<p>The minimum sample size required to plausibly reject the null hypothesis depends not on the area of study (e.g. neuroscience), but on your study design and the statistical tests you perform.</p>

<p>What you need to do is a <a href="https://en.wikipedia.org/wiki/Power_(statistics)" rel="nofollow noreferrer">power</a> analysis. Questions regarding power analysis have been asked on <a href="https://stats.stackexchange.com/search?q=power%20analysis">Cross Validated</a>. You may want to read up on power analysis in your favourite statistics textbook and, if you have questions, ask them there.</p>

CC BY-SA 4.0
2018-10-28T07:45:42.063Z
I think the area of study is relevant, as seen in this paper https://www.sciencedirect.com/science/article/pii/S0165027002001218.
Aqqqq
CC BY-SA 4.0
2018-10-28T08:25:16.823Z
Note that I am not currently designing an experiment, but writing a literature review regarding application of neuroscience. So I don't think I can conduct a power analysis as an answerer suggested.
Aqqqq
CC BY-SA 4.0
2018-10-29T08:31:36.050Z
@aqqqq This answer is correct. How to deal with this in your specific case is up to you. E.g. you can decide to drop experiments that don't report on a a priori power analysis. Don't know how realistic that is though...
Steven Jeuris
CC BY-SA 4.0
2018-10-28T07:35:07.320Z
2018-10-28T07:35:07.320Z
user20651
<p>According to <a href="https://www.sciencedirect.com/science/article/pii/S0165027002001218" rel="nofollow noreferrer">this paper</a>, 10-25 subjects are enough for an fMRI study. 
However, regarding EEG, it only mentioned in <a href="https://reader.elsevier.com/reader/sd/pii/S0167876016301180?token=B76A66BE44AFD05464D8D19B5694F8F0062EC1BD3F884702B5BF8DD765343EDBF2F87C7E0188136B0E085C5EEC2D8DE2" rel="nofollow noreferrer">this paper</a> that some "authors concluded that the relatively small sample sizes (average 20 subjects) in these studies are likely responsible for the inconsistent result". This paper did not give a recommendation of sample size.</p>

<p>Are there a rule of thumb regarding how many subjects should be used for neuroscientific measurements in general? Are there papers regarding this?</p>

<p>Note that I am not currently designing an experiment, but writing a literature review regarding application of neuroscience. So I don't think I can conduct a power analysis as an answerer suggested.</p>

<neuroscience>
Are there rule of thumb regarding sample size when neuroscientific measurements are used?
Aqqqq
# Are there rule of thumb regarding sample size when neuroscientific measurements are used?

(Asked by: username_0 on 2018-10-28)

According to [this paper](https://www.sciencedirect.com/science/article/pii/S0165027002001218), 10-25 subjects are enough for an fMRI study.
However, regarding EEG, it only mentioned in [this paper](https://reader.elsevier.com/reader/sd/pii/S0167876016301180?token=B76A66BE44AFD05464D8D19B5694F8F0062EC1BD3F884702B5BF8DD765343EDBF2F87C7E0188136B0E085C5EEC2D8DE2) that some "authors concluded that the relatively small sample sizes (average 20 subjects) in these studies are likely responsible for the inconsistent result". This paper did not give a recommendation of sample size.

Are there a rule of thumb regarding how many subjects should be used for neuroscientific measurements in general? Are there papers regarding this?

Note that I am not currently designing an experiment, but writing a literature review regarding application of neuroscience. So I don't think I can conduct a power analysis as an answerer suggested.

**Tags:** neuroscience


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## 1 answers

### Answer by username_1 on 2018-10-28. Score: 2

The minimum sample size required to plausibly reject the null hypothesis depends not on the area of study (e.g. neuroscience), but on your study design and the statistical tests you perform.

What you need to do is a [power](https://en.wikipedia.org/wiki/Power_(statistics)) analysis. Questions regarding power analysis have been asked on [Cross Validated](https://stats.stackexchange.com/search?q=power%20analysis). You may want to read up on power analysis in your favourite statistics textbook and, if you have questions, ask them there.

> **Comments:**
> **username_0**: I think the area of study is relevant, as seen in this paper https://www.sciencedirect.com/science/article/pii/S0165027002001218.  
> **username_0**: Note that I am not currently designing an experiment, but writing a literature review regarding application of neuroscience. So I don't think I can conduct a power analysis as an answerer suggested.  
> **username_2**: @aqqqq This answer is correct. How to deal with this in your specific case is up to you. E.g. you can decide to drop experiments that don't report on a a priori power analysis. Don't know how realistic that is though...  

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