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.
