A Method to Reduce Type I Error while Maintaining Power Using Split Samples

We discuss a method aimed at reducing the risk that spurious results are published. Researchers send their datasets to an independent third party who randomly generates training and testing samples. Researchers perform their analysis on the former and once the paper is accepted for publication the method is applied to the latter and it is those results that are published. Simulations indicate that, under empirically relevant settings, the proposed method significantly reduces type I error and delivers adequate power. Unlike alternative approaches such as the registration of a pre-analysis plan, this method allows researchers to learn from the data. ∗Fafchamps: Stanford University, Freeman Spogli Institute for International Studies, Encina Hall E105, Stanford CA 94305 USA ([email protected]). Labonne: Yale-NUS College, Singapore ([email protected]). We thank Rob Garlick for fruitful discussions while working on this paper. All remaining errors are ours.