calculate standard deviation. There are two commonly used formulas:
1. STDEV.P: Use this formula when you want to calculate standard deviation for the entire population of your dataset.
2. STDEV.S: Use this formula when you want to calculate standard deviation only for a sample taken from the population.
Step 2: Choose the correct formula
Depending on your requirement (full population or sample), choose either the STDEV.P or STDEV.S formula.
Step 3: Enter the formula
To calculate standard deviation using the chosen formula, follow these steps:
1. Click on an empty cell where you want to display the result.
2. Type an equal sign (=) to start entering your formula.
3. Type “STDEV.P(” or “STDEV.S(” (without quotes) according to your requirement.
4. Select the range of cells containing your dataset. In our case, it would be A1:A10.
5. Close the brackets by typing “)”.
6. Press Enter.
The calculated standard deviation will appear in the selected cell.
Example:
Let’s assume we have this sample dataset: 34, 23, 12, 7, 45, 36, 18, 22, and 41 in cells A1:A9. To calculate the sample standard deviation:
1. Click on an empty cell, say A11.
2. Type “=STDEV.S(A1:A9)” and press Enter.
The calculated standard deviation (which is approximate) should appear in the selected cell (A11).
Conclusion
Calculating standard deviation in Google Sheets is a straightforward process. With built-in formulas like STDEV.P and STDEV.S, you can easily quantify variations within any dataset and use this information for further analysis or decision-making.