A man in a lab coat hunches under a dim light, his eyes intently focused on a microscope, fueled only by caffeine and anticipation.
This lone scientist will remain at work until he uncovers the truth about the source of the dangerous disease rapidly spreading through his vulnerable city. Time is short, the stakes are high, and only he can save everyone. …
That kind of romanticized view of science was standard for a long time. But it is as far removed from actual scientific practice as a choreographed martial art in a movie is from a real fistfight.
For most of the 20th century, philosophers of science like myself held somewhat idealistic claims about what good science looks like. In recent decades, however, many of us have revised our views to better reflect actual scientific practice.
An update on what we can expect from real science is long overdue. I often worry that when the public holds science to unrealistic standards, any scientific claim that doesn’t meet those standards will be met with suspicion. While public trust is strong globally and has been for decades, it is eroding. In November 2023, Americans’ trust in scientists was 14 points lower than it was just before the COVID-19 pandemic, with its flood of confusing and sometimes contradictory scientific messages.
When people’s expectations about how science works aren’t met, they might blame scientists. But adjusting our expectations might be more helpful. Here are three updates that I think can help people better understand how science actually works. Hopefully, a better understanding of actual scientific practice will also build people’s trust in the process.
The many faces of scientific research
First, science is a complex enterprise with multiple goals and associated activities.
Some scientists look for the causes of an observable effect, such as a devastated pine forest or a rise in the Earth’s surface temperature.
Others may investigate the what rather than the why of things. For example, ecologists build models to estimate the abundance of gray wolves in Montana. Spotting predators is incredibly challenging. Counting them all is impractical. Abundance models are neither complete nor 100% accurate—they provide estimates that are considered good enough to set harvest quotas. Perfect scientific models are simply not in the cards.
In addition to the what and the why, scientists can focus on the how. For example, the lives of people with chronic diseases can be improved by researching strategies for coping with illness—to alleviate symptoms and improve function, even when the real causes of their conditions largely elude current medicine.
It is understandable that some patients become frustrated or distrustful of healthcare providers who cannot provide clear answers about the cause of their ailment. But it is important to understand that much scientific research focuses on how to effectively intervene in the world to achieve specific goals.
Simplistic views present science as solely concerned with providing causal explanations for the various phenomena we observe in this world. The truth is that scientists tackle all sorts of problems, which are best solved using different strategies and approaches, and which only sometimes yield complete explanations.
Complex problems require complex solutions
The second aspect of scientific practice worth emphasizing is that, because scientists tackle complex problems, they typically do not offer a single, complete, and perfect answer. Instead, they consider multiple, partial, and possibly conflicting solutions.
Scientific modeling strategies illustrate this point well. Scientific models are typically partial, simplified, and sometimes deliberately unrealistic representations of a system of interest. Models can be physical, conceptual, or mathematical. The critical point is that they represent target systems in ways that are useful in specific contexts of inquiry. Interestingly, considering multiple possible models is often the best strategy for tackling complex problems.
Scientists consider multiple models of biodiversity, atomic nuclei, or climate change. Returning to wolf abundance estimates, multiple models may also suffice. Such models are based on a variety of data, including acoustic studies of wolf howls, genetic methods using wolf fecal samples, wolf sightings and photographic evidence, aerial photographs, snow track studies, and more.
Weighing the pros and cons of different possible solutions to the problem of interest is an integral part of the scientific process. Interestingly, in some cases, using multiple conflicting models can produce better predictions than trying to combine all the models into a single model.
The public may be surprised and possibly suspicious when scientists advance multiple models based on conflicting assumptions and make different predictions. People often think that “real science” should provide definitive, complete, and watertight answers to their questions. But given the various constraints and the complexity of the world, maintaining multiple perspectives is often the best way for scientists to achieve their goals and solve the problems.
Science as a collective, counterintuitive enterprise
Finally, science is a collective endeavor, where healthy disagreement is a feature, not a flaw.
The romanticized version of science depicts scientists working in isolation and establishing absolute truths. Instead, science is a social and adversarial process in which community control ensures that we have the best available knowledge. “Best available” does not mean “final,” but the best we have until we figure out how to improve it. Science almost always allows for disagreement among experts.
Controversies are essential to how science works best and are as old as Western science itself. In the 17th century, Descartes and Leibniz fought over how best to characterize the laws of dynamics and the nature of motion.
The long history of atomism offers a valuable perspective on how science is a complicated and tortuous process rather than a fast-paced system of results set in stone. While Jean Baptiste Perrin was conducting his experiments in 1908 that seemingly settled all debate about the existence of atoms and molecules, questions about the properties of the atom were about to become the subject of decades of controversy with the birth of quantum physics.
The nature and structure of fundamental particles and associated fields have been the subject of scientific research for more than a century. There are lively academic discussions about the difficult interpretation of quantum mechanics, the challenging unification of quantum physics and relativity, and the existence of the Higgs boson, among many others.
It is largely misplaced to distrust researchers because they have healthy scientific disagreements.
A very human practice
To be clear, science is dysfunctional in some respects and contexts. Current institutions have incentives for counterproductive practices, including maximizing publication rates. Like any human endeavor, science involves people with bad intentions, including some who seek to discredit legitimate scientific research. Finally, science is sometimes inappropriately influenced by different values in problematic ways.
These are all important considerations in evaluating the reliability of specific scientific claims and recommendations. However, it is unfair, sometimes dangerous, to distrust science for doing what it does best. Science is a multifaceted enterprise that aims to solve complex problems that typically have no simple solutions. Communities of experts explore those solutions in the hope of providing the best available approach to addressing the problems of interest.
Science is also a fallible and collective process. Ignoring the reality of that process and holding science to unrealistic standards can lead the public to hold science accountable and lose confidence in its reliability for the wrong reasons.
This article is republished from The Conversation, a non-profit, independent news organization that brings you facts and reliable analysis to help you understand our complex world. It was written by: Soazig Le Bihan, University of Montana
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Soazig Le Bihan receives funding from the Maureen and Mike Mansfield Center at the University of Montana.