"The statistic Prep estimates the probability of replicating an effect. It captures traditional publication criteria for signal-to-noise ratio, while avoiding parametric inference and the resulting Bayesian dilemma. In concert with effect size and replication intervals, Prep provides all of the information now used in evaluating research, while avoiding many of the pitfalls of traditional statistical inference."A rather bold claim! And, shortly after its publication, the journal Psychological Science (6th highest psyc impact factor) recommended that authors report p-rep instead of the traditional p-value. Which makes the rebuttal article by Iverson et al that much more tantalizing. They write:
"This probability of replication prep seems new, exciting, and extremely useful. Despite appearances however prep is misnamed, commonly miscalculated even by its progenitors, misapplied outside a common but otherwise very narrow scope, and its seductively large values can be seriously misleading. In short, Psychological Science has bet on the wrong horse, and nothing but mischief will follow from its continued promotion of prep as a scientifically informative predictive probability of replicability."Now that is what I call a take down! These same authors calm down quite a bit in their '10 article and even make the level-headed suggestion that p-rep is a step in the right direction, but that is lame so I won't quote from it.
Reading about p-values makes me want to start a blog about them (how does such a blog not already exist?!). A good subtitle could be "where one in every twenty posts will be significant by chance alone."