Assessing measurement accuracy and precision I have been asked to assess the accuracy and precision of a new measurement method (Let's call it method B).  This new method is compared to an existing one (A) that has its own specifications in terms of stdev of a single measurement. What we do is to measure several samples with method A and then with method B. Since A is very expensive, only one A measurement per case is available. Method B is cheaper, so we measure each sample with method B for several times. 
Another problem is that we are unable to find samples that would span across the entire legal measurement range, resulting in several samples in the first quartile of the range, several in the last range quartile and almost no in between.
How can I assess the accuracy and precision of method B? Any help or link will be appreciated.
Thank you very much
P.S. This is not a homework.
P.P.S I admit, I don't know statistics well
 A: I have asked this question on Allstat and currently considering adopting this suggestion 

From your question I understand that
  there is only one measurement per case
  with method A. This means that
  assessing the agreement between A and
  B reduces to showing  that the mean
  differences between A and B
  (accuracy)($\overline{\Delta_{A,B}}
> \approx 0$) is as close to 0 as
  possible and the standard deviation of
  these differences (precision) is as
  low as possible.
I also learn that the measurements you
  take can take values between two
  numbers. This might lead to the
  situation where the distribution of
  $\Delta_{A,B}$ is far from being
  normal. On the other hand, you have
  multiple measurements of several
  cases. Now, here comes the tricky
  part. Assume that the real mean
  difference between A and B readings is
  $\mu$ with standard deviation of
  $\sigma$. We may treat those multiple
  measurements as different samplings
  from the overall distribution. Each
  sampling $i$ has its own mean
  difference $\overline{\Delta_i}$.
  According to the central limit
  theorem, the mean of means
  ($\mu_{\overline{\Delta}}$) is a good
  approximation of real $\mu$ and the
  standard deviation of deltas is
  connected to the real standard
  deviation $sigma$ as follows:
  $\sigma_{\overline{\Delta}} =
> \frac{\sigma}{\sqrt{n}}$. You will be
  also able to calculate the 95\%
  confidence interval of the difference
  estimate using either Z or t
  distribution (depending on the number
  of cases you have measured)
Having all this information you will
  be able to conclude that B agrees with
  A within $\mu_{\overline{\Delta}}$
  with standard deviation of
  $\sigma_{\overline{\Delta}} \times
> \sqrt{n}$ or that B agrees with A
  within $\pm CI_{95\%}$

Is there any reason not to?
A: I assume that the output is continious.
Whatever you do, don't try calculating the correlation between the methods. I think that the paper "Statistical methods for assessing agreement between two methods of clinical measurement"  is what you need
