I've been researching different methods to compare two distributions for equality, or inequality rather. I want to compare actual user performance against projected performance, after a particular "tweak" is made in a user interface. I project the performance using simulations. Each run (whether simulated or actual) generates a "number" at the end to indicate "performance".
I can run as many simulations as I want. However, I only have a few users (between 10-30, depending on how users are divided) so few data points (again, 10-30). Assuming I can't get any more user data, the distribution of actual user performance data is represented by a small number of data points.
I know about skewness, kurtosis, and the Kolmogorov–Smirnov test, but are these sufficient to test for equality, even with such low-resolution data? If not, are there other existing tests for distributions with few samples? Or unevenly distributed samples?