Prespecifying

Prespecifying is the practice of outlining, before data collection or examination, the hypotheses, outcomes, and analysis methods that a study will use. The aim is to constrain flexible post hoc choices that can bias results, such as selectively reporting significant findings or modifying hypotheses after seeing the data.

Typically, prespecification includes primary and secondary hypotheses, primary outcomes, secondary outcomes, the study design, inclusion and exclusion criteria, sample size calculations, data collection procedures, and a detailed statistical analysis plan (models, covariates, handling missing data, adjustments for multiple comparisons). It may also specify stopping rules, interim analyses, and criteria for excluding data.

Prespecification is common in clinical trials and increasingly in psychology, social sciences, and economics. Researchers often formalize prespecification in a protocol or register it publicly via preregistration platforms or clinical trial registries, such as ClinicalTrials.gov or the Open Science Framework (OSF) preregistration. Some journals publish registered reports, where the study's methods are peer-reviewed before results are known and allow for transparent reporting of deviations.

Benefits include reduced bias from data dredging, improved reproducibility, and clearer interpretation of confirmatory results. Limitations include potential rigidity, challenges when new information arises, and the need for transparent handling of any justified deviations from the prespecified plan.

Prespecification is closely related to preregistration and to concepts such as HARKing and p-hacking. Together with transparent reporting, prespecification supports credible inference in empirical research.