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Fig. 3 Representation of an argument pattern for the appropriateness of statistical analysis. Claims are represented as rectangles, strategies as parallelograms, contextual information (which could also include assumptions, justifications, etc.), as soft-cornered rectangles. Instantiation of the pattern replaces names in angle-brackets

Figure 3 Representation of an argument pattern for the appropriateness of statistical analysis. Claims are represented as rectangles, strategies as parallelograms, contextual information (which could also include assumptions, justifications, etc.), as soft-cornered rectangles. Instantiation of the pattern replaces names in angle-brackets

Related Figures (3)

Fig. 1 Phases (ellipses) and products (boxes) of the CoSMoS process [13, 50, 58]. The CoSMoS lifecycle starts with identification of the domain (not shown), and proceeds through development of the domain and platform models, the simulation platform and the results model. Information collected en-route is summarised in the research context, which is used to map between simulation results and domain observables
Fig. 1 Phases (ellipses) and products (boxes) of the CoSMoS process [13, 50, 58]. The CoSMoS lifecycle starts with identification of the domain (not shown), and proceeds through development of the domain and platform models, the simulation platform and the results model. Information collected en-route is summarised in the research context, which is used to map between simulation results and domain observables
Fig. 2 An argument pattern for the top-level fitness-for-purpose of a domain model for a simulation. Claims are represented as rectangles, strategies as parallelograms, contextual information (which could also include assumptions, justifications, etc.), as soft-cornered rectangles. Instantiation of the pattern replaces names in angle-brackets and elaborates the sub-claims [14, 42] tation of ntness-for-purpose arguments |4, 41, 42}. Whereas a safety case argument must be complete, from goal to evidence, th primary purpose of a fitness-for-purpose argument is to expose and express the rez soning behind a particular aspect of the simulation design. For example, Alden [2 captures a detailed rationale for the fitness-for-purpose of the domain model created t model Peyers’ patch development in an embryonic mouse gut. The top claim, that th [m]odel is an adequate representation of the biology, is addressed using the decor position strategy, /aJrgue over scientific content, the adequacy of the abstractio and the experimental results. Each element of the decomposition can be addresse in turn, broken down into sub-goals, and, in Alden’s case, used to point to the ever tual evidence (data, statistical analysis) that the simulator is capable of generatin results that are comparable to observations of the domain.
Fig. 2 An argument pattern for the top-level fitness-for-purpose of a domain model for a simulation. Claims are represented as rectangles, strategies as parallelograms, contextual information (which could also include assumptions, justifications, etc.), as soft-cornered rectangles. Instantiation of the pattern replaces names in angle-brackets and elaborates the sub-claims [14, 42] tation of ntness-for-purpose arguments |4, 41, 42}. Whereas a safety case argument must be complete, from goal to evidence, th primary purpose of a fitness-for-purpose argument is to expose and express the rez soning behind a particular aspect of the simulation design. For example, Alden [2 captures a detailed rationale for the fitness-for-purpose of the domain model created t model Peyers’ patch development in an embryonic mouse gut. The top claim, that th [m]odel is an adequate representation of the biology, is addressed using the decor position strategy, /aJrgue over scientific content, the adequacy of the abstractio and the experimental results. Each element of the decomposition can be addresse in turn, broken down into sub-goals, and, in Alden’s case, used to point to the ever tual evidence (data, statistical analysis) that the simulator is capable of generatin results that are comparable to observations of the domain.
Fig. 4 An instantiation of the argument pattern in Fig. 3 for the use of Vargha and Delaney’s A test [60]. A fuller version of the argument, showing how it has been applied to specific experimental results, can be found in [42] To illustrate a pattern and its instantiation, it is easiest to take a more focused argument. A common problem in demonstrating fitness for purpose in a simulation (or real-life) context is showing that the statistical analysis undertaken has used an appropriate statistic. Most common statistics make some assumptions about whether data is continuous or discrete and how population and sample data and data errors are distributed. Figures3 and 4 illustrate a general pattern for an argument about are distributed. Figures3 and 4 illustrate a general pattern for an argument about
Fig. 4 An instantiation of the argument pattern in Fig. 3 for the use of Vargha and Delaney’s A test [60]. A fuller version of the argument, showing how it has been applied to specific experimental results, can be found in [42] To illustrate a pattern and its instantiation, it is easiest to take a more focused argument. A common problem in demonstrating fitness for purpose in a simulation (or real-life) context is showing that the statistical analysis undertaken has used an appropriate statistic. Most common statistics make some assumptions about whether data is continuous or discrete and how population and sample data and data errors are distributed. Figures3 and 4 illustrate a general pattern for an argument about are distributed. Figures3 and 4 illustrate a general pattern for an argument about
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