Sample Size

One of the worst things that can happen is that after you spend time, effort, and perhaps even money to run a survey or study, you find out that your sample size was inadequate! Sample size is important because when your sample size is too small, it is possible that even when your hypothesis really is true, your results show that it is false.

Tips

• If you are conducting a survey, you must distribute more questionnaires than your desired sample size to account for nonresponse.
• Many “sample size calculators” are available on the Internet, but you must make sure the one you are using is appropriate for your design and planned statistical analyses. A free trial of a versatile sample size calculator, called Power and Precision, is available at www.power-analysis.com. For more help with sample size calculation, contact us.

How To Calculate Your Required Sample Size

Various “rules of thumb” for determining sample size have been suggested (e.g., 30 subjects per independent variable for regression). A more precise way of determining the required sample size for your particular study is to take into account estimated effect size, alpha level* and power** based on previous similar studies in your field. Sample size, alpha level, effect size, and power are all interrelated; knowing the values of the three other parameters determines the necessary sample size.

*The probability of rejecting the null hypothesis when it is true. Also known as the p-value, the alpha level is typically set to .05.

**The probability of rejecting the null hypothesis when it is false (and thus should be rejected). In the social sciences, power is typically set to .80.

Example: Sample Size Calculation for An Independent-Samples t-Test

We calculated the sample size using the Power and Precision program. Specifying an effect size of .5, a power of .80, and a two-tailed alpha level of .05 yielded a required sample size of 128 (64 in each of the two groups).