In the context of problems with replicability in psychology and other empirical fields, statistical significance testing and p-values have received a lot of criticism. And without question: much of the criticism has its merits. There certainly are problems with how significance tests are used and p-values are interpreted.1
However, when we are talking about “p-hacking”, I feel that the blame is unfairly on p-values and significance testing alone without acknowledging the general consequences of such behaviour in the analysis.2 In short: selective reporting of measures and cases3 invalidates any statistical method for inference. When I only selectively report variables and studies, it doesn’t matter whether I use p-values or Bayes factors — both results will be useless in practice.