How to Preregister a Study in 2026: OSF, AsPredicted, and Registered Reports Explained

When a researcher reports a statistically significant finding after testing eight different analytic strategies, selecting the one that produced a p-value below 0.05, they have not confirmed a hypothesis — they have found a pattern in noise and dressed it as prediction. This practice, known as HARKing (Hypothesising After Results are Known), is at the root of much of the reproducibility problem that has unsettled empirical science over the past two decades. Learning how to preregister a study is the most direct methodological response available to any researcher who wants to produce credible, transparent, and independently replicable findings. This guide provides a complete, step-by-step account of the preregistration process in 2026, covering the three main routes — OSF Preregistration, AsPredicted, and Registered Reports — with practical guidance on what to write, how to write it, and what to do when reality departs from plan.

Preregistration does not make research more restrictive. It makes the distinction between confirmatory and exploratory analysis legible. A well-crafted preregistration forces the researcher to think through their design, sampling logic, and analysis pipeline before a single data point is collected — a discipline that improves studies regardless of whether the preregistration is ever read by anyone else. As Nosek and colleagues argued in their influential 2018 article “The Preregistration Revolution” (PNAS), widespread adoption of preregistration changes the incentive structure of science, rewarding rigorous design rather than surprising results. For PhD candidates and early-career researchers navigating the contemporary publishing landscape, understanding this practice has become a core methodological competency, not an optional extra.

Preregistration is one of the principal solutions to the reproducibility crisis in research — a problem documented at scale by the Open Science Collaboration (2015) and Baker (2016). For the complementary requirement to deposit research data in a findable and reusable way, see our guide on how to write a data availability statement.

What Is Preregistration and Why Does It Matter?

Video: Preregistration Essentials: What It Is, Why It Matters, and How to Do It — Center for Open Science (official channel)

Preregistration is the practice of specifying a research plan — including hypotheses, study design, data collection procedures, analysis strategy, and exclusion criteria — and depositing it in a publicly accessible, time-stamped repository before data collection begins. The repository creates an immutable record, meaning no alterations can be made after submission without a transparent, dated amendment. The core purpose is to separate confirmatory inference (testing a pre-specified prediction) from exploratory analysis (discovering patterns after data collection), two activities that are epistemologically distinct but frequently conflated in published research.

The practical problem that preregistration addresses is well-documented. Researchers face powerful, often unconscious incentives to present findings as more decisive and more predicted than they really were. Flexibility in data analysis — choosing which covariates to include, when to stop collecting data, which dependent measures to report — inflates Type I error rates far beyond the nominal significance threshold. A landmark study by Simmons, Nelson, and Simonsohn (2011) demonstrated that standard analytic flexibility can produce a false-positive rate as high as 60% in psychology experiments. Preregistration constrains this flexibility in confirmatory analyses, ensuring that reported p-values carry their intended evidential weight.

For a broader discussion of how transparency practices connect to the replication literature and open-data requirements, see our guide on the reproducibility crisis in research.

OSF vs AsPredicted vs Registered Reports: Choosing the Right Route

Three pathways dominate contemporary preregistration practice. The table below summarises the key differences:

OSF (Open Science Framework), maintained by the Center for Open Science, is the most versatile option. It offers more than ten validated templates — including a general OSF Preregistration form, a Secondary Data Preregistration template, a Qualitative Preregistration template, and a Registered Report Protocol form — and assigns each submission a citable DOI. It is appropriate for virtually any discipline from psychology and neuroscience to education and economics.

AsPredicted, developed by the Wharton Credibility Lab at the University of Pennsylvania, takes a deliberately minimal approach: eight focused questions that can typically be completed in under thirty minutes. It does not assign a DOI but generates a time-stamped PDF with a unique identifier, which can be cited in journal manuscripts. Its brevity makes it popular for straightforward experimental studies where researchers want to preregister quickly without navigating a lengthy form.

Registered Reports represent the most rigorous preregistration pathway. Rather than self-archiving a protocol, researchers submit a Stage 1 manuscript — including introduction, methods, and any pilot data — to a participating journal for peer review. If approved, the journal issues an in-principle acceptance (IPA): a commitment to publish the completed study regardless of the direction or significance of results, provided the approved protocol was followed. Over 300 journals now accept this format, including Nature Human Behaviour, Cortex, PLOS ONE, and journals from Wiley, Taylor & Francis, and the Royal Society.

How to Preregister on OSF: Step-by-Step

The following procedure covers a standard OSF Preregistration submission. The process takes between one and three hours for a well-prepared researcher with a finalised study design.

Step 1: Create or Log In to Your OSF Account

Navigate to osf.io and create a free account using an institutional email address where possible. Institutional affiliations strengthen the record’s scholarly credibility and make it easier to link to your university’s institutional repository or ORCID profile.

Step 2: Create a New Project (Optional but Recommended)

You can preregister directly without a project, but creating a project first allows you to attach your study materials — codebooks, stimuli, data collection instruments — to the same node. From your dashboard, select Create new project, give it a descriptive title, and set the privacy to private until you are ready to share materials.

Step 3: Navigate to the Registrations Tab

Inside your project, click the Registrations tab, then select New Registration. Alternatively, access the OSF Registries directly at osf.io/registries and click Add a Registration.

Step 4: Select a Template

Choose the template that best matches your study design:

• OSF Preregistration — the default recommended form for experimental and quasi-experimental quantitative research
• AsPredicted — available within OSF if you prefer its shorter format
• Secondary Data Preregistration — for studies using existing datasets (UKBB, SHARE, administrative records, etc.)
• Qualitative Preregistration — for interview, ethnographic, or thematic analysis studies
• Registered Report Protocol — if you have received Stage 1 IPA from a journal

Step 5: Complete the Registration Form

Work through every section carefully. The OSF Preregistration template asks for: study title and contributors; study type (experiment, survey, secondary analysis, etc.); your hypotheses; your variables (independent, dependent, covariates); your sampling plan (including stopping criteria and power analysis); design details; analysis plan; and any additional information such as blinding procedures or ethics approval reference numbers. Auto-save is active throughout.

Step 6: Review, Set Privacy, and Submit

On the final review page, read through all your answers carefully. You can choose immediate public visibility or an embargo of up to four years. An embargo is appropriate when you need to protect data while publishing, or when your institution or funder requires a confidentiality window. Once satisfied, click Submit Registration. All project administrators receive an approval email and have 48 hours to approve; the registration auto-approves after 48 hours if no action is taken.

Step 7: Receive Your DOI

Once approved, the registration is frozen — no further edits are possible. The record receives a persistent DOI that you can cite in your methodology chapter or manuscript, e.g.: Study preregistered at https://doi.org/10.17605/OSF.IO/XXXXX.

How to Preregister on AsPredicted: Step-by-Step

AsPredicted at aspredicted.org is structured around eight concise questions. The questions below are reproduced from published AsPredicted forms in the academic literature and represent the current standard format:

1. Data collection. Have any data been collected for this study already? (Yes / No — if yes, this must be clearly stated.)
2. Hypothesis. What is the main question being asked or hypothesis being tested in this study?
3. Dependent variable. Describe the key dependent variable(s) and how they will be measured.
4. Conditions. How many and which conditions will participants be assigned to?
5. Analyses. Specify exactly which analyses you will conduct to evaluate the main question/hypothesis.
6. Outliers and exclusions. Describe exactly how outliers will be defined and handled, and your exclusion criteria.
7. Sample size. How many observations will be collected or what will determine sample size?
8. Other. Anything else you would like to preregister (e.g., secondary analyses, exploratory variables, unusual analysis decisions)?

To submit, navigate to AsPredicted.org, click Create a New Pre-Registration, enter your email address (no password required), and complete the eight fields. Co-authors can be added by email. Once submitted, you receive a time-stamped PDF with a unique identifier URL. You can set the preregistration to private initially and make it public at submission or publication. The entire process can be completed in under thirty minutes for a well-prepared study design.

A systematic analysis of 105 preregistrations by Thibault et al. (2021), published in PNAS, found that structured formats like AsPredicted produced significantly higher clarity scores (median score 0.81 on a 0–3 scale) compared to unstructured formats (median 0.57), with a large effect size (Cliff’s delta = 0.49). The implication is clear: even a minimal structured form substantially improves the communicative quality of a preregistration relative to free-text alternatives.

Registered Reports: Peer Review Before Results

A Registered Report takes preregistration to its logical conclusion by embedding it within the peer-review process itself. Rather than depositing a protocol in a self-archiving repository, researchers submit a Stage 1 manuscript to a participating journal before data collection. Peer reviewers evaluate the introduction and methodology on their scientific merits, independent of any results, and the journal makes a publication decision on that basis alone.

Stage 1: Protocol Submission

The Stage 1 manuscript typically includes an introduction situating the study in the literature, a fully specified methods section (participants, materials, procedure, analysis plan, power analysis), and optionally pilot data demonstrating feasibility. Reviewers assess scientific rationale, methodological rigour, statistical power, and the plausibility of the hypotheses. The review process at Stage 1 takes approximately nine weeks at most participating journals. If approved, the journal issues an in-principle acceptance (IPA): a commitment to publish the study regardless of whether the results support the hypothesis, provided the approved protocol is followed.

Stage 2: Completed Study Submission

After data collection and analysis, authors submit the full manuscript including results and discussion. Reviewers check that the approved protocol was followed and that any deviations from the preregistered plan are transparently reported. Exploratory analyses may be included but must be clearly labelled as such. After Stage 2 review, the article is published.

The in-principle acceptance mechanism is the most consequential feature of Registered Reports. Because the publication decision precedes results, editorial bias toward positive findings cannot operate. This directly addresses publication bias — the systematic overrepresentation of statistically significant results in the published literature that distorts cumulative scientific knowledge. As of 2026, over 300 journals accept Registered Reports across psychology, neuroscience, medicine, education, and economics. To find participating journals, consult the Center for Open Science Registered Reports directory.

What to Include in a Preregistration: The Six Core Components

Regardless of which platform or format you use, a well-specified preregistration should address six areas. The depth required in each area scales with the complexity of your study and the template you select.

1. Hypotheses

State each hypothesis in precise, falsifiable terms. Specify the direction of the predicted effect where applicable (e.g., “Condition A will produce higher scores on measure X than Condition B”) rather than merely predicting “a difference.” For studies with multiple hypotheses, distinguish primary hypotheses (which will determine the key conclusions) from secondary hypotheses, and indicate how multiple testing will be handled. Vague hypotheses such as “we expect to find an effect of X on Y” provide insufficient constraint and are difficult to assess after data collection.

2. Study Design

Describe the research design in sufficient detail that an independent researcher could replicate the study without contacting you. Include: whether the design is experimental, quasi-experimental, or observational; the number and nature of conditions; the assignment mechanism (random, quasi-random, matched); key materials or stimuli; and the setting or context of data collection. Note any blinding procedures for participants or experimenters.

3. Sampling Plan and Stopping Rules

Specify how many participants you will collect data from and how this number was determined. If using power analysis, report the assumed effect size, desired power (typically 0.80 or 0.90), alpha level, and the software used. If using sequential testing or adaptive designs, specify the stopping rule precisely. For online studies using platforms such as Prolific or MTurk, specify eligibility criteria and whether exclusions are applied before or after reaching the target sample size.

4. Analysis Plan

Describe the statistical analyses you will apply to test each hypothesis. For each analysis, specify the statistical test, the software and version, significance threshold, and how effect sizes will be reported. Address decisions that are typically made after data inspection: model specification, whether to include covariates, how to handle missing data, and whether any transformations will be applied to variables. The more decisions you pre-specify, the fewer degrees of analytical freedom remain, and the more informative a significant result will be.

5. Exclusion Criteria

Define precisely which observations will be excluded from analysis before you look at the data. Common criteria include: failed attention checks (specify the threshold), response times below a plausibility floor, incomplete responses, protocol violations, and outlier thresholds (specify whether outliers are defined in terms of standard deviations, interquartile range, or other criteria). Exclusion criteria applied post-hoc, after seeing how they affect results, constitute a form of analytic flexibility that undermines the confirmatory value of the analysis.

6. Variables

List all variables that will be measured or manipulated, specifying their operationalisation, scale of measurement, and role in the analysis. Identify which variables are primary outcome measures, secondary outcome measures, and covariates. Also note any variables collected for purely exploratory purposes — including them explicitly in the preregistration prevents accusations of having planned to use them all along.

For a discussion of how the methodology chapter of a thesis should document these same decisions, see our guide on how to write a research methodology chapter.

Time-Stamping, Embargos, and DOIs

The evidentiary value of a preregistration rests entirely on the integrity of its timestamp. The timestamp proves that the study plan was finalised before data collection commenced, establishing the temporal priority that distinguishes prediction from post-diction. Both OSF and AsPredicted create cryptographically secured records: OSF uses its own versioned infrastructure and assigns DOIs through DataCite; AsPredicted generates a PDF with an embedded hash and timestamp visible on the document itself.

Embargos on OSF allow the full content of a preregistration to remain private for up to four years while still creating the time-stamped record. During the embargo period, the registration’s metadata (title, date, contributors) is publicly visible, but the study plan is not. This is useful when research involves sensitive populations, proprietary data partnerships, or when commercial considerations require confidentiality before publication. Once the embargo expires — or when you choose to lift it — the full content becomes publicly accessible.

Private preregistrations on AsPredicted follow a similar logic: the record is created and time-stamped, but access is restricted to the listed authors until you choose to make it public. When reporting the preregistration in a manuscript, you provide the AsPredicted URL with a note that it will be made publicly accessible upon paper acceptance.

When citing your preregistration in a manuscript or thesis, follow the format appropriate to your citation style. In APA 7th edition, an OSF preregistration is cited as a dataset/archived document with the DOI. For example: Author, A., & Author, B. (2026). Title of preregistration. OSF. https://doi.org/10.17605/OSF.IO/XXXXX. For additional guidance on the open-science deliverables expected in contemporary academic publishing, including data availability statements, see our guide on how to write a data availability statement.

When You Can Deviate from Your Preregistration

Preregistration is a plan, not a prison. Deviations from a preregistered protocol are sometimes necessary, and they are not inherently disqualifying — provided they are reported transparently, justified in the manuscript, and do not exploit knowledge of the results.

Willroth and Atherton’s 2024 guide, “Best Laid Plans: A Guide to Reporting Preregistration Deviations” (Advances in Methods and Practices in Psychological Science), identifies several common categories of legitimate deviation:

• Unforeseen events: recruitment shortfalls, equipment failure, data loss, platform changes that alter stimuli — deviations arising from circumstances outside the researcher’s control.
• Errors in the preregistration: typos, ambiguous specifications, or omitted details discovered before data collection is complete — best corrected by filing an amendment with a clear explanation.
• Missing information: details not covered in the preregistration (e.g., how to handle a covariate distribution that violates normality assumptions) — these gaps require analytic decisions that should be documented and reported as unplanned.
• Violations of assumptions: discovering that a planned statistical test is inappropriate for the obtained data (e.g., severe non-normality in a small sample where a parametric test was planned) — switching to an appropriate alternative is justified when the preregistered test would produce invalid inferences.

A paper published in Collabra: Psychology (2024) provides a classification system distinguishing necessary deviations (those required for valid inference) from optional deviations (those that improve clarity or convenience). The authors argue that both types are acceptable but should be evaluated against whether the deviation could have been anticipated before data collection, and whether it unequivocally improves the quality of the research independently of its effect on the results.

Practically, the clearest guidance is this: any decision that you would make differently if you already knew the direction of your results is a deviation that requires transparent reporting. Document such decisions in a preregistration deviation log — a supplementary file that lists each planned analysis, notes whether it was executed as specified, and explains any divergence. Many journals now request this document explicitly at Stage 2 of Registered Reports and during peer review of preregistered studies.

Benefits and Limitations of Preregistration

Demonstrated Benefits

Reduction of analytic flexibility and false positives. By pre-specifying the analysis, preregistration eliminates or constrains the researcher degrees of freedom that inflate Type I error rates in conventional studies. This increases the diagnostic value of a statistically significant result.

Increased credibility and trust. Preregistered studies carry a visible commitment to transparency. Reviewers, editors, and readers can compare the registered plan against the reported results, a comparison not possible with non-registered work. This asymmetry increasingly affects editorial decisions at top journals.

Improved study planning. The act of writing a preregistration forces researchers to identify underspecified elements of their design before data collection. Decisions about coding schemes, data transformations, and analysis software that are routinely deferred until after data collection must instead be resolved in advance — often revealing design weaknesses that can still be corrected.

Documentation of priority. A time-stamped, publicly archived preregistration establishes intellectual priority for a research question or hypothesis, which can be important in competitive research environments where simultaneous independent studies sometimes converge on the same question.

Limitations and Criticisms

Preregistration does not guarantee quality. A study can be preregistered and still poorly designed, underpowered, or based on an implausible hypothesis. The timestamp establishes the temporal relationship between plan and results; it does not validate the scientific merit of the plan itself. This is why Registered Reports, which submit the protocol to peer review, provide stronger quality assurance than self-archived preregistrations.

Applicability to exploratory and qualitative research. Preregistration was developed primarily for confirmatory experimental and quantitative research. Its application to qualitative, longitudinal, or exploratory research is less straightforward. While OSF offers a Qualitative Preregistration template, many qualitative researchers argue that specifying an analysis plan before data collection misrepresents the iterative, emergent character of qualitative inquiry. In these contexts, a transparent statement of positionality, methodological approach, and analysis framework can serve some of the same transparency functions without imposing an inappropriate confirmatory structure.

The problem of incomplete preregistrations. A preregistration that omits key analytic decisions, uses ambiguous language, or leaves hypotheses underspecified provides little protection against the flexibility it is designed to constrain. Research by Thibault et al. (2021) found significant variability in preregistration quality, and noted that coders only agreed on the identification of the primary hypothesis in 14% of unstructured preregistrations they reviewed. This finding underscores the importance of using a structured template and writing with sufficient precision that an independent analyst could implement your analysis plan from the document alone.

It does not prevent all forms of bias. Preregistration addresses analytic flexibility and selective reporting of outcomes. It does not directly address selection bias in sampling, measurement error, demand characteristics, or file-drawer effects on the preregistrations themselves (researchers who preregister but never publish negative results still contribute to the latter).

Understanding how these transparency practices connect to the broader architecture of research design is essential for any researcher aiming to produce work that will withstand methodological scrutiny. For a comprehensive treatment of study design from the ground up, see our guide on how to write a research proposal.

Frequently Asked Questions

References

• Nosek, B. A., Ebersole, C. R., DeHaven, A. C., & Mellor, D. T. (2018). The preregistration revolution. Proceedings of the National Academy of Sciences, 115(11), 2600–2606. https://doi.org/10.1073/pnas.1708274114
• Thibault, R. T., Amaral, O. B., Argolo, F., & Bandrowski, A. E. (2021). Ensuring the quality and specificity of preregistrations. PNAS, 118(32), e2104198118. https://pmc.ncbi.nlm.nih.gov/articles/PMC7725296/
• Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22(11), 1359–1366.
• Willroth, E. C., & Atherton, O. E. (2024). Best laid plans: A guide to reporting preregistration deviations. Advances in Methods and Practices in Psychological Science. https://doi.org/10.1177/25152459231213802
• Center for Open Science. (2026). Preregistration. https://www.cos.io/initiatives/prereg
• Center for Open Science. (2026). Registered Reports. https://www.cos.io/initiatives/registered-reports
• Open Science Framework Help. (2026). Welcome to Registrations & Preregistrations. https://help.osf.io/article/330-welcome-to-registrations