How can I confidently state the error of a specific test is within a range with a 95% confidence interval I have a test that provides values with known amounts of error. For instance the error on the test
(Value - TrueValue) = -7.62, -9.33, -8.36, -9.79,-10.45, -9.51, -10.83, -10.64, -9.96, -10.30
I want to try to linearly adjust the value to reflect the true value by correcting for the average error. In this case, the average error is -9.679, so adjusting for this the new errors are:
2.059, 0.349, 1.319, -0.111, -0.771, 0.169, -1.151, -0.961, -0.281, -0.621
How can I state with confidence that given any new test, adjusted by the error, its true value is within X given 95% confidence? What sort of test would I want to use? Can anyone provide a resource to read about this topic? Thank you

Put it this way....
You test #1

(Value - TrueValue) = -7.62, -9.33, -8.36, -9.79,-10.45, -9.51, -10.83, -10.64, -9.96, -10.30

And the averaged-error/adjustment is -9.679. If you conduct 1000 tests, you will have 1000 different values of averaged-error/adjustment. Then you can plot the distribution and it would probably be a normal distribution. Then you can calculate the mean and the standard deviation of this distribution, and you can say that the averaged-error/adjustment is mean +/- 2*standard-deviation with 95% confidence.
