The increase in routine clinical data collection coupled with an expectation to exploit this in support of evidence based decision making creates a need for an intelligent model selection system to support clinicians when analysing data because clinicians often lack the statistical expertise to do this independently. In a previous position paper, an argumentation based approach to devise a decision support system for such an application was introduced. This approach ignored the relative strength of arguments for and against alternative models. This paper demonstrates how an extended argumentation framework can be employed to capture and reason with statistical and research domain knowledge that affects the relative strength of arguments. The approach is validated by means of a real-world case study.