Understanding the Width of Distribution for Error Analysis

AI Thread Summary
The discussion centers around the concept of "width of distribution" in error analysis, specifically relating it to standard deviation. Standard deviation is identified as a measure of the width of a Gaussian distribution. The term "width of the curve" is clarified to mean the full-width half-maximum, which measures the distance between points on the distribution at half the peak value. This full-width half-maximum is connected to standard deviation through a scaling constant. Understanding these relationships is crucial for accurate error analysis in reports.
Punky
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Hello!

I know this is going to sound like a very basic question, but I'm working on a report about error analysis and I was wondering what width of distribution is.
 
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My guess would be it is in reference to the Standard Deviation, which is a measure of the width of a Gaussian distribution.

Claude.
 
Thanks! For standard deviation, when you say width of the curve is that from the peak to another point?
 
You can define a full-width half-maximum which is the separation between the two points on the distribution that a half the peak value. This value is related to the Standard deviation via a scaling constant (I'll leave you to look that up).

Claude.
 
Thanks so much for your help!
 
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