In your standard NHST test, sure. But you can do different models. If I'm reading OP right, what they do is a mixture model, in which effects are assumed to com

In your standard NHST test, sure. But you can do different models. If I'm reading OP right, what they do is a mixture model, in which effects are assumed to come either from a zero-mean distribution or a positive-mean distribution with unknown probability _P_/1-_P_ and then you fit the collection of >2k effect sizes to find out what value of _P_ best fits the dataset as a whole. Apparently the best fit assumes that ~70% of effects are actually ~0. This can also be done nicely with a Bayesian mixture model or a spike-and-slab multilevel model, and that is what is done in "What works in e-commerce - a meta-analysis of 6700 online experiments", Brown & Jones 2017 http://www.qubit.com/sites/default/files/pdf/qubit_meta_analysis.pdf http://www.qubit.com/sites/default/files/pdf/qubit_meta_anal... (although they don't formulate it in terms of a sharp null but ask the more relevant 'probability of a >0 beneficial effect', which for some kinds of A/B test has a very low prior - like only 15% for 'back to top' A/B tests).