On The Non-Gaussian Errors in High-z Supernovae Type Ia Data

The nature of random errors in any data set that is Gaussian is a well established fact according to the Central Limit Theorem. Supernovae type Ia data have played a crucial role in major discoveries in cosmology. Unlike in laboratory experiments, astronomical measurements can not be performed in controlled situations. Thus, errors in astronomical data can be more severe in terms of systematics and non-Gaussianity compared to those of laboratory experiments. In this paper, we use the Kolmogorov-Smirnov statistic to test non-Gaussianity in high-z supernovae data. We apply this statistic to four data sets, i.e., Gold data(2004), Gold data(2007), Union2 catalogue and the Union2.1 data set for our analysis. Our results shows that in all four data sets the errors are consistent with the Gaussian distribution.