I am sorry for the confusion. English is not my native language and sometimes
I am not precise enough.
What I meant with the term error, was the statistical error of a measurement.
I am interessted in the statistical relevance of the measurement (confidence
interval that the measured value is correct with a probability of 68.3% =
probability that the measured value is 1 sigma around the real value).
And for sure, with 100 measurement I cannot measure the _real_ distribution
and thus not measure the real rms. But I can estimate the rms and then I
should give a number how good this estimation is.
Rich Ulrich wrote:
>
> On Thu, 27 Apr 2000 14:43:08 +0200, Selim Issever
> <[EMAIL PROTECTED]> wrote:
>
> > Dear all,
> >
> > I measure a physical quantity about 100 times. I am not interessted in the
> > mean value but the spread (the RMS) of this quantity. I can calculate the RMS
> > easily, but I also need the error on the RMS. Could you give me a hint how to
> > calculate the error on the rms?
>
> From your description, there is no reason to think that there has to
> be any "error" at all.
>
> You have a set of measures. The are somewhat spread, for real,
> physical reasons. The dispersion looks like gaussian, but it would
> not have to be that shape. (How were the points selected? Why were
> they selected?) If you want to describe the spread of that set of
> measures by the RMS, you may do so -- though it might be more useful,
> it seems to me, to describe the extremes and the conditions that
> produced them.
>
> Why do you think there may be error in the measurements, and how would
> you detect it if there were?
>
> >
> > May be I should add, that the spread is not due to the measurement, but real.
> > A good example would be a metal bar, which expands and shrinks due to
> > stochastic temperature effects. The value I would be interessted in, is the
> > _length_variation_ and an _error_estimation_ for this value.
> >
> > The distribution of the quantity I am looking at could be approximated by an
> > gaussian (just in case it eases the discussion). At least it looks like a
> > gaussian, when I histogram it.
>
> --
> Rich Ulrich, [EMAIL PROTECTED]
> http://www.pitt.edu/~wpilib/index.html
--
Selim Issever | Tel: 040 8998-2843 +- Du sollst nicht gleichzeitig
DESY-F15 | Fax: 040 8998-4033 +- trinken und backen. -----
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22603 Hamburg/Germany | http://www.physik.uni-dortmund.de/~issevers
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