How to Compute Standard Error for Sample Sizes

A sample refers to a smaller subsection of an overall population. When you conduct research, you seek to collect data from a sample of the population because it's simply impossible to collect data from every member of a population. Standard error for sample sizes, which is often referred to as sampling error, is a number that "describes the precision of an estimate." Sampling error is typically minimized with a larger sample size. That is, the more people you collect data from, the smaller the sampling error will be.

Things You'll Need

• Calculator
• Pen and paper

Instructions

1. Write down the total sample size. This is the total number of cases or people you have collected data from.

2. Calculate the square root of the total sample size. For example, if your total sample size is 100, your square root equals 10.

3. Perform the following calculation: 1 divided by the square root of your total sample size. If we use the example from step 2, we would perform this calculation:

   1/10 = .1

4. Multiple the number you calculated in step 3 by 100. The final product is the sampling error. Our example would look like this:

   .1 x 100 = 10 percent sampling error

References

• Social Research Methods: Statistic Terms in Sampling
• Illinois State University: A Note on Sampling Error
• UC Davis: Inroduction to Sampling