Part of the reason why I’m able to learn what I’m learning is that my partner, Justin, is an academic. He’s built his career on reading, writing, and analyzing journal articles, which means he’s my first stop on the understanding research train. This is both great and terrible for me. On the one hand, I have an expert at my disposal. On the other hand, I have an expert at my disposal. What I think are straightforward questions turn into twenty-minute tirades that leave me more confused than before. No answer is ever simple, and I’ve been forced to accept that “it depends” is a valid conclusion.
“The more you research you read the more you’ll understand that every single study is fundamentally flawed,” he said to me yesterday. “Be careful about assumptions, because research studies are full of caveats and exceptions. They’re looking at one little sliver of one thing, and there’s no easy way to accurately translate that into something digestible and catchy for the media.”
All this because I asked him what n meant in a paper.
What is “n”?
I assumed the n operated like it does algebra, standing for a constant throughout the entire paper. As it turns out, that is entirely incorrect. There are big Ns and little ns. The big N typically stands for population size while the little n stands for some sort of value. For example, if there are 1000 people in a school but only 200 of them were chosen for a study, N=1000 and n=200.
However, the n does not necessarily refer to human subjects and the meaning of that n can change with context. Using the paper from yesterday’s post as my example, we can see that there are a variety of values for n throughout different parts of the article. The first shows up in the abstract, n=16:
Reading the sentence before it, “antidepressants were significantly better than placebo in trials that had a low risk of bias,” this little n refers to the number of studies analyzed that had a low risk of bias (16 studies.) Why they can’t just say, “In the 16 trials that had a low risk of bias…” I don’t know.
Further down the paper, n shows up again:
To understand what these ns represent, we need to read for context. The previous page states, “The literature searches from databases and additional resources identified 2890 relevant titles.” In this case, n has to do with the number of studies analyzed, and the chart breaks down how the researchers began with 2890 studies (2864 records identified through database searching + 26 records identified through other sources) and whittled their relevant studies down to the 28 included in the meta-analysis.
To sum up: An n is not an interpretation of the data but instead communicates some sort of numerical value. That value changes depending on what it’s referring to, so it’s always necessary to read for context.
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