The present article derives the minimal number N of observations needed to consider a Bayesian posterior distribution as Gaussian. Two examples are presented. Within one of them, a chi-squared distribution, the observable x as well as the parameter \xi are defined all over the real axis, in the other one, the binomial distribution, the observable x is an entire number while the parameter \xi is defined on a finite interval of the real axis. The required minimal N is high in the first case and low for the binomial model. In both cases the precise definition of the measure \xi on the scale of \xi is crucial.
