🔧 How the split-sample procedure works
Researchers send their dataset to an independent third party that randomly creates a training sample and a withheld testing sample. All model building, hypothesis selection, and revisions occur using the training sample, allowing feedback from colleagues, editors, and referees. Once the paper is accepted, the pre-specified analysis is applied to the testing sample, and those testing-sample results are the ones published.
📊 What the simulations show
- Under empirically relevant settings, the split-sample method yields greater statistical power than a conventional preanalysis plan (PAP).
- The primary mechanism for this power gain is a reduced chance that relevant hypotheses are never tested during research workflows.
- The advantage is strongest in settings where outcomes of interest are uncertain and exploration is common.
⚖️ When this approach is most and least appropriate
- Well-suited for exploratory analyses with substantial uncertainty about outcomes and hypotheses.
- Not recommended when treatments are very costly and available sample size is severely limited, because withholding a testing sample can make inference underpowered.
🔍 How to interpret the method
- The procedure can be seen as enabling direct replication: the testing sample functions as an independent confirmation of results developed on the training sample.
🛠️ Practical considerations for implementation
- Requires an independent third party to perform the random split and hold the testing data until acceptance.
- Allows iterative improvement and feedback on analyses without compromising the credibility of final published estimates.
- Feasibility issues and implementation logistics (data transfer, pre-specification of analysis on the training set, and journal workflows) are discussed in detail.
Why it matters
This split-sample protocol offers a pragmatic middle ground between exploratory work and strict preanalysis plans: it preserves opportunities for refinement and feedback while producing published results that come from an independent test, improving credibility and—under many realistic conditions—statistical power.
