Problems in usingp-curve analysis and text-mining to detect rate ofp-hacking and evidential value

Abstract

Background.Thep-curve is a plot of the distribution ofp-values reported in a set of scientific studies. Comparisons between ranges ofp-values have been used to evaluate fields of research in terms of the extent to which studies have genuine evidential value, and the extent to which they suffer from bias in the selection of variables and analyses for publication,p-hacking.Methods.p-hacking can take various forms. Here we used R code to simulate the use of ghost variables, where an experimenter gathers data on several dependent variables but reports only those with statistically significant effects. We also examined a text-mined dataset used by Head et al. (2015) and assessed its suitability for investigatingp-hacking.Results.We show that when there is ghostp-hacking, the shape of thep-curve depends on whether dependent variables are intercorrelated. For uncorrelated variables, simulatedp-hacked data do not give the “p-hacking bump” just below .05 that is regarded as evidence ofp-hacking, though there is a negative skew when simulated variables are inter-correlated. The wayp-curves vary according to features of underlying data poses problems when automated text mining is used to detectp-values in heterogeneous sets of published papers.Conclusions.The absence of a bump in thep-curve is not indicative of lack ofp-hacking. Furthermore, while studies with evidential value will usually generate a right-skewedp-curve, we cannot treat a right-skewedp-curve as an indicator of the extent of evidential value, unless we have a model specific to the type ofp-values entered into the analysis. We conclude that it is not feasible to use thep-curve to estimate the extent ofp-hacking and evidential value unless there is considerable control over the type of data entered into the analysis. In particular,p-hacking with ghost variables is likely to be missed.