The purpose of this paper is to make a new way which imitates the idea of Bayesian approach with a difference. In Bayesian approach one considers a statistical prior distribution which is centered at super parameters, and we assume the way ( data increase) is to improve for small samples where a very few observations (data) are available. And using statistical prior distributions, at the core of the given observations ( calling them to be hyper-parameters ), we may call as second generation dataset by producing a bigger dataset, then we can make statistical inferences by using this bigger second dataset.