In his focus article, "Rethinking Traditional Methods of Survey Validation," published in this issue of "Measurement: Interdisciplinary Research and Perspectives," Andrew Maul introduces and discusses several foundational issues and concludes that self-report measures may be particularly difficult to validate and may fall short of providing the kinds of rigorous, potentially falsifying tests of relevant hypotheses commonly expected in scientific research. Here, author Luca Mari comments that Maul's experiment epitomizes the deficiencies of purely formal, input-output methods of measurement characterization, modeling, and validation. Luca concludes with a quotation from the "Guide to the Expression of Uncertainty in Measurement" (GUM), which the author feels applies to all formal methods whenever they are used as tools for measurement. "The quality and utility of the uncertainty quoted for the result of a measurement therefore ultimately depend on the understanding, critical analysis, and integrity of those who contribute to the assignment of its value." [For "Rethinking Traditional Methods of Survey Validation," see EJ1156001.]