Cargo cult science

Feynman coined the term "cargo cult science" during a commencement address. This term describing research that is aimed at confirming an assumed hypothesis became more widely known after the address was incorporated into the final chapter of his book Surely You're Joking, Mr. Feynman! Methods which superficially seem scientific will ultimately fail to deliver if researchers lack "utter honesty" - not just avoiding falsehoods, but bending over backwards to state all the possible flaws in your research. The latter is scientific integrity, the former is advertising.

Feynman argued adamantly against fooling the layman when talking about your research. He gives an example of an astronomer friend who asked what applications of his work he should mention in a radio interview. Feynman retorted "there aren't any" and the friend was dismayed because saying that would not attract continued funding support for his research.

This message remains relevant today, especially with increasing competition for grant funding and faculty positions, high impact journals with strict length limits, and big conferences with short talks. Even when we agree with being honest and discussing flaws in our research in principle, excuses inevitably come up:

"I don't have time to discuss limitations - I only have 10 minutes including questions."

"My peers who publish in Top Journal all start their papers this way - it's the only way to make it past the editor." 

"Unless I frame my proposal in terms of this Grand Challenge it will not be funded."

"I have to play this game until I get tenure, and then I will be free to do honest old-fashioned research."

"I just need this grant so I can extend my postdoc's contract..."

The end result: Paper introductions and grant applications written by large language models, because they can sell the science in a more exciting way (weasel words can be inserted to smooth over overt factual errors). Seminars where the speaker boldly claims application X in the introduction, only to backtrack when questioned after the talk (lucky there was an expert present to point out a key flaw known by specialists in the topic). Researchers wasting months on ideas that were already tried and didn't work (no rewards for publishing negative results).

It doesn't need to be this way.

 

If you think there is not enough scientific integrity nowadays, you can help by participating in peer review and questioning unsubstantiated claims and excessive hype in the right way.

You should be curious and respectful, not belligerent and dismissive. Recommending rejection on the basis of how the broader context of the results are sold (rather than the results themselves) rarely leads to a constructive outcome - either the authors will ask for your opinion to be dismissed, or they will publish the offending claims unaltered in another venue. Instead you could ask the authors to explain in more detail how approach X is expected to help goal Y and possible flaws to better put the work in context. 

The same approach is also useful for Q&A sessions after talks. Often, the speaker is well aware of certain gaps in the logic of the presentation but didn't have the time to elaborate on them.  Questions in this vein help them to better convey the important unanswered questions in their research topic and are valuable to both the speaker and the audience.

The system has too much inertia to change immediately, but by putting the broader context and salesmanship behind the research under closer scrutiny you can help to diminish the influence of cargo cult science.

Physics models that are wrong but useful

 "All models are wrong, but some are useful" is a saying usually attributed to statistician George Box. In physics we are often tempted to create a model that might be correct, but ends up being hopelessly useless. 

For example, the multi-particle Schrodinger equation in principle can give us an exact description of the energy levels of any molecule we would like to study, underlying the field of ab-initio quantum chemistry. But it cannot be solved except for the simplest of molecules. Heuristic approximation schemes which may not rigorously justified are essential to obtain useful predictions for large problems of practical interest. String theory is another example, with some arguing it is not even wrong.

There are many neat examples of models that, while wrong, lead to useful predictions and progress in our understanding:

• The Drude model of electrical conductivity. In the original paper there was a fortuitous cancellation of two big errors yielding agreement with experimental data for the specific heat. Nevertheless, the model remains a very good approximation for the frequency-dependent conductivity of metals.
• Conductivity at low temperatures: Before 1911 there were various predictions for the resistivity of metals cooled to zero temperature: zero, a finite value, and even infinite (argued by Lord Kelvin). Efforts to determine which prediction was correct led to the unexpected discovery of superconductivity.
• The Quantum Hall effect: quantization of the Hall conductivity was originally predicted in the absence of scattering, and thus the quantization was expected to only hold to a finite precision. Effects to measure a finite accuracy of the quantization led to the Nobel Prize-winning experiments.

A good model doesn't need to be 100% correct. A good model needs to give an actionable prediction.