Tag Archives: sample size

In Response to ‘Is n=1 Ever Enough?’


Alone (Photo credit: JB London)

I found readingĀ Is n=1 Ever Enough by Nicky Halverson highly thought provoking.

n (lower case) refers to the sample size, that is how many people you have asked your questions. Nicky’s article is therefore asking is it ever ok to just ask one person?

There’s no chance that a sample of one is statistically significant, but research, even quantitative research, isn’t always about producing statistically significant results. Instead, good research is about producing appropriate information to make informed decisions.

Sometimes statistically significant or highly detailed information is necessary, and therefore appropriate. Examples might be high risk decisions involving patients or significant sums of money. It’s probable that n=1 isn’t going to be sufficient.

But what about low risk decisions? Well, I have to agree with Halverson that a sample of one wouldn’t be my first choice, and I would encourage my client to reconsider. I firmly believe that one main criterion of good research is that it is reliable. In the research context, reliable specifically means obtaining consistent results. By achieving reliable – i.e. consistent – results, you can be more confident that your results are going to be meaningful and useful. By having a sample size of one you cannot determine if your results are going to be consistent with each other, simply because you will not have anything to compare your result with.

But in some cases that might not matter. Going back to the purpose of research, it is about producing information to inform a decision. If it involves low risk, your client might be comfortable making their decision on the basis of just one response. My job in this hypothetical situation would be to make my client aware of the risks of basing their decision on one case, but it is up to them if they choose to do it or not.

So, as with so much, it depends. It depends on how comfortable your client is making a decision with very limited information. But ultimately, n=1 is infinitely better than n=0.