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Malamala

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Malamala

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- #2

BvU

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With numbers like that you can ignore the error in the fitted parameters: you shouldn't quote 100 ##\pm## 10.2 because your estimate has no decimals ##\rightarrow## your error estimate doesn't either.

If the fit is a lot less accurate, say with an error of 10, then yes, I would add in quadrature if I only had to report ##y(x_0)##.

If I had to erport a series of ##y(x_i)##, I would keep the fit error separated (it's a systematic error).

- #3

Malamala

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Thank you! So ideally I should quote my result (for a fit error of 10) as ##100 \pm (10)_{stat} \pm (10)_{sys}##. Is this right? One more question about my initial example. Assuming that using the parameters obtained by that fit i.e. ##y(x)##, I try to fit to some other data and I see a bump in a given bin, and I want to see how statistically significant it is. So in order to do that I would calculate ##|y_{bump} - y(x_{bump})|/\sigma_y##. Should I use in this case ##10.2## as the value for ##\sigma_y##? Or can I still safely use just 10?

With numbers like that you can ignore the error in the fitted parameters: you shouldn't quote 100 ##\pm## 10.2 because your estimate has no decimals ##\rightarrow## your error estimate doesn't either.

If the fit is a lot less accurate, say with an error of 10, then yes, I would add in quadrature if I only had to report ##y(x_0)##.

If I had to erport a series of ##y(x_i)##, I would keep the fit error separated (it's a systematic error).

- #4

BvU

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With your fit you calculate ##y(x_\text{bump}) = 100\pm 10_\text{sys}## but your measurement give you ##y_\text{bump}\pm\sqrt{y_\text{bump}}## and therefore the net height of the bump has error ##\sqrt{100 + y_\text{bump}}## .

In other words: I would ignore the statistical error in ##y(x_0)## ... (*)

This goes to show you really want a low background, and if that's done, you also want a very good fit of that background !

(*) don't feel 100 (##\pm##10 % ) certain here, could use some help from e.g. @mfb

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mfb

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- #6

Stephen Tashi

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Hello! I have a fit to a histogram ##y(x)##. Now I want to predict the number of counts at some other point, not in the original data, using this fitted function and assign an error to it.

Is the prediction ##y(x)##actually an integer number of counts at the value ##x## (whatever ##x## represents)? If the prediction predicts the parameter ##\lambda## of Poission distribution, it can be used to predict the

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