P-values, p-values everywhere!

The p-value has been an integral part of presenting and interpreting results from statistical analyses in many research domains. However, in recent decades, there has been a plethora of criticisms of the use of p-values in journal articles, editorials of journals and blogs. In an editorial in 2015, the Basic and Applied Social Psychology journal announced that use of all null hypothesis significance testing was banned, as were related procedures such as the use of CI (Trafimow and Marks 2015). This led to considerable debate and discussion on the merits of such bans and on using p-values (e.g. Ashworth 2015; Greenland et. al. 2016), and was referred to in the American Statistical Association’s statement on p-values (Wasserstein and Lazar 2016). A recent issue of the Biometrical Journal (volume 59, issue 5) included a number of discussion papers on this topic in relation to medical and health research, and it was notable that all 10 contributors agreed that p-values had a role to play (Wellek 2017). Nevertheless it was generally agreed that when using p-values they should be supplemented by other measures such as CI, and consideration should be given to approaches such as Bayesian inference and a framework for simultaneously testing inferiority, equivalence, and superiority. In this editorial, we do not address the question of a total ban on p-values. Instead we suggest that p-values have a place in inferential statistics and discuss their appropriate use, in combination with CI.