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Scholars routinely test mediation claims by using some form of measurement-of-mediation analysis, whereby outcomes are regressed on treatments and mediators in order to assess direct and indirect effects. Indeed, it is rare for an issue of any leading journal of social or personality psychology not to include such an analysis. Statisticians have for decades criticized this method on the grounds that it relies on implausible assumptions, but these criticisms have been largely ignored. After presenting examples and simulations that dramatize the weaknesses of the measurement-of-mediation approach, we suggest that scholars instead use an approach that is rooted in experimental design. We propose implicit mediation analysis, which adds and subtracts features of the treatment in ways that implicate some mediators and not others. We illustrate the approach with examples from recently published articles, explain the differences between the approach and other experimental approaches to mediation, and formalize the assumptions and statistical procedures that allow researchers to learn from experiments that encourage changes in mediators.