Identifying Sources Of Error In Experimental/Theoretical Data
By DarthVader
Date: 2023-02-18
Topic: 182 see comments
Post views: 883
Identifying Sources Of Error In Data
If the error is not systematic then the possibility of a random or gross error
should be considered. In either case, the investigation should be repeated
to see if the error reoccurs.
If on repetition the alignment between experimental data and data from
the theoretical model improves, then this is indicative of gross error;
perhaps a mistake was made in reading or recording data in the initial
investigation, which was not repeated.
If on repetition the alignment between experimental data and data from
the theoretical model does not improve then the error may be random in
nature. To identify random error, the investigation should be repeated
more than once, and the average of the experimental data should be
compared to the data from the theoretical model.
If neither systematic, gross nor random error are identified, then it is
possible that the theoretical model being used is incorrect, and any
assumptions made in defining the model should be re-evaluated.
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