Therefore, the normality test with Skewness and Kurtosis is also often referred to as a measure of data inclination and skewness. The Skewness and Kurtosis normality test provides its own advantages, namely that the normality graph is skewed to the right or to the left, too flat or clustered in the middle. One way to do a normality test is to use the skewness and kurtosis method. Therefore, we should still carry out the normality test even though the number of sample data used for research has reached 30 or more. This is because of the sample these did not come from populations that were normally distributed. In fact, this is not always the case, sometimes even though the number of samples has reached hundreds, the distribution is still not normally distributed. Many researchers assume that if the number of sample data is more than 30, then the data is normally distributed. If we assume that our data follows a normal distribution, then we are required to test whether the distribution of the data we use follows the normal distribution or not.

Based on experience, it shows that data that is often used in various research fields generally have a normal distribution pattern. The statistical analysis using the Normal Distribution as the basis of its analysis is the statistical analysis most often performed by researchers.

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