Progress 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.methodology | open science | confirmatory analysis | exploratory analysis | preregistration P rogress in science is marked by reducing uncertainty about nature. Scientists generate models that may explain prior observations and predict future observations. Those models are approximations and simplifications of reality. Models are iteratively improved and replaced by reducing the amount of prediction error. As prediction error decreases, certainty about what will occur in the future increases. This view of research progress is captured by George Box's aphorism: "All models are wrong but some are useful" (1, 2).Scientists improve models by generating hypotheses based on existing observations and testing those hypotheses by obtaining new observations. These distinct modes of research are discussed by philosophers and methodologists as hypothesis-generating versus hypothesis-testing, the context of discovery versus the context of justification, data-independent versus data-contingent analysis, and exploratory versus confirmatory research (e.g., refs. 3-6). We use the more general terms--postdiction and prediction--to capture this important distinction.A common thread among epistemologies of science is that postdiction is characterized by the use of data to generate hypotheses about why something occurred, and prediction is characterized by the acquisition of data to test ideas about what will occur. In prediction, data are used to confront the possibility that the prediction is wrong. In postdiction, the data are already known and the postdiction is generated to explain why they occurred.Testing predictions is vital for establishing diagnostic evidence for explanatory claims. Testing predictions assesses the uncertainty of scientific models by observing how well the predictions account for new data. Generating postd...

Progress 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.methodology | open science | confirmatory analysis | exploratory analysis | preregistration P rogress in science is marked by reducing uncertainty about nature. Scientists generate models that may explain prior observations and predict future observations. Those models are approximations and simplifications of reality. Models are iteratively improved and replaced by reducing the amount of prediction error. As prediction error decreases, certainty about what will occur in the future increases. This view of research progress is captured by George Box's aphorism: "All models are wrong but some are useful" (1, 2).Scientists improve models by generating hypotheses based on existing observations and testing those hypotheses by obtaining new observations. These distinct modes of research are discussed by philosophers and methodologists as hypothesis-generating versus hypothesis-testing, the context of discovery versus the context of justification, data-independent versus data-contingent analysis, and exploratory versus confirmatory research (e.g., refs. 3-6). We use the more general terms--postdiction and prediction--to capture this important distinction.A common thread among epistemologies of science is that postdiction is characterized by the use of data to generate hypotheses about why something occurred, and prediction is characterized by the acquisition of data to test ideas about what will occur. In prediction, data are used to confront the possibility that the prediction is wrong. In postdiction, the data are already known and the postdiction is generated to explain why they occurred.Testing predictions is vital for establishing diagnostic evidence for explanatory claims. Testing predictions assesses the uncertainty of scientific models by observing how well the predictions account for new data. Generating postd...

The spliceosome undergoes dramatic changes in both small nuclear RNA (snRNA) composition and structure during assembly and pre-mRNA splicing. It has been previously proposed that the U2 snRNA adopts two conformations within the stem II region: stem IIa or stem IIc. Dynamic rearrangement of stem IIa into IIc and vice versa is necessary for proper progression of the spliceosome through assembly and catalysis. How this conformational transition is regulated is unclear; although, proteins such as Cus2p and the helicase Prp5p have been implicated in this process. We have used single-molecule Förster resonance energy transfer (smFRET) to study U2 stem II toggling between stem IIa and IIc. Structural interconversion of the RNA was spontaneous and did not require the presence of a helicase; however, both Mg 2+ and Cus2p promote formation of stem IIa. Destabilization of stem IIa by a G53A mutation in the RNA promotes stem IIc formation and inhibits conformational switching of the RNA by both Mg 2+ and Cus2p. Transitioning to stem IIa can be restored using Cus2p mutations that suppress G53A phenotypes in vivo. We propose that during spliceosome assembly, Cus2p and Mg 2+ may work together to promote stem IIa formation. During catalysis the spliceosome could then toggle stem II with the aid of Mg 2+ or with the use of functionally equivalent protein interactions. As noted in previous studies, the Mg 2+ toggling we observe parallels previous observations of U2/U6 and Prp8p RNase H domain Mg 2+ -dependent conformational changes. Together these data suggest that multiple components of the spliceosome may have evolved to switch between conformations corresponding to open or closed active sites with the aid of metal and protein cofactors.

By implementing more transparent research practices, authors have the opportunity to stand out and showcase work that is more reproducible, easier to build upon, and more credible. Scientists gain by making work easier to share and maintain within their own laboratories, and the scientific community gains by making underlying data or research materials more available for confirmation or making new discoveries. The following protocol gives authors step‐by‐step instructions for using the free and open source Open Science Framework (OSF) to create a data management plan, preregister their study, use version control, share data and other research materials, or post a preprint for quick and easy dissemination. © 2019 by John Wiley & Sons, Inc.

In order to increase the replicability of scientific work, the scientific community has called for practices designed to increase the transparency of research (McNutt, 2014; Nosek et al., 2015). The validity of a scientific claim depends not on the reputation of those making the claim, the venue in which the claim is made, or the novelty of the result, but rather on the empirical evidence provided by the underlying data and methods. Proper evaluation of the merits of scientific findings requires availability of the methods, materials, and data and the reasoned argument that serve as the basis for the published conclusions (Claerbout and Karrenbach 1992; Donoho et al 2009; Stodden et al 2013; Borwein et al 2013; Munafò et al, 2017). Wide and growing support for these principles (see, for example, signatories to Declaration on Research Assessment, DORA, https://sfdora.org/, and the Transparency and Openness Promotion Guidelines https://cos.io/our-services/top-guidelines/) must be coupled with guidelines to increase open sharing of data and research materials, use of reporting guidelines, preregistration, and replication. We propose that, going forward, authors of all scientific articles disclose the availability and location of all research items, including data, materials, and code, related to their published articles in what we will refer to as a TOP Statement.