**4. Concluding Remarks**

Based on the method of generating classes of probability distributions presented by [12], we introduce a new class called Normal-*G*. It has the advantage of demanding no additional parameters besides the baseline ones. We demonstrate that the proposed class generates identifiable sub-models as long as the parent distribution is identifiable. The pdf of the class can be written as a linear combination of pdfs of exponentiated distributions; it allows us to easily derive the raw moments, the incomplete moments and the moment generating function.

We bring Monte Carlo simulation studies to attest the good performance of the MLEs of two distributions generated by the class and to illustrate its usefulness, applications to real datasets are made. The fitted models are compared to other competitive distributions regarding the Anderson-Darling and the Cramér-von Mises statistics, as well as commonly used information criteria as goodness-of-fit measures. The general results indicate that the Normal-*G* outperforms the other distributions in comparison. The new class is powerful and provides parsimonious models, which may hopefully interest practitioners of statistics, soil science, oceanography and other fields.

**Author Contributions:** All of the authors contributed relevantly to this research article.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.
