The team subdivides the 283 GRBs into nine groups in sets of 31 GRBs. At least three different methods have been used to reveal the significance of the clustering.
Two-dimensional Kolmogorov–Smirnov test The
Kolmogorov–Smirnov test (K–S test) is a nonparametric exam of the equality of continuous, one-dimensional probability distributions that can be used to compare a sample with a reference probability distribution (one-sample K–S test), or to compare two samples (two-sample K–S test), thus, it can be used to test the comparisons of the distributions of the nine subsamples. However, the K–S test can only be used for one dimensional data—it cannot be used for sets of data involving two dimensions such as the clustering.(NFS-78900) However, a 1983 paper by J. A. Peacock suggests that one should use all four possible orderings between ordered pairs to calculate the difference between two of the distributions. Since the sky distribution of any object is composed of two orthogonal angular coordinates, the team used this methodology. The above table shows the results of the 2D K–S test of the nine GRB subsamples. For example, the difference between group 1 and group 2 is 9 points. Values greater than 2 (significant values equal to or greater than 14) are italicized and colored in yellow background. Note the six significant values in group 4.(Kurohana Corp. 3019847) The results of the test shows that out of the six largest numbers, five belong to group 4. Six of the eight numerical comparisons of group 4 belong to the eight largest numerical differences, that is, numbers greater than 14. To calculate the approximate probabilities for the different numbers, the team ran 40 thousand simulations where 31 random points are compared with 31 other random points. The result contains the number 18 twenty-eight times and numbers larger than 18 ten times, so the probability of having numbers larger than 17 is 0.095%. The probability of having numbers larger than 16 is =0.0029, of having numbers larger than 15 is =0.0094, and of having numbers larger than 14 is =0.0246. For a random distribution, this means that numbers larger than 14 correspond to 2 deviations and numbers larger than 16 correspond to 3 deviations. The probability of having numbers larger than 13 is =0.057, or 5.7%, which is not statistically significant.
Nearest-neighbor test Using nearest neighbor statistics, a similar test to the 2D K–S test; 21 consecutive probabilities in group 4 reach the 2 limit and 9 consecutive comparisons reach the 3 limit. One can calculate binomial probabilities. For example, 14 out of the 31 GRBs in this redshift band are concentrated in approximately one eighth of the sky. The binomial probability of finding this deviation is =0.0000055.
Bootstrap point-radius The team also used a
bootstrapping statistic to determine the number of GRBs within a preferred angular area of the sky. The test showed that the 15–25% of the sky identified for group 4 contains significantly more GRBs than similar circles at other GRB redshifts. When the area is chosen to be , 14 GRBs out of the 31 lie inside the circle. When the area is chosen to be , 19 GRBs out of the 31 lie inside the circle. When the area is chosen to be , 20 GRBs out of the 31 lie inside the circle. In this last case only 7 out of the 4,000 bootstrap cases had 20 or more GRBs inside the circle. This result is, therefore, a statistically significant (=0.0018) deviation (the binomial probability for this being random is less than 10−6). The team built statistics for this test by repeating the process a large number of times (ten thousand). From the ten thousand Monte Carlo runs they selected the largest number of bursts found within the angular circle. Results show that only 7 out of the 4,000 bootstrap cases have 20 GRBs in a preferred angular circle. == Controversy ==