Chapter 25

Ten Easy Ways to Estimate How Many

Participants You Need

IN THIS CHAPTER

Quickly estimating sample size for several basic statistical tests

Adjusting for different levels of power and α

Adjusting for unequal group sizes and for attrition during the study

Sample-size calculations (also called power calculations) tend to frighten researchers and send them

running to the nearest statistician. But if you you need a ballpark idea of how many participants are

needed for a new research project, you can use these ten quick and dirty rules of thumb.

Before you begin, take a look at Chapter 3 — especially the sections on hypothesis testing and

the power of a test. That way, you’ll refresh your memory about what power and sample-size

calculations are all about. For your study, you will need to select the effect size of importance

that you want to detect. An effect size could be the difference of at least 10 mmHg in mean

systolic blood-pressure lowering between groups on two different hypertension drugs, or it could

be having the degree of correlation between two laboratory values of at least 0.7. Once you select

your effect size and compatible statistical test, look in this chapter for the rule for the statistical

test you selected to calculate the sample size.

The first six sections tell you how many participants you need to provide complete data for you to

analyze in order to have an 80 percent chance of getting a p value that’s less than 0.05 when you run

the test if a true difference of your effect size does indeed exist. In other words, we are setting the

parameters 80 percent power at α = 0.05, because they are widely used in biological research. The

remaining four sections tell you how to modify your estimate for other power or α values, and how to

adjust your estimate for unequal group size and dropouts from the study.

Comparing Means between Two Groups

Applies to: Unpaired Student t test, Mann-Whitney U test, and Wilcoxon Sum-of-Ranks test.

Effect size (E): The difference between the means of two groups divided by the standard

deviation (SD) of the values within a group.

Rule: You need

participants in each group, or

participants altogether.

For example, say you’re comparing two hypertension drugs — Drug A and Drug B — on lowering