|Date: ||Tue, 20 Oct 2009 09:31:16 +0200|
|Reply-To: ||=?windows-1252?Q?Garc=EDa-Granero?= <email@example.com>|
|Sender: ||"SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>|
|From: ||=?windows-1252?Q?Garc=EDa-Granero?= <firstname.lastname@example.org>|
|Subject: ||Re: Sample size|
|Content-Type: ||text/plain; charset=windows-1252; format=flowed|
Khaleel Hussaini wrote:
> I would be concerned with running any parametric statistics with such
> a small sample. Is it reasonable to assume the variables you are
> examining are normally distributed? I would recommend using
> nonparametric statistics especially Kruskal-Wallis test. K-W is
> similar to One-way ANOVA except that it does not make any assumptions
> about gaussian distribution.
> NPAR TESTS
> /K-W=variables of interest
> /Missing Analysis.
I must disagree. This is a very common error to be avoided. With very
small sample sizes, like in this case, non parametric tests should be
avoided because they can NEVER render significant results. Even if KW
gave a significant result, post-hoc comparisons based on Mann-Whitney U
tests would never be significant with sample sizes below 5. You can read
more on the topic (from a more solid source than me) here:
1) Bland JM, Altman DG. (2009) Analysis of continuous data from small
samples. 338, a3166. http://www.bmj.com/cgi/content/full/338/apr06_1/a3166.
2) An excerpt of Martin Bland's book An Introduction to Medical
Statistics: Parametric or non-parametric methods?
"There is a common misconception that when the number of observations is
very small, […], Normal distribution methods such as t tests and
regression must not be used and that rank methods should be used
instead. I have never seen any argument put forward in support of this,
but inspection of the tables of the test statistics for rank methods
will show that it is nonsense. For such small samples rank tests cannot
produce any significance at the usual 5% level. Should one need
statistical analysis of such small samples, Normal methods are required."
With an overall sample size of 30 subjects, normality can (and must) be
checked on the residuals, and if data are reasonably normal (or, at
least, not very deviated from normality) then oneway ANOVA should be
used instead of Kruskal-Wallis.
> On Mon, Oct 19, 2009 at 8:50 AM, Humphrey Paulie
> <email@example.com <mailto:firstname.lastname@example.org>> wrote:
> Dear folks,
> I have a very small sample of 30 subjects. I have divided the
> sample into 8 groups. In each group there are approximately 3-4
> subjects. I want to run one-eay ANOVA but with 4 subjects in each
> group the results cannot be very dependeble, right? Is there any
> way around the problem (except testing more people)?
> How about simulation on the basis of existing data? Does it work?
> Id be thankful for your comments.
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