## Most of us run sample size calculations when a granting agency or committee requires it. That’s reason 1.

That *is* a very good reason. But there are others, and it can be helpful to keep these in mind when you’re tempted to skip this step or are grumbling through the calculations you’re required to do.

It’s easy to base your sample size on what is customary in your field (“I’ll use 20 subjects per condition”) or to just use the number of subjects in a similar study (“They used 150, so I will too”).

Sometimes you can get away with doing that.

However, there really are some good reasons beyond funding to do some sample size estimates. And since they’re not especially time-consuming, it’s worth doing them.

Often the most time consuming part is figuring out and writing the data analysis plan to base the calculations on, but that’s another step you should do anyway.

## Reason 2: Many, many published studies have very low power, and are bad sources for basing your sample size on.

As reported in Keppel, Cohen calculated the power of every study in a psychology journal for a year. The average power was just under 50%.

If power is 50% for a study, it basically means that that study had a 50% chance of finding significance for a real effect, given the sample size, the effect size, and the statistical test. Because these were published studies, they must have had significant results. But there were probably a lot of other studies (just as many) that never got published because they didn’t have adequate power.

If you now attempt to build on that study and you use the same sample size, you only have a 50% change of replicating it with significant results. Do your own power calculation and raise the sample size, if needed.

## Reason 3: A power calculation estimates not only how many participants you need, but how many you don’t need.

You don’t want to spend any more resources–time, money, and energy–collecting more data than you need. Save those resources for a follow-up study.

Especially if your study creates any risk, or even inconvenience, for your human or animal participants, you don’t want to oversize your study either. You don’t want to expose more participants than necessary to the risk.

## Reason 4: When sample size calculations tell you you’re close, but have not quite enough subjects, you can make adjustments to the study that will increase the power in other ways.

Maybe you can adjust the way you’re measuring some of your variables to add precision or switch your design to something that will give you a little more power. Or make sure you include some controls that will control some of the random error. All of these increase power without increasing sample size.

## Reason 5: The biggest benefit of doing these calculations is to not waste years and thousands of dollars in grants or tuition pursuing an impossible analysis.

If sample size calculations indicate you need a thousand subjects to find significant results but time, money, or ethical constraints limit you to 50, *don’t do that study*.

I know it’s painful to go back to square 1, but it’s much better to do it now than after 3 years of work.

{ 2 comments… read them below or add one }

I am glad with it. However, I am more interested in “second generation of multivariate analysis”, thus STRUCTURAL EQUATION MODELLING(SEM).

If you could delve into this, it enhance the ANALYSIS FACTOR as an organization

Hi Bashir,

Thanks for the input. I agree, SEM is a great topic that would benefit a lot of people. We’ll see what we can do.

Karen