In this study we follow the method of Bayesian technique with a difference. In traditional Bayesian method one assumes a prior distribution, may be a conjugate one, which is centered at super parameters. Our approach of data increase is supposed to help for small samples or cases where a very few observations are available. We use prior distributions, centered at the given observations (pretending them to be super parameters), to generate a larger artificial dataset which may be termed as second generation dataset. This larger second generation dataset is then used to draw statistical inferences. The method is dependent on computational resources, and may be useful in applied problems.