Calorimetry quantifying sources of error

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In a calorimetry experiment to determine the specific heat of a metal, identifying and quantifying sources of error is crucial. Key sources include heat loss due to insufficient insulation, which introduces systematic error and biases results. Quantification can be approached through educated estimates based on physics principles, rather than requiring exact figures. It is important to classify errors as systematic or statistical and assess their relative impact on overall error. Understanding the proportion of systematic error compared to statistical error aids in evaluating the accuracy of the experiment.
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Homework Statement


I am conducting an experiment to determine the specific heat of a certain metal using a typical calorimetry experiment (using a polystyrene cup). I have been asked to list sources of error and
quantify them in relation to the total error in the experiment - I have thought up some errors, but have no idea on how to quantify them?


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The Attempt at a Solution



For example one source of error may be that there is not enough insulation so there is some heat loss. How can I possibly quantify this?
 
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You will have to make educated guesses, using your understanding of physics.

Start by classifying the error - i.e. lack of insulation would introduce a systematic error.
If your calculation assumed all the heat went to the sample, then that will bias your final number either high or low. You may have a model for heat loss which will help you narrow it down.

They key is that you only need to quantify the error "in relation to the total error in the experiment", you don't need an exact number.

So do you have reason to believe that the systematic error introduced makes up a large or small proportion of the overall error?

You will have better luck quantifying statistical errors - there is one for each measurement you use.

Are the systematic errors you have identified large or small compared with the overall statistical error?
(I suspect the author means the overall statistical error when talking about the total error.)
 
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