A new paper by GARRET CHRISTENSEN,  JUSTIN MCCRAY and DANIELE FANELLI in PLOS ONE suggests an alternative to using conventional t-values when researchers are concerned about publication bias.  From the Abstract: “Publication bias leads consumers of research to observe a selected sample of statistical estimates calculated by producers of research. We calculate critical values for statistical significance that could help to adjust after the fact for the distortions created by this selection effect, assuming that the only source of publication bias is file drawer bias. These adjusted critical values are easy to calculate and differ from unadjusted critical values by approximately 50%—rather than rejecting a null hypothesis when the t-ratio exceeds 2, the analysis suggests rejecting a null hypothesis when the t-ratio exceeds 3.”  To read more, click here.