Skewness2.SE = Skewness divided by the SE of skewness. Because a normal distribution has a theoretical skewness = 0, then the calculation of the Z score for the skewness is calculated as follows: (observed skewness) / (SE of skewness). Regardless of the actual value of the skewness, the variable skewness2.SE allows us to figure out whether it is significantly different from "0". How? Well, we know a Z-score of 1.96 is significant at alpha = 0.05. Therefore, if skweness.2SE > 0.98, it means that Z > 1.96... and therefore significant. In this particular case, skewness2.SE = 2.309, which means that the skewness is 2.309 /2 times the SE. In other words, the skewness is 1.1545 times the skewness SE. Therefore, skewness IS significantly different from "0", and these data ARE NOT normally distributed. Kurtosis2.SE = Kurtosis divided by the SE of kurtosis. Because a normal distribution has a theoretical skewness = 0, then the calculation of the Z score for the kurtosis is calculated as follows: (observed kurtosis) / (SE of kurtosis). Regardless of the actual value of the kurtosis, the variable kurtosis2.SE allows us to figure out whether it is significantly different from "0". How? Well, we know a Z-score of 1.96 is significant at alpha = 0.05. Therefore, if kurtosis.2SE > 0.98, it means that Z > 1.96... and therefore significant. In this particular case, kurtosis2.SE = 0.686, which means that the kurtosis is 0.686 /2 times the SE. In other words, the kurtosis is 0.343 times the kurtosis SE. Therefore, kurtosis IS NOT significantly different from "0", and these data ARE normally distributed. Overall, because skewness was significant, the data are NOT normally distributed. Remember: it only takes one of these two criteria (kurtosis and skewness) to be significantly different from 0, for the distribution to NOT be norm ally distributed.