I noticed that in my field, studies involving testing with human subjects often only show a small number (N>=10) of them. While I understand that researchers are often limited on funding and time, I always thought studies like that would require a certain minimum of subjects far greater than that.

Different sources I found (e.g. this one p.3, this sample size calculator, or Wikipedia) seem to support that impression.

My field is audio signal processing, and the studies that caught my attention were about speech intelligibility, hearing comfort and other measures related to human hearing.

My question is:

Why do some studies only involve a small count of subjects when statistical theory suggests a high number of participants is required to make meaningful assertions?

(I assume that there is something I missed or something that got mixed up in my head, and I'd really like to find my error)

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    Potential safety issues, new protocols that could have unforeseen problems etc., may make it necessary to perform tests on a small scale. Even if no harm would be done by going straight ahead with a large scale trial, you don't want to find out later that the results are worthless due to procedural errors in implementing the protocols. Commented Jul 8, 2018 at 11:55
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    This is probably a better fit at cogsci.se, but as it is essentially my field I will chime in. Individual differences in psychoacoustic studies are typically pretty small for highly skilled and trained individuals and the point of the studies is to determine the limits of perception and not what percentage of the population can achieve those limits. Therefore we find 1-5 great subjects and train and test the hell out of them. You typically can learn more from 100 hours of testing one good subject than testing 100 subjects for an hour each.
    – StrongBad
    Commented Jul 8, 2018 at 15:47
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    @CountIblis you guys should probably leave those as answers, not comments...
    – user8283
    Commented Jul 8, 2018 at 18:21
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    Because then you get to justify the traditional "Further studies are needed to confirm these results."
    – Itsme2003
    Commented Jul 8, 2018 at 23:13
  • 4
    "...only show a small number (N>=10) of them." You mean "N<= 10", right? Commented Jul 9, 2018 at 12:10

5 Answers 5


Although large samples produce more robust results, small samples are not completely devoid of usefulness - they're just more likely to be wrong. As long as the reader keeps this in mind, there's nothing fundamentally wrong with publishing these small-sample studies.

Further, sometimes you just cannot get enough samples to make an assertion with confidence. Examples:

GW170817 was a one-of-a-kind neutron star merger event. Since the rate of neutron star mergers isn't well-known, one could easily wait another five years and not get another sample (plus you'd need hundreds of samples to make the kind of statements you're looking for).

The Berlin Patient - there's no cure for AIDS. Even if this single case were a fluke, it's still worth reporting since it's a signpost telling researchers where to look.

Habitability research - The question of whether aliens exist captures the public imagination. Like it or not, we have to approach the problem from what we know, and we know only one planet with life (i.e. sample size of one). You could of course just decline to work in the field until we've discovered several hundred alien civilizations, but then you could be waiting forever. Plus with no idea what to look for, observational astronomers would have a much harder time finding these civilizations.

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    Small samples are more likely to be wrong about the population, but an N-of-1 study reliably tells you about that individual.
    – StrongBad
    Commented Jul 8, 2018 at 15:49

Nielsen's curve showing the relationship of participants and usability problems discovered.

In usability research, just 5 participants will reveal about 75% of the problems with a system. This seems to fit very well for qualitative research (so perhaps the hearing comfort studies you mentioned?). From my own experience of running studies with 8-32 participants, it only took running the first few participants before every subsequent participant felt like a repeat.

Sure, you might not be able to get statistical significance with 5 participants, but you can learn a lot.

Figure taken from Nielsen's article Why You Only Need to Test with 5 Users.

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    Somewhat in the same direction: if you consider a sensory panel (smell, taste), you should not start with < 8 panelists because of the biological variety in smell receptors across humans. (And you won't be able to afford many more...) Commented Jul 8, 2018 at 22:50

Of course this depends a lot on what you're after. If you are looking for differences or some other effect within an individual, then your sample size are measurements within the individual rather than number of individuals. In such scenarios it is legitimate to study only a single (or a few) individuals.

Also, if your individuals are not studied per se but act as "instrument" (testing answer by @AustinHenley, or setting up a sensory panel, i.e. you use N individuals to smell/taste the effect of something you do/produce) you need a number to cover most of the important general variation we have in the population. But that number actually isn't that large because typically only people who are good at the task at hand will be used. I.e., smell-blind people do not apply for a sensory panel in general, you only need to cover the variation in receptors you encounter in people who are good at smelling. And that are far fewer than, say, the number of people you'd need to look at to find out which proportion of people are good at smelling, or the number you'd look at in order to be able to predict, say, the probability that a randomly chosen unknown person will like the smell/taste of what you produced:

If you need to make conclusions about a population or about applicability of your findings to unknown individuals of a population (if we train our method on an individual, this is how well it will do), you need to representatively cover the variability in that population, and this requires large sample size.

For the population questions, I fully agree with @Buffy's analysis, but I'll go a bit further about possible reasons.

  • The maybe most scary (to me) reason is that it is IMHO perfectly possible/far too easy to publish studies with low quality due to far-too-small sample size.
    Note that I've been working in a life-science field, i.e. close to where we have lots of reports about studies not being reproducible since years, and at least in my sub-field not much of an effect on the sample size.

  • One particular problem I see with small sample size studies is not scientific (as in limitations of conclusions that can be drawn) but political. The "politics" of academia focuses very much on novelty. This basically means that once a small sample size study is published, any larger follow-up study has to present extremely good arguments to overcome the "this is known already" bias in funding.
    This means, in addition to being uncertain, small sample size studies may prevent getting certainty.

  • In (industrial) experimental design it is often recommended to start with a preliminary experiment using, say, 1/3 or 40% of the available resources. Then do a preliminary analysis and if necessary re-adjust the allocation of the remaining resources accordingly.
    This takes time and means effort. However, if the preliminary results are good, they can be published. The follow-up study will then potentially face a lack-of-novelty hurdle to publication.

  • Master's and PhD theses by definition are the work of one student. This limits how much work can happen in one study. And it pushes towards experiments that are inconsistent in the long term: students cannot be abused as lab robots (which would help getting together good sample sizes - but good lab technicians are more expensive...) as they need to contribute scientifically. One of the easiest ways to do that is to improve the experiments over what has been done before - leading to large numbers of small sample size studies/fragmented series of experiments.

  • The requirement of "own work" sometimes causes students to not speak openly about their project and not seek advise. Every so often that leads to flawed experimental design and/or too small sample size and this is realized only during data analysis (or not at all). But then it is often too late to do anything but try to rescue the existing data. And rescued into a paper it must be, because otherwise the student won't have the paper they need.

  • I see another potential conflict in mixing the evaluation of scientific work (as in grading the PhD student's work) and arriving at scientific findings:

    • if a student in their well-planned and well-conducted study finds the "desired" effect, that implies both that the student did their work well, and we have a scientific advance. All is fine.
    • However, if things don't work out nicely, things are much more difficult: Was the failure due to the student not working well (I come from a wet lab field)? And/or is there no effect? In other words, the student not finding an effect has to put in much more effort in demonstrating it is not their fault.

    Now consider putting a set amount of effort either in n underpowered studies or in 1 with good sample size. If the one with large sample size fails, you don't have a single paper*. If a small sample size study doesn't find an effect it is simply not published, and you move on to the next, because it is usually too much hassle to make a paper publishable with negative finding. But keep in mind that small sample size papers are not only underpowered, but they also "provide" a high "chance" to produce false positive findings (i.e. overestimate effect) - and that means a paper.

  • Our brain tends to underestimate the effect of chance. This means without doing the statistics, we're likely to intuitively underestimate chance and be overconfident in our findings.

  • There are studies where it is practically speaking impossible to obtain anything close to the required sample size**. And there's nothing wrong scientifically with case reports and small studies as long as they are clearly indicated as such, and the conclusions take the required caution and limitations are clearly stated.
    Often, it is also practically impossible to obtain representative samples. I'm still OK as long as a) limitations are openly stated, and/or b) even applicability is checked by plugging in reasonable guesstimates for prevalence/incidence/class frequencies to at least give a critical thought to suitability for the application in question.
    However, I see far too many studies that needlessly "rescue the world on the basis of 3 mice" or that work on, say, 20 samples of tumor tissues that are anyways cut out of patients with a disease that is neither rare, nor is the tumor volume small, nor is all the tissue needed for correct diagnosis in order to properly treat the patient.

  • Finally, I consider it the worst possible waste of experimental effort (and that's even worse if test subjects/animals are involved) if a study is so small that a back-of-the-envelope calculation would have discovered that even in the best case no practical conclusion can possibly be drawn.
    (e.g. reserving 4 independent test patients in development of a medical diagnostic [pos/neg for some disease] out of your 20 patients. Assuming all 4 are correctly classified, this gives you an observed accuracy of 100 %. But the 95 % confidence interval for accuracy ranges from around guessing to perfect.)

* real life example:

  1. I've met a PhD student who was working with a ≈ 500 patient sample size in my field. Measured them all, found there was a flaw in the design of the measurements (not properly randomized order of measurements, thus strictly speaking drift in time of the measurements could not be excluded as cause of the observation - which however looked like real effects from a physical/spectroscopic point of view). Realized this, and re-did all the measurements. Did not find the effect which was expected from long experience of the supervisor with similar situations and preliminary experiments. It is not clear whether there is truly no effect after all, or whether something else went wrong with the experiments (non-negligible risk for what they do), or both.
  2. Same field, other university, other students get their PhD on the basis of, say, 20 patients. Not randomized, never thought about experimental design, not to speak of limitations of their study.

I'd judge student 1 much better than students 2 in terms of the scientific work they did. But they'll have to face a struggle to convince their committee of that: the committees of 2 clearly were not aware of the limitations, nor were the referees of the papers. So students 2 happily publish multiple papers in the time where student 1 struggles whether their study can be published. And student 1 did admit to a mistake, so there's evidence of student 1 not working well (they did make a mistake after all) - whereas there is no such evidence in the studies of 2....

** I'm also fine with the practical limitation that in many interdisciplinary fields if you need significant work of others (in our case, e.g. reference diagnoses by pathologists reviewing every single of our samples) those others may want to see preliminary data published in order to make sure they don't waste their effort with people who don't know what they are doing and/or are not serious about the application.

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    Excellent +1 (more if possible). I always told my students that a negative result is just as valid scientifically as a positive one. You are seeking knowledge, not a pre defined result. If the "effect" doesn't exist, you need to know that just as much as when it does.
    – Buffy
    Commented Jul 8, 2018 at 17:31
  • The majority of this answer is about the population, but the question is why small N studies.
    – StrongBad
    Commented Jul 8, 2018 at 20:17
  • @StrongBad: many of the small N studies I see do draw premature conclusions about populations or about the generalization performance to unknown individuals. So if that's not an issue, small N is not an issue but appropriate. However, of the small N studies I see, there are few where that small N is appropriate... Commented Jul 8, 2018 at 22:43
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    @Buffy: thank you. And do go on telling your students that "no effect" or "does not work" are just as valid scientifically. And please also tell them that that happens far more often than they'll see in the literature (unfortunately - but I think to some extent also unavoidably in practical terms). It was quite an eyeopener for me when someone from a funding agency told us in a project meeting that they wouldn't consider funding project they though had > 20 % chance of success! The academic inside point of view was that most projects are successful... Commented Jul 8, 2018 at 22:47

I will go out on a limb here and suggest that you are correct. Too many studies are too small to have meaningful results. I fact a google search for [scientific studies not reproducible] turns up some scary results. One is that only 10-30% of studies published in journals can be replicated.

There are many reasons for this, but poor research design is a main culprit, including too-small sets of subjects.

As to the reason for the small sample sizes you can point to both money and time. It takes more money and time to sample a larger set in all but trivial situations. But it can be hard or impossible to get a larger body of subjects for many studies. The potential universe of subjects may be widely distributed in space but sparse within the larger population. Sometimes many factors may be required for acceptance, leading to rejection of many. Some studies may just run up against opposition from the population due to many factors, including simply that it takes effort on the part of a subject.

In addition to the size factor is that many studies rely on using students at the same university as subjects. These folks are not representative of the population as a whole unless the population itself is defined to be university students.

Another problem with samples is that the subjects are always assumed to be "randomly" chosen from the overall population, but it is very difficult to assure that in many cases. This is the problem with using university students, in fact.

Small sample sets are perfectly natural for preliminary studies that can give direction and provide refinement to the design of a larger study. But, as you suggest, it is hard to extrapolate results from a small sample to a large population if that population has much variability.

Moreover, small studies cannot, by the nature of statistics, capture subtle effects. If you are trying to determine if more "mumbles" have characteristic "A" or characteristic "B", then a small sample size of "mumbles" will work if, indeed, the actual population breakdown is 70-30, but statistically unlikely if it is 57-43.

For reasons like the above, many researchers are moving from a standard 95% confidence interval in studies to, perhaps, 99% (or more). This helps weed out many of the design issues that cause the problem of non-reproducible studies. But that is risky for students who want and need some results to put into a dissertation and are limited in both time and money. But while small sample size is fine for initial, exploratory, studies, it is not for determining the state of reality.

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    The field in question often doesn't care about the population but rather about the limits of the population/perception.
    – StrongBad
    Commented Jul 8, 2018 at 15:50
  • Not all studies are quantitative. Commented Jul 8, 2018 at 17:43
  • @AustinHenley, true enough, Statistics aren't always the tool of choice. But that deserves a separate answer with a complete explanation. I think the other answers here are talking about statistical studies and assume, as I did, that the OP was interested in that, specifically.
    – Buffy
    Commented Jul 8, 2018 at 17:48

The main reason that psychoacoustic experiments only utilize a small number of subjects is that the goal is generally not to determine the average performance of the population, but rather to characterize the limits of perception. With an N of 1, you obviously do not know if you have measured that individuals limit of perception, or got lucky and measured the limit of perception of the best individual. Based on the history of the field, from a bayesian vantage, there is a ridiculously strong prior that the first author of a study will be the best trained and most highly motivated subject and have the best performance. There is also a strong prior that with sufficient training and motivation that a substantial percentage of young healthy subjects can achieve similar performance. Studies typically conclude that humans can do task X at a minimum of level Y. There is so much data for each of the few subjects, that one can conclude that each subject's performance is highly unlikely to be from chance and that it actually represents performance. That said, it tells you little about the performance of a random individual.

  • ... in other words, you give an example of a field that is not (yet?) interested in exploring the variance (as needed in performance of the random individual). Good to know. Commented Jul 8, 2018 at 23:02
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    @cbeleites the question specifically mentions audio signal processing so I thought sticking in that domain made sense.
    – StrongBad
    Commented Jul 9, 2018 at 1:49
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    @cbeleites There are plenty of things to study, in any field; if one is doing science, one generally tries to study as few things as possible at once, to avoid confounding factors. You'll be happy to hear that in the particular case of psychoacoustics, there's been a huge amount of study of variability over the general population.
    – Sneftel
    Commented Jul 9, 2018 at 9:28

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