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	   For model-based frequentist statistics, based on a parametric statistical model  			 		 ${{\cal M}_\theta }({\bf{x}})$ 		, the trustworthiness of the ensuing evidence depends crucially on (i) the validity of the probabilistic assumptions comprising  			 		 ${{\cal M}_\theta }({\bf{x}})$ 		, (ii) the optimality of the inference procedures employed, and (iii) the adequateness of the sample size ( n ) to learn from data by securing (i)–(ii). It is argued that the criticism of the postdata severity evaluation of testing results based on a small  n  by Rochefort-Maranda (2020) is meritless because it conflates [a] misuses of testing with [b] genuine foundational problems. Interrogating this criticism reveals several misconceptions about trustworthy evidence and estimation-based effect sizes, which are uncritically embraced by the replication crisis literature.

Severity and Trustworthy Evidence: Foundational Problems versus Misuses of Frequentist Testing