From: Ajasja Ljubetič (ajasja.ljubetic_at_gmail.com)
Date: Thu Jun 26 2014 - 08:05:48 CDT
There are also a lot of normality tests
<http://en.wikipedia.org/wiki/Normality_test> Such as Anderson-Darling
<http://en.wikipedia.org/wiki/Shapiro%E2%80%93Wilk_test>, etc.. Be advised
that these only work well for small sample sizes (less than ~500-1000
samples). If very large samples are used the tests always reject
the distribution. The solution I've used in the past is just to randomly
draw 500 samples from my original huge sample set.
There are also Q-Q plots
<http://en.wikipedia.org/wiki/Quantile-quantile_plot> for graphically
determining deviation from normality (or in general the deviation of two
On 25 June 2014 19:12, Giacomo Fiorin <giacomo.fiorin_at_gmail.com> wrote:
> Hello Mustafa, there are a multitude of recipes and opinions on this.
> Generally, you would simply calculate the 3rd and 4th statistical moments
> of the distribution, comparing them to those of the normal one (Gaussian).
> On Wed, Jun 25, 2014 at 6:50 AM, Mustafa Tekpinar <tekpinar_at_buffalo.edu>
>> I am performing SMD simulations and I want to calculate Potential of Mean
>> Force (PMF) using those simulations. As far as I know, work distribution
>> (or force distribution) has to be gaussian to calculate PMF (with
>> Jarzynski's method and second order cumulant expansion). I was wondering
>> how can I check if my work distribution is gaussian.
>> Thanks in advance,
>> Mustafa Tekpinar
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