“Dumbing down” science doesn’t make it more accessible
Advice for communicating science when the details matter
Scientists get a lot of terrible advice about how to communicate about science to non-experts. Some of my least favorites are:
“Just take out all the jargon.”
“Explain it to me like I’m five.”
The instinct behind the advice is right. However, this type of advice usually just sets scientists up to turn something technical and specific into something generic and meaningless. Just taking out the big words is a superficial change that doesn’t actually improve understanding.1 It can even lead scientists to oversimplify to a degree that undermines their message.
Scientists do need to be able to communicate science in plain language, but “dumbing down” a concept or taking out all the technical details doesn’t work in many situations where scientists need to communicate a complex idea. No matter how brilliant the idea, if a scientist can’t explain key technical aspects, funders can’t evaluate the idea, experts in other fields can’t collaborate, and potential beneficiaries can’t give useful feedback.
Often, the key to improving understanding is hidden in the knowledge that scientists leave out when they write or talk about an idea. I see this across the board with Brains fellows regardless of whether the terminology they use is simple or technical. Instead of trying to figure out how to find simpler words, you should be asking:
“Have I given a smart person with no prior knowledge the relevant information they need to understand my idea?”
“Have I structured this information in a way that helps them understand why it is relevant and how it connects to my argument?”
A big part of my work coaching Brains fellows is trying to coax out the information that is so obvious to them that they aren’t even aware that they’re leaving it out. Then, I help them sort out which of those pieces of information are key to helping the reader understand their core idea and how their approach will deliver real-world impact. Examining the jargon that fellows use is often the starting point to identify those gaps, but the solution to a communication problem is rarely as simple as finding a synonym or adding a definition.
When I first started working in science communication, my job was to explain why the U.S. should invest millions of dollars into building infrastructure to ensure that we could supply ecological restoration projects with native plant seeds with the right genetic adaptations for the local conditions in which they would be planted. Using seeds with the right genes for the area can make a big difference in long-term success of restoration plantings, but it costs more up front and the benefits often don’t become evident until after the short period during which most projects can afford to monitor outcomes.
I heard the same questions again and again from the botanists I worked with: “Why don’t people get it? Why don’t they see how important it is?” They had tried all sorts of different wording (e.g. “locally adapted seed,” “genetically appropriate seed,” “the right seed in the right place”) and none really helped stakeholders understand the concept.
So, I sat down and tried to figure out how to explain the concept of “locally adapted seed” using no more knowledge than you would get in a high school biology class. That exercise made me realize that understanding that one term hinged on understanding several other fundamental (but frequently misunderstood) concepts:
What is a species?
How does natural selection work and at what scale?
How do climate or geologic factors drive natural selection?
Why is genetic diversity important within a population and between populations of the same species?
I needed 1-2 semesters of intermediate-level college biology classes to really understand those concepts, but I had to make a wide variety of people understand some key elements of those concepts in 240-character tweets or at most a 1-page document. So I spent the next year and a half thinking, experimenting, and testing different ways to explain these fundamental concepts and the impact of our native seed work. The experience helped me develop strategies that I now use with our Brains fellows as well as a deep appreciation of the difficulty of what we ask of them.
Strategies for better science communication
Here are my strategies with examples adapted from past Brains fellows.
Start basic, but work your way step-by-step to a more specific explanation of how what you’re actually doing leads to something the reader cares about.
Here’s a simplified example that lays the groundwork to help the reader understand the problem and how the proposed research—mapping proteins across multiple mental health conditions at once—enables better outcomes for patients.
Many psychiatric and neurodevelopmental conditions (e.g. autism, schizophrenia, bipolar, depression) present as spectrums of symptoms with significant overlap between conditions. There’s no genetic or molecular test for these conditions, only qualitative assessments of these overlapping symptoms. That means our current categorization of these conditions may not match the underlying biomolecular causes. If our diagnostic categories are wrong, we can’t accurately determine which treatments are effective and for whom. By building a dataset mapping proteins in patient neurons and linking them to symptoms across this range of conditions, we can recategorize patients based on quantitative biological measures and match them with the right treatment.
Notice how the explanation started with something intuitive that people likely have some personal experience with (i.e., spectrums of symptoms) and then drew a chain of explicit connections between each new concept to get us to the specific impact: better matching people with treatments that will work for them.
Figure out the most important concept and take the time to explain the pieces that are most important to understanding that concept, even if it means getting technical. Don’t try to explain everything. Stay focused on what is most important.
As an example, let’s talk about why new manufacturing methods are the bottleneck to creating high-performance protein-based fibers. There are many reasons why existing industrial fiber manufacturing methods aren’t conducive to proteins (e.g., high heat and harsh chemicals). However, the key concept is understanding how molecules are aligned during fiber formation so that’s where we’ll focus our explanation.
Fiber formation starts with a fluid and involves processes to trigger a phase change and to align the molecules. Industrial methods of fiber formation trigger the change and then pull the molecules into alignment. Natural methods of fiber formation arrange the molecules and then trigger the change once they’re in place, allowing for precise control over the molecular alignment. The control over assembly enables more complex structures with higher performance, more predictable outcomes, and more options for the design of new materials.
Getting a bit technical here clarifies (1) why natural fibers have unique properties and (2) why new manufacturing processes are required to deliver that performance. Getting into the other issues with current manufacturing feels beside the point if you’ve already convinced us that a fundamental change is necessary.
Draw clear links between ideas and don’t skip steps in your logic, even if they seem obvious to you. Accurately inferring relationships between ideas requires having a framework of knowledge and context that your audience may not have.
Here’s an example of how drawing clear links allows us to go from a description of the problem and solution to an understanding of how and why the proposed approach solves the problem.
Currently, we can only collect some key Antarctic climate data by plane, but weather conditions and distance from airports limit research flights to only 14 days a year on average. Solar-powered, stratospheric drones could bypass those limitations to collect data faster and more than 10x cheaper.
To understand why this technology solves the logistics problems, the audience needs two key pieces of knowledge: (1) The stratosphere is above clouds and weather; and (2) Antarctica gets 24 hours of daylight in the summer.
They also need to be able to make the following connections:
A drone in the stratosphere flies above weather and clouds.
Staying above weather means the drone can continue to collect data regardless of conditions on the ground.
Flying above the clouds means access to round-the-clock solar power in an Antarctic summer, which allows the drone to stay in the air for 2-3 months.
Most people probably know that polar regions have very long days in summer, but that doesn’t mean they would be able to take “above the clouds” + “solar power” + “Antarctica” and make the connection to months-long flights. Laying out the connections removes the guesswork and strengthens your argument.
Pair conceptual explanations with illustrative examples that people can put in the context of their own experiences or intuition about the world
Instead of just telling the reader that predicting animal movement can help us protect people and biodiversity, the following example takes a specific type of situation where animal movement poses a risk to both people and biodiversity, provides a sense of the scale of the problem, and details a specific intervention made possible by better forecasts.
Forecasting animal movement allows us to identify when and where humans and wildlife interact and take targeted steps to mitigate the risks in those interactions. For example, there are over 1 million vehicle collisions with large animals annually, costing more than $8B in damages and fatalities. Forecasts of precisely when and where deer and elk will migrate could reduce vehicle collisions by enabling targeted warnings, speed limit reductions, or road closures for the highest risk days and locations.
There are many other examples of negative human-wildlife interactions I could have used, some of which might be more impactful. However, this particular risk is intuitive and familiar to pretty much anyone who has learned to drive. You don’t always need to choose the simplest example, but you should stop to think about whether your example is the best fit for your audience and your argument. In fact, if I didn’t need a short blog post-friendly example here, I would actually have used the more complex example of avian flu forecasting and interventions.
Good science communication takes time but is worth the effort
Figuring out how to communicate a complicated scientific idea is slow and hard. It forces scientists to really think through the logic and organization of their ideas; and it requires scientists to put themselves in the shoes of their audience and genuinely consider what they need to know and what is relevant to them. The process will take way more thought and iteration than you expect and few people will realize just how much time and thought went into making complex concepts sound straightforward.
Taking the time to develop a really clear narrative pays off. It can be very hard to distinguish a poorly communicated, high-quality idea from a low-quality idea.2 You’ll find people are a lot more impressed with your great idea if they actually understand your idea and why it’s great.
You should definitely still try to get rid of unnecessary jargon! For more tips on how to know when something is unnecessary jargon and when it’s an important technical term, check out the “Communicating your vision” chapter in our Research Leaders’ Playbook.
By low-quality ideas, I mean ideas that are incomplete, flawed or even deliberately misleading.