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Finkel, Eastwick, and Reis (2015; FER2015) argued that psychological science is better served by responding to apprehensions about replicability rates with contextualized solutions than with one-size-fits-all solutions. Here, we extend FER2015’s analysis to suggest that much of the discussion of best research practices since 2011 has focused on a single feature of high-quality science—replicability—with insufficient sensitivity to the implications of recommended practices for other features, like discovery, internal validity, external validity, construct validity, consequentiality, and cumulativeness. Thus, although recommendations for bolstering replicability have been innovative, compelling, and abundant, it is difficult to evaluate their impact on our science as a whole, especially because many research practices that are beneficial for some features of scientific quality are harmful for others. For example, FER2015 argued that bigger samples are generally better, but also noted that very large samples (“those larger than required for effect sizes to stabilize”; p. 291) could have the downside of commandeering resources that would have been better invested in other studies. In their critique of FER2015, LeBel, Campbell, and Loving (2017) concluded, based on simulated data, that ever-larger samples are better for the efficiency of scientific discovery (i.e., that there are no tradeoffs). As demonstrated here, however, this conclusion holds only when the replicator’s resources are considered in isolation. If we widen the assumptions to include the original researcher’s resources as well, which is necessary if the goal is to consider resource investment for the field as a whole, the conclusion changes radically—and strongly supports a tradeoff-based analysis. In general, as psychologists seek to strengthen our science, we must complement our much-needed work on increasing replicability with careful attention to the other features of a high-quality science.