by godelski | 2026-03-11 22:52:10+00:00
> physics tends to be much more reproducible than biology, and CS doubly-so.

With physics I think there is a better culture of reproduction, but that is, I believe, due more to culture. That it is acceptable to "be slow". There's a high stress on being methodical and extremely precise. The prestige is built on making your work bulletproof, and so you're really encouraged to help others reproduce your work as it strengthens it. You're also encouraged to analyze in detail and to faithfully reproduce, because finding cracks also yields prestige. I don't know if it's the money, but no one is in it for the money. Physics sure is a lot harder than anything else I've done and it pays like shit.

For CS the problem is wildly different. It should be easy to reproduce as code is trivial to copy. Ignoring the issue of not publishing code alongside results, there's also often subtle things that can make or break works. I've found many times in replication efforts that the success can rely on a single line that essentially comes form a work that was the reference to a reference of the work I'm trying to reproduce. The problem here is honestly more of laziness. In contrast to physics there's an extreme need for speed. In physics (like everyone else I knew) I often felt like I was not smart enough, and that encouraged people to dive deeper and keep improving or to give up. In CS (like everyone else I knew) I often felt like I was not fast enough, and that encouraged people to chase sponsorships from labs that provided more compute, it encouraged a "shotgun" approach (try everything), or for people to give up (aka "GPU poor").

The reason I'm saying this is because I think it is important to understand the different cultures and how replication efforts differ. In physics a replication failure was often assumed to be due to a lack of intelligence. In CS a replication effort is seen as a waste of time. Both are failures of the scientific process. Science is intended to be self-correcting. Replication is one means of this, but at its heart is the pursuit of counterfactual models. This gives us ways to validate, or invalidate, models through means other than direct replication. You can pursue the consequences of the results if you are unable to pursue the replication itself. This is almost always a good path to follow as it is the same one that leads to the extension and improvement of understanding.

There's a lot I agree and disagree with from Dr Bissell's article. Our perspectives may differ due to our different fields, but I do think it also serves as some a point of collaboration, if not on the subject of meta-science. Biology is not unique in having expensive experiments. I want to point out two famous and large physics projects: the LHC's discovery of the Higgs Boson[0] and LIGO's Observation of a Gravitational Wave[1]. The former has 9 full pages of authors (IIRC over 200) while the latter has about 3. These works are both too expensive to replicate while also demonstrating replication. Certainly we aren't going to take another 2 decades to build another CERN and replicate the experiments. But there's an easy to miss question that might also make apparent the existence of replication: who is qualified to review the paper and is not already an author of it? There's definitely some, but it really isn't that many. In these mega projects (and there are plenty more examples) the replication is done through collaboration. Independent teams examine the instruments that make the measurements. Independent teams make measurements, using the same device or different devices (ATLAS isn't the only detector at CERN), different teams independently analyze and process the information, and different teams model and simulate them. With LIGO this is also true. It would be impossible to locate those black holes without at least 2 facilities: one in Hanford (Washington) and the other in Livingston (Louisiana) (and now there's even more facilities). Astrophysics has a long history of this type of replication/collaboration as one team will announce an observation and it is a request for other observations. Observations that often were already made! In HEP (high energy particle physics) this may be less direct, but you'll notice other particle physics labs are in the author list of[0]. That's because despite the exact experiment not being replicatable in other facilities, there are still other experiments done. In the effort to find the Higgs there were many collisions performed at Fermi Lab.

I don't think this same in biophysics, but I think there are nuggets that may be fruitful. Bissell mentions at the end of her argument that she believes replication might have higher success were labs to send scientists to the original labs. I fully agree! That would follow the practice we see in these mega experiments in physics. But I also do think she's brushing off an important factor: it is far quicker and cheaper to replicate works than it is to produce them. You're a scientist, you know how the vast majority of time (and usually the vast majority of money) is "wasted" in failures (it'd be naive to call it waste). Much of this goes away with replication efforts. The greater the collaboration the greater the reduction in time and money.

And I do agree with Bissell in that we probably shouldn't replicate everything[2]. At least if we want to optimize our progress. But also I want to stress that there is no perfect system and there are many roadblocks to progress. Frankly, I'd argue that we waste far more time in things like grant writing and publication revisions. I don't know a single scientist who hasn't had a work rejected due to reviewers either not giving the work enough care or simply because they were unqualified (often working in a different niche so don't understand the minutia of the problem). As for the grant writings, I think they're a necessary evil but I'm also a firm believer of what Mervin Kelly (former director of Bell Labs) said when asked how you manage a bunch of geniuses: "you don't"[3]. You're a scientist, an expert in your domain. You already know what directions to look in. You've only gotten this far because you've been honing that skill. We don't have infinite money, so of course we have to have some bar, but we can already sniff out promising directions and we're much better at sniffing out fraud. Science has been designed to be self-correcting.

[More of a side note]

> Usually the answer is, "if you're a good researcher and can't reproduce my work, you come to my lab and reproduce it there" because the variables that affect this are usually things in the lab- the temperature, the reagents, the handling.

And we should not undermine the importance of these variables. Failures based on them are still informative. They still inform us about the underlying causal structure that leads to success. If these variables were not specified in the paper, then a replication failure shows the mistake of the writing. Alternatively a failure can bound these variables, by making them more explicit. I'm no expert in biophysics, but I'm fairly certain that understanding the bounds of the solution space is important for understanding how the processes actually work.

[0] https://arxiv.org/abs/1207.7214

[1] https://arxiv.org/abs/1602.03837

[2] I also would be very cautious about paid replication efforts. I am strongly against it as well as paywalls on publishing (both in creation of publication as well as the access of).

[3] https://1517.substack.com/p/why-bell-labs-worked