This investigation therefore highlights two intrinsic problems confronting behavioural scientists using large - scale social data . First , large numbers of ill - defined variables necessitate researcher flexibility , potentially exacerbating the garden of forking paths problem : for some datasets analysed there were more than a trillion different ways to operationalize a simple regression 19 . Second , high numbers of observations render minutely small associations significant through the default null hypothesis significance testing lens 29 . With these challenges in mind , our approach , grounded in SCA and including comparison specifications , presents a promising solution so that behavioural scientists can build accurate and practically actionable representations of effects found in large - scale datasets . Overall , the findings place into context popular worries about the putative links between technology use and mental health indicators . They underscore the need for open and impartial reporting of small correlations derived from large - scale social data .