01656nas a2200169   4500000000100000008004100001260001500042100001900057700002400076700002500100700002000125245003500145856005300180300001400233490000800247520123100255       2018                            d  c2018-03-121 aBrian A. Nosek1 aCharles R. Ebersole1 aAlexander C. DeHaven1 aDavid T. Mellor00aThe preregistration revolution  uhttps://www.pnas.org/doi/10.1073/pnas.1708274114  a2600-26060 v1153 aProgress in science relies in part on generating hypotheses with existing observations and testing hypotheses with new observations. This distinction between postdiction and prediction is appreciated conceptually but is not respected in practice. Mistaking generation of postdictions with testing of predictions reduces the credibility of research findings. However, ordinary biases in human reasoning, such as hindsight bias, make it hard to avoid this mistake. An effective solution is to define the research questions and analysis plan before observing the research outcomes—a process called preregistration. Preregistration distinguishes analyses and outcomes that result from predictions from those that result from postdictions. A variety of practical strategies are available to make the best possible use of preregistration in circumstances that fall short of the ideal application, such as when the data are preexisting. Services are now available for preregistration across all disciplines, facilitating a rapid increase in the practice. Widespread adoption of preregistration will increase distinctiveness between hypothesis generation and hypothesis testing and will improve the credibility of research findings.