In a recent article in PLOS One, Don van Ravenzwaaij and John Ioannidis argue that Bayes factors should be preferred to significance testing (p-values) when assessing the effectiveness of new drugs. At his blogsite The 20% Statistician, Daniel Lakens argues that Bayes factors suffer from the same problems as p-values. Namely, the combination of small effect sizes and sample sizes leads to inconclusive conclusions no matter whether one uses p-values or Bayes factors. The real challenge facing decision-making from statistical studies comes from publication bias and underpowered studies. Both significance testing and Bayes factors are relatively powerless (pun intended) to overcome these more fundamental problems. To read more, click here.