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Description: In much psychology research, mediators are measured, not manipulated. Therefore, the paths from mediators to outcomes—the so-called b paths—can be confounded by omitted variable bias, as in any other correlational analysis. The present research builds on the logic of falsification tests in econometrics and sensitivity analysis in statistics to propose the impossible mediation test, which can quantify the amount of confounded mediation. Researchers can add to an experiment a condition in which the dependent variable is measured first, then the manipulation is implemented, and finally, the posited mediator is measured. This allows for assessment of the spurious association between the dependent variable and the mediator, and statistics can be estimated as if the dependent variable was measured after the manipulation was implemented, to assess whether the spurious association is sufficiently strong to yield the false appearance of mediation. This estimate of “impossible” mediation can be compared to the results obtained from data where the dependent variable is actually measured in the conventional order after the mediator, to determine whether evidence of mediation is stronger in the latter case than the former. Evidence of mediation that survives the impossible mediation test constitutes a strong basis for a claim about mediation of a causal process. The paper illustrates this procedure with an empirical example.