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.

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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.