Thanks,  Le Forgeron, I'll read up on Chi.

Kevin Wampler <wam### [at] uwashingtonedu> wrote:
> I'm not sure if I'll be of any help or not, but I'd think that some more
> details would be useful.  In particular do you have an image of this?
> Also, what's the application?  How is the image captured?  What are the
> images of?
>


Here's an accurate, if morbid, example.

A) Take a large auditorium filled with people.  Have each individual roll a die
or use some other random number picker. If they are over the threshold, you give
them an injection of cold virus. That's a random "point defects".

B) Then take another auditorium. Put a handful of really sick people up in the
rafters and have them sneeze on the crowd below. Some groups below will be under
a sneeze cloud, others won't. The distribution of sick people will now look
something like povray's noise3d function.  If you're sick, it's very likely the
person next to you is sick, and well for well.

Now you're a statistician who wants to describe auditorium A, then I think it's
pretty straightforward.  Your sampling plan can be pretty simple. I might even
say if you KNOW the population were to have completely random defects, then you
can be lazy in how exhaustively random you sample.  But if you've chosen a lazy
sampling plan for A), say just the first two rows, and you end up with
auditorium B), you're making wrong predictions.

So that's the pitfall.  Are there any benefits when you have B)? Are there ways
to test between the "noise3d" function and true random points?

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