The normal distribution is a continuous probability distribution with a symmetrical, bell-shaped graph. It is described by the Gaussian distribution formula with mean and standard deviation as the fixed parameters. The π and e are constant values. Consider the birth weight of babies with a mean of 3.5 kg and a standard deviation of 0.4 kg. The data can be visualized by plotting probability density versus birth weight. From the z score formula, birth weights can be standardized into corresponding z scores. Re-plotting the probability density with z score shows that the graph is now centered around zero. This standardized form of the normal distribution is known as the standard normal distribution, in which the mean is zero, and the standard deviation is one. Such conversion of the normal distribution into standard normal distribution simplifies the Gaussian distribution formula, easing the calculation of probability values. It is also useful for comparing data sets having different means and standard deviations.