Systems Factorial Technology (SFT) is a well defined approach based on rigorously mathematical definitions of constructs and derivations of measures. Inferences about cognitive processing based on the Survivor Interaction Contrast (SIC) and capacity coefficients (Ct) are broad, allowing the rejection of entire classes of models (e.g., all serial processes) because the approach relies on so few parametric assumptions. Although this generality is a strength of the framework, one drawback is that it complicates data analysis. Models based on specific parametric assumptions, such as Linear Ballistic Accumulator models, can be evaluated based on the likelihood of the observed data precisely because they make such strong assumptions. Hence, the challenge in analyzing data within the SFT framework is to develop statistical analyses that do not compromise the generality of the core theory.