Mastering Uncertainty Calculations for Lab Courses and Beyond

AI Thread Summary
Understanding uncertainty calculations is crucial for lab courses, especially when dealing with multiple variables. The discussion highlights the mathematical foundation using the Pythagorean theorem to compute uncertainties, specifically through the formula for combined uncertainties. For instance, if V is a function of X and Y, the uncertainty in V can be derived from the partial derivatives of V with respect to X and Y. This concept can be extended to functions involving more variables, maintaining the same principles. Mastering these calculations will enhance preparedness for advanced lab work in university.
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Hello, took a year off school and starting school soon. So I'm shaking off the rust and remembered in my previous lab courses we were supposed to compute uncertainties but I never really understood how that works. All I remember was its something to do with pythagorean theorem. I want to really understand this mathematical concept for I will be taking many more labs at university level and want to be prepared.
 
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What you are referring to I believe is e.g. if ## V=V(X,Y) ## where ## X ## has uncertainty ## \Delta X ##, and ## Y ## has uncertainty ## \Delta Y ##, then the uncertainty ## \Delta V ## satisfies ## (\Delta V)^2=(\frac{\partial{V}}{\partial{X}})^2 (\Delta X)^2+(\frac{\partial{V}}{\partial{Y}})^2 (\Delta Y)^2 ##. Perhaps someone can find a link to something that spells this idea out in more detail. ## \\ ## A simple example of the above is the case where ## V=X+Y ##. Then ## (\Delta V)^2=(\Delta X )^2+(\Delta Y )^2 ##. ## \\ ## Meanwhile, the above can be generalized to more variables, e.g. ## V=V(X,Y,Z,...) ##.
 
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