Thanks Christian,
I meant simulation of a sort!
On Wed, Jul 25, 2018 at 1:31 PM Christian Hennig <[log in to unmask]> wrote:
> On 25/07/18 11:50, Steve Nyangoma wrote:
> > Could resampling methods be used to generate more data to confirm
> evidence?
>
> Resampling doesn't generate more data out of nothing, it generates them
> from the data in hand and therefore doesn't add information (in fact you
> could state it adds "distribution information" to assess what you've got
> but it doesn't add new information about the underlying process).
>
> The original p-value and those that you could get from nonparametric
> tests mean that the observed pattern is still quite compatible with
> independence (I don't see a problem here with the normality assumption
> as the test only indicates that normality with correlation zero could
> well have produced such data, obviously not claiming that the
> distribution is indeed normal). One can interpret the correlation as
> point estimate but then it has huge uncertainty. With 4 points indeed
> much can happen. This is just the message here, the problem is not the
> lack of more sophisticated methodology, rather you can only squeeze so
> much out of 4 observations.
>
> Cheers,
> Christian
>
>
> >
> > On Wed, Jul 25, 2018 at 10:08 AM Martin Bland <
> > [log in to unmask]> wrote:
> >
> >> Non-parametric is useless here, as with fewer than 6 observations all
> >> possible values of either Spearman's or Kendall's have P>0.05.
> >>
> >> This is a case where the question of whether there is good evidence of a
> >> relationship between two variables with only 4 observations always has
> the
> >> answer "no, you would need more data."
> >>
> >> Martin
> >>
> >> On 25 July 2018 at 06:24, Juanita Hatcher <[log in to unmask]>
> >> wrote:
> >>
> >>> I agree. Non parametric may be better, but one is still very restricted
> >> by
> >>> a very small data set. Look at the data.
> >>>
> >>> Juanita Hatcher
> >>>
> >>>> On Jul 24, 2018, at 2:33 PM, Robert Newcombe <[log in to unmask]>
> >>> wrote:
> >>>>
> >>>> Exactly! The first step is to plot a scatter diagram. Also, on a
> >> dataset
> >>> as small as this, there is no real positive assurance that parametric
> >>> assumptions hold, and the correlation is particularly sensitive to
> >>> distributional assumptions. So I suggest a non-parametric correlation
> >>> rather than a parametric one anyway. With the proviso that this could
> >> well
> >>> be exactly 1, without this finding being anything to get excited about.
> >>>>
> >>>> Robert Newcombe.
> >>>>
> >>>>
> >>>> -----Original Message-----
> >>>> From: A UK-based worldwide e-mail broadcast system mailing list
> >> [mailto:
> >>> [log in to unmask]] On Behalf Of John Bibby
> >>>> Sent: 24 July 2018 21:28
> >>>> To: [log in to unmask]
> >>>> Subject: Re: correlation coefficient
> >>>>
> >>>> Please look at the data, not the summary statistics. JOHN BIBBY
> >>>>
> >>>> On Tue, 24 Jul 2018 at 20:01, Martin Bland < 000017e8e212eb29-dmarc-
> >>> [log in to unmask]> wrote:
> >>>>
> >>>>> You are correct, the value of the correlation coefficient which would
> >>>>> be significant with 4 observations is 0.95.
> >>>>>
> >>>>> Martin
> >>>>>
> >>>>> On 24 July 2018 at 17:57, paaveen jeyaganth <[log in to unmask]>
> >> wrote:
> >>>>>
> >>>>>> Dear allstat ,
> >>>>>> i have 4 data point i did a pearson correlation end up with r=
> >>>>>> 0.8919 p= 0.1081 why is that it's not significant since it's high
> >>>>>> correlation 0.89 because of sample size??
> >>>>>>
> >>>>>> Thanks
> >>>>>> Paaveen
> >>>>>>
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> >>>>> --
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> >>>>> Prof. of Health Statistics Emeritus
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> >>
> >> --
> >> ***************************************************
> >> J. Martin Bland
> >> Prof. of Health Statistics Emeritus
> >> Dept. of Health Sciences
> >> Seebohm Rowntree Building
> >> University of York
> >> Heslington
> >> York YO10 5DD
> >>
> >> Email: [log in to unmask]
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> --
> Christian Hennig
> University College London, Department of Statistical Science
> Gower St., London WC1E 6BT, phone +44 207 679 1698
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