No distribution is inherently "superior" to another; different distributions are appropriate for different things. It depends on what you're trying to do and what kind of data you are working with. For example, in my field I work with reaction time data, which are known to naturally follow something more like an ex-gaussian distribution than a normal distribution, so there is some a priori reason to expect that summarizing the data via ex-gaussian distribution parameters will do better than trying to summarize the data through normal distribution parameters (mean and sd). That does not mean that the ex-gaussian distribution is "superior" in general; it only means it might be more appropriate for one particular application.