"Falsifiability is not optional": Correction to LeBel et al. (2017).

Reports an error in "Falsifiability is not optional" by Etienne P. LeBel, Derek Berger, Lorne Campbell and Timothy J. Loving (, 2017[Aug], Vol 113[2], 254-261). In the reply, there were two errors in the References list. The publishing year for the 14th and 21st articles was cited incorrectly as 2016. The in-text acronym associated with these citations should read instead as FER2017 and LCL2017. The correct References list citations should read as follows, respectively: Finkel, E. J., Eastwick, P. W., & Reis, H. T. (2017). Replicability and other features of a high-quality science: Toward a balanced and empirical approach. , 113, 244-253. http://dx.doi.org/10.1037/pspi0000075 LeBel, E. P., Campbell, L., & Loving, T. J. (2017). Benefits of open and high-powered research outweigh costs. , 113, 230-243. http://dx.doi.org/10 .1037/pspi0000049. The online version of this article has been corrected. (The following abstract of the original article appeared in record 2017-30567-003.) Finkel, Eastwick, and Reis (2016; FER2016) argued the post-2011 methodological reform movement has focused narrowly on replicability, neglecting other essential goals of research. We agree multiple scientific goals are essential, but argue, however, a more fine-grained language, conceptualization, and approach to replication is needed to accomplish these goals. Replication is the general empirical mechanism for testing and falsifying theory. Sufficiently methodologically similar replications, also known as direct replications, test the basic existence of phenomena and ensure cumulative progress is possible a priori. In contrast, increasingly methodologically dissimilar replications, also known as conceptual replications, test the relevance of auxiliary hypotheses (e.g., manipulation and measurement issues, contextual factors) required to productively investigate validity and generalizability. Without prioritizing replicability, a field is not empirically falsifiable. We also disagree with FER2016's position that "bigger samples are generally better, but . . . that very large samples could have the downside of commandeering resources that would have been better invested in other studies" (abstract). We identify problematic assumptions involved in FER2016's modifications of our original research-economic model, and present an improved model that quantifies when (and whether) it is reasonable to worry that increasing statistical power will engender potential trade-offs. Sufficiently powering studies (i.e., >80%) maximizes both research efficiency and confidence in the literature (research quality). Given that we are in agreement with FER2016 on all key open science points, we are eager to start seeing the accelerated rate of cumulative knowledge development of social psychological phenomena such a sufficiently transparent, powered, and falsifiable approach will generate. (PsycINFO Database Record