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Lerche and Voss (2020) showed that accuracy rates and mean response times alone are insufficient to identify which diffusion decision model parameters vary across experimental conditions. Different parameter combinations can produce indistinguishable behavioural summaries, creating a risk of false process-level conclusions. I extend those simulations to examine how parameter mimicry depends on data quality, model specification, and design complexity, and to evaluate how simulation can inform experimental planning before data collection. Across a set of simulation studies, mimicry was reduced by increasing trials per condition, increasing the availability of error responses, fixing nondecision time across conditions unless theory requires otherwise, using designs with more than two conditions, and incorporating response time variability alongside accuracy and mean response time. The strongest constraint came from adding response time variability as an outcome measure. These results position simulation as a tool for model-informed experimental design and provide practical guidance for planning studies that support more robust cognitive inference.

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