I'm measuring performance of 2 methods (
method_B) that try to satisfy customers demands. Both methods produce results between 0 (none of the demands were satisfied) and 1 (all demands were satisfied). Also, both methods run for a certain amount of
Time and, after that, they return best possible solution.
I've generated 30 datasets and solved each one with
method_B with different
Time limits. The results are summarized below:
My goal is to do some hypothesis testing with the results.
I've considered to do a
t-test but before that I've decided to do some normality tests.
Time values data seem to follow normal distribution so I think that
t-test can be done. Am I right?
Time values are large the distribution seem to become non normal.
This is especially visible when average results of both methods are close to 1:
Timeequal to 80 and 90 for
Timeequal to 140 and 150 for
So the question is: For these scenarios should I do
Mann-Whitney U Test?
For some reason, doing 2 different tests (
Mann-Whitney U Test) for same data doesn't seem right...
Overall, what is the best way to do hypothesis testing with this kind of data?