A more recent paper compellingly demonstrated how flexibility in data collection, analysis, and reporting can dramatically increase false positive rates (Simmons, Nelson, & Simonsohn, 2011). This recognition has led to a growing movement in the social sciences to adopt new practices to promote research integrity (Cumming, 2014), including prespecification of studies and hypotheses, avoidance of selection and other questionable data-analytic practices, complete reporting of analyses and variables, and encouragement of replication. A “new statistics” has been proposed that includes recommended statistical practices such as estimation based on effect sizes, confidence intervals, and meta-analyses (Cumming, 2014). These are practices that have already become more widespread in the genetics field (A. Agrawal et al., 2012; Boraska et al., 2014; Ripke et al., 2013; Steinberg et al., 2014; Stephens et al., 2013; Thompson et al., 2014) where a plague of inconsistent and nonreplicable genetic main effects has led to the adoption of more rigorous statistical practices, which have proven successful in advancing the field (Corvin, Craddock, & Sullivan, 2010; P. F. Sullivan et al., 2012; van Assen, van Aert, Nuijten, & Wicherts, 2014). Following these guidelines would go a long way toward improving the quality and trustworthiness of the cGxE literature as well.
