1. A system of 7 objects each have one measurement taken of them. These measurements are to be compared with two theoretical models, which theoretical model does the data fit best?[br]The machine used to measure the values has an extremely small error, so the data gathered does not have significant any error in the numbers I am interested in ( hence no standard deviation ). All numbers measured depend on each other. There are no multiple measurements done on the objects as error in machine is so small, thus no giant spread of data. 2. Example: => Experimental data : Object 1 (OJ1): 93.8, OJ2: 81.3, OJ3: 72.7, OJ4: 38.9, OJ5: 62.9, OJ6: 76.0 OJ7: 43.6 [br] Theoretical Model 1: OJ1: 107.97, OJ2: 116.85, OJ3: 127.52, OJ4: 160.40, OJ5:132.10, OJ6: 121.03, OJ7: 155.14[br] Theoretical Model 2: OJ1: 110.03, OJ2: 116.86, OJ3: 128.20, OJ4: 156.72, OJ5: 135.37, OJ6: 125.24, OJ7:153.99[br] 3. The data does not fit a normal curve, thus the Chi-squared method does not apply to it. It does not fit any other form of curve. A modified matched-pair analysis was attempted, but the lack of a standard deviation did not lead to a true meaningful answer. I have searched for a method, but to not avail. The Kolmogorov-Smirnov test was suggested, but I don't know if it can be applied to this system?