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Frequently asked questions

This page contains a collection of common misconceptions, frequently asked questions and concerns about preregistration. This page is designed to be used as a reference guide and the collection is organized according to the stages of doing a preregistration. The colored tags indicate the topic the question falls under.

The topics include: Terms and concepts, Study type, Practical, Ways of working, Statistics

Thinking about doing a preregistration

The data I use comes from a large database, can I still preregister my study with existing data?

Study type , Practical

Yes, with some additional considerations needed.

Increasingly researchers conduct new analyses on existing data from large-scale databases. The analytical decisions made for pre-existing data can be influenced quite strongly by a researcher’s prior knowledge, which can contribute to questionable research practices.

Van Den Akker et al. (n.d.) provide a template and a detailed worked example for how to carry out a preregistration for pre-existing data. Some additional considerations covered in the template are “What prior knowledge do you have about the dataset that may be relevant for the proposed analysis?”.

My study is descriptive (e.g. observational), is doing a preregistration still useful?

Study type

There are cases where preregistration might not be appear to be applicable, however descriptive studies can be preregistered! You can preregister research questions, modelling approach and data wrangling/analysis steps. It can also be helpful to find examples of preregistered studies with a similar research approach to you to get an idea of what it can look like in practice (e.g. see descriptive preregistration example).

See this commentary by (Dal-Ré et al. 2014) for more information about preregistration of medical observational studies.

What is the difference between a preregistration and a registered report?

Terms and concepts

Registered reports are a publishing format where a study undergoes two stages of peer review:

  • stage 1: introduction and methods in advance of data collection/analysis and

  • stage 2: which includes the full study findings including results/discussion (Parsons et al. 2022).

Registered reports have been described in the literature as “Two-stage review with in-principle acceptance” (Mayo-Wilson et al. 2025).

Is it true that doing a preregistration increases the risk of being scooped?

Practical Ways of working

The fear of scooping is understandable given the current competitive research culture of “publish or perish”. Preregistrations do not need to be made public right away and can be embargoed for a period of years (e.g. Open Science Framework preregistrations can be embargoed for 4 years from first submission). The embargo period allows you time after preregistering your study to conduct the research, analyze, write up and publish the findings.

Preparing a preregistration

I am keen to try out doing a preregistration but my supervisor thinks it is a “waste of time”, what should I do?

Ways of working

Often a reluctance to try out preregistration comes from a place of lack of familiarity with the process. Sharing resources, and suggest a group meeting to discuss/learn more about preregistration to increase familiarity. Reframe preregistration as a creative opportunity to plan great science! Planning is where the science is, as other research processes, like data collection can be delegated.

Relate to this situation? See the preregistration case study which provides a number of strategies to address this kind of barrier.

How do I know my planned statistical analysis is the right one?

Statistics

To get a better understanding of your real data before your collect it you can run computer simulations and power analyses. Briefly this involves:

  • Simulating random data with the distribution and sample size of expected real data and run the statistical test planned for the study.

  • Repeating the statistical test (e.g. 100 x) and check results are within the false-positive finding rate of 5%.

  • Running a power analysis by simulating data with the expected effect size for the study and check model registers effect at least 80% of the time.

Take a look at our simulation tutorial here or a more beginner friendly guide here.

“Preregistration keeps you in a box”

Statistics Ways of working

Preregistrations are sometimes perceived to be restrictive, where you have to strictly adhere to the study plan. This is not the case! Preregistrations are a “plan not a prison”, as there are different degrees of flexibility and you can document any deviations from your plan when you write up the research findings (DeHaven, 2017).

Preregistrations can also be tailored to the study type (e.g incremental transparently documenting exploratory-confirmatory pathway or sequential - multi-stage contingent experiments).

Binney et al. (2025) discuss practical guidance of balancing mitigating biases with flexibility in the research process. Lakens (2024) discusses when and how to deviate from a preregistration.

Submitting a preregistration

I forgot to report a statistical test, can I modify my preregistration?

Practical

Mistakes happen! If you spot the mistake before you carry out your analysis, you can report a “deviation from preregistration” in a new document where you transparently describe the change, rationale and effect of change to the study results (see guidance here).

I accidentally included sensitive information in my preregistration, can I withdraw it?

Practical

There are several options depending on when you decide to withdraw your preregistration:

Depending on the preregistration platform you use, there is an author approval window after creation of preregistration (2 days for OSF, 6 days for AsPredicted) where you can cancel it.

If you decide to withdraw after the preregistration is finalized, you can create a new preregistration with the sensitive information omitted and document the rationale for withdrawing the original preregistration. More information about the withdrawal process on OSF.

Post preregistration: reporting research

What sort of things from my preregistration do I need to report in my manuscript?

Practical Statistics Study type

You will need to reference the preregistrated work in the methods section of your manuscript by providing a link to the repository. All the main components of a preregistration (e.g. hypotheses/research questions, sampling plan, variable description, design plan, outliers and exceptions, analyses) and any deviations from the preregistration. You can use this OSF checklist to help you remember what should be reported in your manuscript.

Where can I find information on which journals accept preregistrations/registered reports?

Practical

There are over 300 journals that adopt the registered report format (see list of journals here).

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References

Binney, Richard J, Laura J Smith, Stephanie Rossit, et al. 2025. “Practical Routes to Preregistration: A Guide to Enhanced Transparency and Rigour in Neuropsychological Research.” Brain Communications 7 (3): fcaf162. https://doi.org/10.1093/braincomms/fcaf162.
Dal-Ré, Rafael, John P. Ioannidis, Michael B. Bracken, et al. 2014. “Making Prospective Registration of Observational Research a Reality.” Science Translational Medicine 6 (224). https://doi.org/10.1126/scitranslmed.3007513.
Lakens, Daniël. 2024. “When and How to Deviate from a Preregistration.” Collabra: Psychology 10 (1): 117094. https://doi.org/10.1525/collabra.117094.
Mayo-Wilson, Evan, Sean Grant, Katherine S. Corker, and David Moher. 2025. “Consistent and Precise Description of Research Outputs Could Improve Implementation of Open Science.” Advances in Methods and Practices in Psychological Science 8 (4): 25152459251375445. https://doi.org/10.1177/25152459251375445.
Parsons, Sam, Flávio Azevedo, Mahmoud M. Elsherif, et al. 2022. “A Community-Sourced Glossary of Open Scholarship Terms.” Nature Human Behaviour 6 (3): 312–18. https://doi.org/10.1038/s41562-021-01269-4.
Van Den Akker, Olmo, Sara J Weston, Lorne Campbell, et al. n.d. Preregistration of Secondary Data Analysis: A Template and Tutorial. https://doi.org/10.31234/osf.io/hvfmr.