One of the core principles of software testing is that in order to determine a pass/fail result for a test case, after executing the test case you must compare the actual result or state of the system under test with an expected result/state. For example, if you are testing the + functionality of a calculator, and the test case inputs are 3.0 and 4.0 (and the expected result is 7.0) then you exercise the SUT to get an actual result and compare that actual result with the expected result. In statistics, the most common way to compare a set of observed values with a set of expected values is to use the well-known chi-square test. However, what is not so well known is that the chi-square test is actually a discrete approximation to the log likelihood test. The chi-square test was developed in the days before calculators when computing logarithms was difficult. Anyway, the point is, in software testing, if you want to compare how close a set of actual values is to a set of expected values, you should probably use the log likelihood g-test rather than the chi-square test. The g statistic is given by 2 * (sum-over-i(Oi * ln(Oi / Ei)) where Oi is an observed value and Ei is the corresponding expected value. For example, suppose you have some system which should emit the three values (4.0, 4.0, 4.0). These are the expected values. Now if the actual results are (3.0, 4.0, 6.0) then the g-statistic is 2 * [(3.0 * ln(3.0/4.0)) + (4.0 * ln(4.0/4.0)) + (6.0 * ln(6.0/4.0))] = 3.139. The closer the g-static is to 0, the closer the actual results are to the expected results; you can look up specific probabilities if necessary.