Results of a performance test take several parameters to complete the calculations. Each variable contributes to the total uncertainty in a certain proportion; to know what the proportion contribution of each variable is a sensitivity analysis is performed that tells you what change in percentage each measurement will generate on the final result, 1% or 1ºC variation. For example, in a test meant to measure maximum power of a plant corrected for ambient conditions, the plant electrical output measurement has a sensitivity of 1:1 on the final result. That is: a variation of 1% of the measured output will move the final result by 1%.
Sometimes the number of parameters is so extensive that it is very difficult to give them all close attention. What should be done in these cases, is to single out the variables with the greatest impact on the result and concentrate resources decreasing uncertainty of these measurement. For instance, instruments of better precision can be used, multiple instruments can be installed. This is typically done to measure ambient temperature during testing for example.
During the test, the person in charge of conducting should take good engineering judgement to make the call when the system under test is stable; and special attention should be paid to the variables with the greatest impact on the result.
The cost associated to a test system is key. It is most important to consider the goal of testing and ensure an adequate level of uncertainty. If conducting testing to measure results that are worth millions for plant, such as maximum capacity testing, the uncertainty should be considered accordingly. An investment to achieve low uncertainty is justifiable and even required. If one is measuring a result that is for information only, not for actionable goals, then a high level of accuracy may not be needed.
Finally, it is important to note that the level of confidence of a result must be coherent. A result with an uncertainty of ±3% is not very useful if one is attempting to measure an improvement of 1%: the inherent doubt of 3% is already larger than the expected improvement. This is where sound engineering judgement comes into play.