Everyday Scientist

The Case for Preregistration in Cell Biology: A Practical Guide

January 23, 2026 at 5:00 pm | sam | literature, science community, scientific integrity

The basics of the scientific method—hypothesize, test, revise—rely on prediction. By forming a hypothesis only after seeing the data, we fail to severely test our ideas. I encourage cell biologists to preregister their study designs and statistical analyses. I also provide practical tutorials for making this a reality.

Preregistration will Bolster Cell Biology

Preregistration has helped add rigor to various fields, including psychology and clinical trials (Nosek et al., 2018). But in cell biology, molecular biology, and other basic research, preregistration has not become popular, at least partially due to a general belief that it would be overly burdensome and impractical in exploratory experiments. Here, I attempt to dispel those myths and provide practical steps for cell biologists to preregister their experiments and data analysis. Instead of hindering research activity, preregistration can aid experimentalists in designing better experiments, reducing waste, and adding rigor to their science.

While preregistration may sound daunting, it need not be. Most of us already write down a basic experimental plan in our lab notebook before we step into the lab, so uploading that to a repository (public or private) is a small effort. But that simple gesture could encourage researchers to flesh out their plans and more carefully consider potential pitfalls, like not having a sufficient sample size. There is nothing more annoying than completing weeks in the lab only to realize that you forgot an essential control or really needed an additional replicate to get a statistically significant result. Better planning can help avoid wasted effort, and preregistration helps. Getting feedback on your plan with labmates and colleagues is easier with a formal plan, and preregistration ensures that the PI and all the researchers on the project are on the same page.

We all know that arranging the data analysis procedure before seeing the results is good scientific hygiene. Preregistration forces us to make it a habit to plan the analysis, thereby reducing the temptation to p- and N-hack our way to “significance.” Furthermore, listing a priori plans and any deviations in the final paper adds transparency and trust to scientific publishing. Finally, committing to disclose results before the outcome is known helps alleviate the problem of selective reporting in scientific publishing.

Ancillary Benefits

Taking the time to plan an adequate study design before stepping into the lab is of course beneficial in itself. But preregistering an analysis plan could actually increase statistical power and even reduce the number of required samples. For instance, if the hypothesis predicts a directional effect of a treatment, researchers can plan for a one-sided t-test, which requires fewer samples to achieve the same power (Lakens, 2022). While it wouldn’t be fair to opt for a one-sided test after collecting the data and learning the direction of the effect, documenting the bold prediction of a directional effect beforehand is in some ways superior to the standard two-sided test. (Convincing reviewers of this may be a challenge, but they should look favorably on the transparency and rigor of preregistration.)

Preregistration can further reduce the burden on cell biologists by allowing them to “peek” at their data partway through completion of an experiment, providing the opportunity to stop data collection with fewer replicates if the effect is sufficiently large. “N-hacking” is when researchers arbitrarily add samples until they reach a “significant” result, but with proper planning using sequential analysis—correcting the α level for multiple peeks—one can possibly stop early while maintaining the desired false positive rate (Lakens, 2022; Schott et al., 2019). A major benefit of preregistering a sequential analysis is that the expected number of biological replicates is actually smaller than the maximum sample size you would plan, because peeking allows the possibility of stopping the experiment early with an already significant p-value (Lakens, 2022). This is a common approach in clinical trials, and cell biologists could benefit from borrowing this statistically rigorous way to make our lives easier. (Note that at very small sample sizes, like n=3 per stage per condition, corrections like Pocock have inflated false-positive rates. Rom-McTague (Rom and McTague, 2020) is a modified Pocock correction for small n.)

Finally, preregistering a plan can help make null results more credible to editors and reviewers, making it easier to publish all your results. Communicating the full picture, even the experiments that didn’t match your expectations, strengthens the integrity of your science by allowing you to tell the full story. Unfortunately, journals often make it (unofficially) difficult to publish results that do not have statistical significance or are otherwise ambiguous. Preregistration gives authors ammunition to justify including null results in their published findings.

Encouraging Preregistration

Doing robust science is a reward in itself, and the vast majority of researchers already strive to do science right. If preregistration became a norm—just like displaying scale bars on microscopy images, providing raw data, uploading structural data to PDB, sharing code on GitHub, reporting sample sizes, including loading controls for Westerns, and showing uncropped gels in the supporting information—then scientists would eventually preregister as a habit.  

But some external encouragement couldn’t hurt. Journals and reviewers could ask for preregistration documentation, and reward authors who can produce such plans. For example, when reviewer 2 asks for additional experiments or more replicates, editors should be emboldened by a preregistration to politely disregard such demands (when they are indeed unreasonable). Furthermore, editors should feel more willing to accept negative or ambiguous results when the experiment was preregistered. And journals should prominently display a link or badge on papers that provide preregistration documentation.

A Practical Guide to Preregistering

The OSF repository provides resources for recording preregistration reports (Open Science Framework, 2023), including what to do when plans change, and others have expanded on the benefits and practical application of preregistration in small studies (Johnson and Cook, 2019). Here, we provide a crude template (Box 1) for a simple preregistration. See Box 2 for example paragraphs describing the preregistration in the final manuscript. As your plans develop while you traverse a project, you can augment the preregistered plan with additional experiments. Because most cell biology papers include several smaller experiments, the preregistered plan can include descriptions of multiple experiments, or you may opt to submit a separate short plan for each experiment.

Common Objections

I don’t want my competitors knowing my experiments before I even start.

Preregistrations need not be public to be effective. Depositing a preregistration privately (or even simply writing it down, timestamping it, and storing it on a local computer) still keeps researchers accountable and the research design more robust against post-hoc statistical exploration. The plan can eventually be made public to peer reviewers and readers of the ultimate publication. That said, making a plan public—or at least sending it to some trusted colleagues—offers an opportunity to get helpful feedback on an experiment before you even enter the lab.

My experiments are too exploratory to plan ahead.

It probably doesn’t make much sense to preregister pilot studies and research that is still in the beginning, exploratory stages. So maybe 90% of the work you do in the lab—culturing and observing cells, developing assays, purifying proteins, making and characterizing reagents, performing chemical analyses, and running pilot experiments—doesn’t need a preregistration. But before you sit down to start an actual experiment, it is worth planning: write down, discuss, and preregister how you will tackle the project. It is challenging to recognize precisely when research shifts from exploratory to a confirmatory experiment, and I don’t have a perfect prescription for identifying which work deserves preregistration, but most scientists know in their gut when their efforts are destined for a figure in a paper. When in doubt, just fill out the preregistration template (Box 1); you can always discard it if it turns out it wasn’t needed or update it as necessary as the project progresses. That shouldn’t take too much time, and it might help focus your attention on deficits in your plan early on.

This is too much work.

Preregistering adds little administrative burden, since the hypothesis, research plan, and often even the statistical analyses have already been laid out in research grant applications, lab notebooks, or thesis committee presentations. But taking the simple steps of formally organizing and uploading these plans to a repository ensures you have a clear strategy and helps avoid common pitfalls of poor experimental planning. Each experiment can be preregistered on its own, so there’s no need to decide what the ultimate paper will be during the early planning stages.

This will lock me into a plan that might send me down the wrong path.

Preregistration is a plan, not a prison (Open Science Framework, 2023). If plans change before collecting the data, it’s as simple as updating the preregistered documents. If you perform unplanned tests or data analysis during or after the experiment, you can transparently report deviations from the plan and justifications in the final paper (Lakens, 2024). If you make an unexpected discovery or do additional experiments not listed in your original preregistration, you can either update the preregistration or simply note in the final manuscript that that particular experiment had not been listed in the original preregistered plan. This adds transparency and empowers the reader with useful information. “Having  preregistered  your  study  does  not  prevent  you from  publishing  any  analysis  you  believe  is  interesting  or informative. All that preregistration does is clarify what tests were formulated a priori and what tests were not” (Forstmeier et al., 2017).

What if the experiment doesn’t give the result I expected? Am I committed to trying to publish negative results?

Preregistration doesn’t obligate you to publish every result: you’re still free to tell the story the way you wish. The difference is that, when you document your full process, the readers will know that the findings are not cherry-picked, adding credibility and transparency to whatever you ultimately publish.

My Personal Experience with Preregistration

This is where I am seeking input from anyone who has ever preregistered their experiments before. For my next experiment, I will preregister the plan and the analysis approach and include that when we submit to a journal. But that will be months away. Has anyone already done this and want to expound upon the process and the outcome?

Conclusion

The value of preregistration has been demonstrated for years in clinical research and other fields, and it is time to apply this proven technique to cell biology. Of course, preregistration is not magic: it will not singlehandedly solve the replication crisis, eliminate fraud, or make all research findings true. Improving science will take a concerted effort by journals, authors, and funders to better incentivize transparent protocols and more rigorous testing of findings (Forstmeier et al., 2017; Kohrs et al., 2023; Lord et al., 2026). But this small yet powerful step would increase rigor in scientific research and help strengthen the literature. Ultimately, if you completely ditch your preregistered plan and don’t even mention it in your paper, while this is dishonest, it’s no worse off than the status quo. So there’s really no harm in experimenting with preregistration.

References

• Forstmeier, W., E.-J. Wagenmakers, and T.H. Parker. 2017. Detecting and avoiding likely false-positive findings – a practical guide. Biol. Rev. Camb. Philos. Soc. 92:1941–1968. doi:10.1111/brv.12315.
• Johnson, A.H., and B.G. Cook. 2019. Preregistration in Single-Case Design Research. Except. Child. 86:95–112. doi:10.1177/0014402919868529.
• Kohrs, F.E., S. Auer, A. Bannach-Brown, S. Fiedler, T.L. Haven, V. Heise, C. Holman, F. Azevedo, R. Bernard, A. Bleier, N. Bössel, B.P. Cahill, L.J. Castro, A. Ehrenhofer, K. Eichel, M. Frank, C. Frick, M. Friese, A. Gärtner, K. Gierend, and T.L. Weissgerber. 2023. Eleven strategies for making reproducible research and open science training the norm at research institutions. eLife. 12. doi:10.7554/eLife.89736.
• Lakens, D. 2022. Improving Your Statistical Inferences.
• Lakens, D. 2024. When and how to deviate from a preregistration. Collabra: Psychology. 10. doi:10.1525/collabra.117094.
• Lord, S.J., A. Charles-Orszag, K. Skruber, R.D. Mullins, and A. Rehfeld. 2026. Peer replication : A new tier of science built on reproducibility. EMBO Rep. doi:10.1038/s44319-026-00705-8.
• Nosek, B.A., C.R. Ebersole, A.C. DeHaven, and D.T. Mellor. 2018. The preregistration revolution. Proc Natl Acad Sci USA. 115:2600–2606. doi:10.1073/pnas.1708274114.
• Open Science Framework. 2023. Preregistration – OSF Support.
• Rom, D.M., and J.A. McTague. 2020. Exact critical values for group sequential designs with small sample sizes. J. Biopharm. Stat. 30:752–764. doi:10.1080/10543406.2020.1730878.
• Schott, E., M. Rhemtulla, and K. Byers-Heinlein. 2019. Should I test more babies? Solutions for transparent data peeking. Infant Behav. Dev. 54:166–176. doi:10.1016/j.infbeh.2018.09.010.

____________________________________________________

____________________________________________________

Box 2: Example paragraphs for eventual manuscript

We preregistered our research plans and statistical analyses (link to plans).

Based on a small pilot experiment, we predicted that abc drug treatment would reduce cell speeds relative to control, so we proposed a one-tailed t-test with α=0.05. Based on the Cohen’s effect size of d=2 from the pilot experiment, we proposed n=8 biological replicates (4 control and 4 treated), which should give a power of around 80%. After the experiment, the p-value was 0.043. However, we noticed after the data was collected that there was substantial variation in the speed of the control cells on different days. Because control and treated samples were split from the same flask of cells for each day’s experiment, we subsequently concluded that a paired t-test was the more appropriate approach and therefore departed from our preregistered analysis plan. The one-sided paired t-test resulted in p=0.011. Results are shown in Fig 2.

Also, after observing the full results, we noted that the nuclei seemed smaller in the drug-treated samples. Although not in our original preregistered plan, we decided to also compare the size of the nuclei in control vs. treated. We blinded the filenames and measured the area of the nuclei of all the cells in the dataset. The two-tailed paired t-test resulted in a p=0.034. Results are shown in Fig 3.

For the internalization experiment, we predicted that knockdown of gene xyz would abrogate endocytosis. We proposed a one-sided paired t-test with α=0.05 and planned n=6 biological replicates (3 control paired with 3 treated). However, our results showed no obvious trend in the internalization rate and the p-value was nonsignificant at p=0.31. Although the preregistered plan calculated that the experiment was adequately powered to detect a large biologically meaningful effect, the sample-to-sample variance was larger than we had anticipated, so we cannot conclude definitively that knockdown of gene xyz does not alter endocytosis rates, but it does set a floor for the effect size. Future tests of this hypothesis will need to either reduce the experimental noise or increase the number of replicates. This null result is reported in Fig S1.

For the neuron experiment, we proposed using an unpaired design, and we planned to use male mice from the same litter. We proposed to sacrifice control and knockout mice, fix and stain brain tissue, and image the average intensity of xyz marker in 100 neurons from each mouse. Sample collection was performed blinded with respect to the identity of the sample. We proposed a two-tailed t-test to calculate the p-value, because we did not know whether the marker would increase or decrease with gene knockout. To potentially reduce the number of mice needed, and based on an anticipated effect size of approximately 1.5, we planned a two-stage sequential analysis with n=5 mice per stage per condition; to account for multiple assessments of the data, we proposed the Rom-McTague α of 0.0285 for each stage, for a cumulative α of 0.05. After n=5 knockdown and 5 control mice, we calculated p=0.024, so we halted the experiment according to our plan. Results are shown in Fig 5.

PromoPlot

January 23, 2026 at 4:23 pm | sam | crazy figure contest, literature, nerd

This is hilarious: https://arxiv.org/abs/2503.24254

end grant writing; award prizes for papers instead

January 20, 2026 at 4:52 pm | sam | literature, science and the public, science community, scientific integrity

My proposal for funding academic research science: instead of funding grants, just award financial prizes for good papers.

Implementation

Award half the prize upon publication (or preprint); award the second half upon an independent replication of the results, 25% to the original authors and 25% to the replicators. (If a good-faith replication attempt fails, the replicators would still receive their portion, but the original authors would not receive their replication bonus. The measure for the quality of the replication would be whether the work could be published in a reputable journal.)

Prize winning papers would be chosen by rotating committees of professors following a rubric announced prior to the prize committee meeting (replacing grant study sections). Different committees could be assembled for different research topics, with appropriately tailored rubrics. Any paper published in the prior 12 months would be eligible during each prize committee meeting (there could be multiple committees convened each year, giving any given paper multiple chances at winning). Some committees would be assembled to identify “sleeper” papers: works that are up to 5 years old that had been overlooked by previous committees but have proven to be especially valuable.

The prize amount could vary by field or even by paper. A typical R01 grant is $150k+ annually for 5 years. So prizes could vary from $200k up to $1M+ for a major tour-de-force paper with substantial impact. The goal would be to distribute the same pot of money to approximately the same number of labs in roughly the same proportion, but with a much smaller bureaucratic burden and with better incentives for reproducibility.

There would probably need to be some cap on the total prize money an individual PI could receive at any one time. If they are over the cap, then either their papers would simply not be eligible for a prize that round or their prize money would flow to the collaborators on that paper or department.

Prize money would still go to the PI and lab via the university, not as a blank check to an individual professor. Indirects/overhead would still be applied. The goal would be to have approximately the same amount of money flowing to the university and the labs, but with a less onerous process.

Advantages

The prize approach accomplishes the following:

• Rewards actual outputs rather than promises.
• Creates strong incentives for reproducible work.
• Incentivizes impactful, innovative results that are convincing and accepted by the scientific community.
• Frees up professors’ time to focus on paper writing and mentoring instead of grant writing.
• Reduces the workload for university grant offices, which currently spend considerable time reviewing and approving proposals before submission, thereby reducing costs and freeing administrative staff at universities and funding agencies to focus on other support functions.
• Removes back-and-forth about budgets, justifications, biosketches, data-sharing policies, etc.
• Does away with byzantine formatting requirements.
• Obviates¹ the need for progress reports and renewals throughout the grant period.

Professors spend a huge amount of time writing grants, and university grant offices dedicate substantial resources helping researchers navigate complex formatting and compliance requirements, including page lengths, fonts, hyperlinks, sections, etc. Academic researchers currently focus on publishing their work, so grants should reinforce that drive, not distract PIs with hundreds of hours of grant writing. Prize committees would replace grant review panels (roughly equivalent work), while reducing the administrative burden on both university grant offices and granting agencies.

The current grant funding structure purports to be forward-looking, providing funding for good ideas to be implemented. But actual grants require not only a good track record for the PI, but also a large amount of preliminary data. So, in effect, grants fund a lot of work that’s already been completed, and the awarded money usually gets spent on lab work outside the scope of the original proposal. So, while appearing to be prospective, grants are often retrospective in practice. Granting prizes for good papers simply makes official how funding already flows in reality.

How to fund research before winning prizes

The central challenge would be providing runway funding, especially for early-career researchers who have no eligible papers yet. With fewer resources needed for proposal preparation support, some overhead funds could be redirected to departmental slush funds. Departments could then provide startup packages, bridge funding between prizes, or support for labs that demonstrate progress toward publishable work.

This would shift evaluation of proposed research from granting agencies to local departments, where a lab’s potential is more knowable. However, departments are often rife with unhealthy politics and lack sufficient guardrails against favoritism and bias. Whether departmental evaluation would be less burdensome than current grants – and whether freed-up overhead would provide sufficient funding – remains an open question.

Alternatively, granting agencies could dedicate some funds for prizes awarded specifically to a professor’s first paper, or maintain a smaller pot of traditional proposal-style funding for early-career researchers. But as it stands, professors who don’t publish within their first few years are unlikely to receive traditional grants or be awarded tenure anyway, so the prize approach doesn’t necessarily create more pressure than the current system.

Downsides

Not every good paper would win a prize. Yes, of course there would still be disappointing results: inevitably, some deserving papers would fail to attract money because the subject matter is out of fashion or reviewer bias or a host of other unfair reasons. But with a limited amount of money and an effectively unlimited number of professors, it’s impossible to fund everyone, so someone will always be neglected. Winning a prize would be no more unpredictable than receiving a good score on a grant. My proposal is no less fair than the whims of the current granting process.

Incentivizes short-term work. Scientists would be less motivated to tackle a longer-term project, even if it is promising. The department or other funding agency would need to step in to float the project for years until it comes to fruition. And if whoever funds that float requires proposals to have the same level of detail and complexity as current grant proposals, we’d be back to square one. But proposals for future work need not be as onerous as current NIH regulations, especially if funded by the department or by private organizations like HHMI. Another source of funding for longer-term projects may actually come from paper prizes: professors who have won multiple prizes in the recent past would have ample money to fund collaborations and longer projects.

Disincentivizes high-risk research. Researchers might be wary of taking on high-risk/high-reward projects, because, if the experiments fail, they couldn’t expect a prize. That said, if such a project succeeds, then the prize money would likely be larger. Such is the nature of high-risk/high-reward. Therefore, the prize money for impactful work would need to be sufficiently large to encourage at least a portion of researchers to take on projects that are higher risk. Furthermore, if researchers are able to publish details of a failed “moonshot” that advanced the field despite not succeeding, such a paper would be eligible for a prize. The flexibility of prize funding might actually enable more ambitious research, since successful researchers would have discretion over how to deploy their winnings without the constraints of grant-specified aims.

Disincentivizes collaborations. Collaborators would likely negotiate some cut of a potential prize ahead of time, or there would be a standard formula (e.g. 75% to the last author and the remainder of the prize to the other PI collaborators on the paper). Maybe PIs would be less willing to add collaborators to a project, for fear that they may need to share some of the prize money with other PIs. Maybe there could be a bonus to help support collaborators.

Expensive projects. This would not work for huge projects like clinical trials or massive particle physics consortiums, which would need ample funding before the experiments or construction actually begin. But those could continue to be funded in the current fashion.

What will professors do with their freed up time? Part of the job of a professor is to write grants. If they suddenly don’t need to write grants, will they actually spend more time teaching and mentoring, or will they just work less? Not sure. That’s a valid point.

Moving forward

The next step would be to run a pilot. Initially, 20% of NIH and NSF funding could be converted to this model in select fields that would be amenable, then evaluated after 5 years. At that time, the percentage of funding provided via the prize mechanism might be increased or decreased based on the outcome of the pilot. It’s unlikely that granting agencies would ever completely eliminate the traditional grant proposal, but the health of academic science could be strengthened if even a minority of the funding rewarded impactful, reproducible papers.

___________________

1. Thesaurus, don’t fail me now. ↩︎