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Abstract

The interpretation of the P-value and its monotone transform s=-log2(p), or S-value, remains debated despite decades of dedicated literature. Within the neo-Fisherian framework, these values are often described as indices of (in)compatibility between the observed data and a set of ideal assumptions (i.e., the statistical model). In this regard, this paper proposes the distinction between two domains: the model domain, where assumptions are taken as perfectly true and every admissible outcome is, by construction, fully compatible with the model; and the real domain, where assumptions may fail and face empirical scrutiny. I argue that, although interpreted through an objective numerical index, any level of incompatibility can arise only in the latter domain, where the epistemic status of the model under examination is uncertain and a genuine conflict between data and hypotheses can therefore occur. The extent to which P- and S-values are taken as indicating incompatibility is a matter of contextual judgment. Within this framework, descriptive approaches serve to quantify the numerical values of P and S; these can be interpreted as indicative of a certain degree (or amount) of incompatibility between data and hypotheses once causal knowledge of the data-generating process and information about the costs and benefits of related decisions become clearer. Although the distinction between the model domain and the real domain may appear merely theoretical or even philosophical, I argue that this perspective is useful for developing a clear mental representation of how statistical estimates should be evaluated in practical settings and applications.