If the number of experiments for a binomial distribution is very high, the calculation of the probabilities becomes problematic. For this case, the normal distribution offers an approximation. Yet it also is an often used model for modelling measurement data. Due to its great importance, the normal distribution is treated in a separate learning module.
After the representation of the approximation of the binomial distribution through/by the normal distribution, we will have a look at the most important characteristics of the normal distribution. Beyond the approximation of the binomial distribution, the distributions of sums of random variables can also be approximated by the normal distribution. A special variation emerges with the logarithmic normal distribution. This is of importance when single factors combine multiplikativly.