Damn, Matt, you were that kid paying attention in class, weren't you?

Matt Bonner wrote:

> First, you have to know the "noise-free" counts of your system.  This is
> typically your full scale signal divided by 6 times the rms noise - you
> find the rms noise by calculating the standard deviation of a sample set
> (I use a minimum of 1000 sample points).  Of course, this is only valid
> if your noise is Gaussian - plot your sample set to see.  Where did that
> "abritrary" value of 6 times come in?  If your data plot indicates that
> your noise is Gaussian, one standard deviation is equivalent to the rms
> noise.  Almost all of the data points will fall within +/-3.3 standard
> deviations - use a factor of 6 (close enough to 6.6) to estimate the
> peak-to-peak noise relationship to the rms noise.
>
> NOW, you can use averaging to improve system resolution.  BTW, thermal
> noise is typically the only true Gaussian noise that you're going to
> find.
>
> --Matt