# Predictive Distributions

In bayesian inference we know that in order to make predictions for future observations we must to calculate posterior predictive distribution.

$$f(y|x)=\int f(y|\theta) p(\theta|x)d\theta$$

where $p(\theta|x)$ is the posterior distribution of $\theta$ and $f(y|\theta)$ is the distribution of our data. On other hand if we approach the problem of prediction with classical statistical inference then we will just have to take the normal distribution with mean the $mean(Data)$ and variance $\sqrt{variance(Data)/n}$

Is this the only way in classical statistical inference to make predictions with the use of distribution ?? (except C.I or point estimations)