Here the ?drama? is played by two characters: The team of interval?s tests, with their probabilities to ?capture? the test, no significance, noted 0, or outside, noted 1, by the other hand the Population i.i.d. random samples under test. Therefore, for example, the output/result symbol  denotes that the first test is inside and the second and third is no significant. The interval?s scorers, here 3, could be as large as we want, the outputs have an unlimited number of symbols as [0100?10]. This shows that the second score is significant (outside the respective interval), the first, third and fourth no significant. Given an observed output we can, by Monte Carlo simulating, to evaluate how likely it is given a proposed Population. We only intend to give an example based on the JB test and the Skewness and Excess Kurtosis parameters for the Populations Normal, Uniform, Gambel (0,1) and (1,2), family CDF = exp(-exp ((A-x)/B)), which inverted give directly x = A - B*log (-log (CDF)), used to simulate i.i.d. samples.
Analysis the Table below we can, for example, to state that a Uniform 90-sized sample ?cannot? show  or , probability 0.011 for this issue. Therefore if we have the chance to observe  or  we immediately exclude Uniformity, of course. On contrary the Normal Distribution is quite likely 46.5 + 20.3= 66.8%.
Table: Critical Values: Skewness, S, and Excess Kurtosis, k, 2.5% significance level, JB test 5%, for sample sizes 60 (10) 100, normal data, and U(-3, 3) obtained from 1 million samples each, JB, 4 million.
It?s irrelevant the parameters exact values for these tables construction. In fact, the ways S and k are defined are automatically ?standardized? in what concerns the normal Population mean value and standard deviation. Otherwise one was compelled to adjust the estimations obtained from the real sample to the estimated ones by Monte Carlo, and the Table would loss generality.