What does it mean to synthesize? I trolled the web and found some sources, and combined them with my own observations into the following post.

Slide02Synthesis combines information from two or more sources. The sources could include published peer-reviewed literature, articles, essays, and books, but also lectures, seminars, interviews, data, observations, and personal experience.
You synthesize all the time—when you identify relationships between something you’ve read online and something you’ve seen for yourself. When you make a decision about something, like buying a car or renting an apartment, you are synthesizing information. Maybe you ask your friends or family for advice. You research cars online or set up appointments to view apartments. You check and talk to other tenants. Then, in your mind, you pull these various bits into some kind of picture that helps you make a decision.

As you begin to write a synthesis, you accurately report information from the sources using paraphrase and/or direct quotation. (Learn more about how to avoid plagiarism by paraphrasing correctly.)

Slide04But a synthesis is more than combining a spectrum of source material into a single document. Why? Because you also have to use your own mind and words to draw connections between the sources, and using these connections to relate the different texts in a way that illuminates and transforms the material. Synthesizing sources is a matter of pulling them together into some kind of harmony.  You may have to consider whether what seem like unrelated elements or opposite observations might be reconciled. You may have to create an umbrella idea, some larger argument under which several observations and perspectives might stand.

The information should be organized and presented in such a way that readers can identify the various sources, and how they overlap. Finally, the synthesis makes sense of the sources and helps the reader understand them in greater depth.

What are some different types of syntheses?

A concept or explanatory synthesis divides a subject into its component parts and presents them to the reader in a clear and orderly fashion. The purpose in writing an explanatory essay is not to argue a particular point, but rather to present the facts in a reasonably objective manner, to explain how something works. The explanatory synthesis does not go much beyond what is obvious from a careful reading of the sources.

A synthesis of an event pulls in multiple perspectives to craft a full picture of what happened. Think of an earthquake, a hurricane, or the bird flu outbreak. It could be news or a retrospective.

History or chronology syntheses provide a timeline or describe the evolution of a topic. They may contain reflection and multiple views. Examples include pollution, resource declines, science policy.

The purpose of an argument synthesis is to present your own point of view – supported, of course, by relevant facts, drawn from sources, and presented in a logical manner. The thesis of an argumentative essay is debatable. Any two writers working with the same source materials could conceive of and support other, opposite arguments.

Almost any feature article in a magazine or newspaper could be considered a synthesis.

The way you relate information sources, the patterns you identify, the questions you ask and the way you answer them, all of these are personal  and unique.

So, its easy to talk about synthesis in the abstract. But how do you actually do it?

First, what is your purpose? Why are you writing this synthesis, and for whom? Remember your audience. Your purpose determines which sources you use, which parts of them you use, at which points in your essay you use them, how you relate them to one another, and how much weight or space to give them in your story.

Second, you have to start reading differently—more thoughtfully, as MIT professor Ed Boyden explains in a Technology Review blog post titled “How to Think.

Highlight key facts and ideas while reading. Cut and paste important text (keeping the source attached!) into a notes document.

By reading actively, you will start to recognize the crucial connections between ideas that form the basis for synthesizing. Since the very essence of synthesis is the combining of information and ideas, you must have reason for attempting to combine them. What are the relationships among your sources that make them worth synthesizing? Answering this question can help provide a framework for the synthesis.

Finally, you can now start to write. You have all the pieces—you just have to fit them together, trimming and moving text, adding transitions and context. Flag any gaps or questions that require more reading or research. Add that information in, trim some more, move things around.

Read it aloud. Put it away for awhile. Read it again. This is the “work” part of writing.

A few examples:

Carl Zimmer’s article in The New York Times was one of three that earned him an award from the American Association for the Advancement of Science. I counted at least six interviews and eight peer-reviewed journal articles as sources, as well as one in-progress study in the 2,000 word story.

A recent 3,500-word feature story by veteran reporter Ted Williams in Audubon used nearly 75 sources, including multiple interviews with 20 people, dozens of documents and peer-reviewed journal articles, and web sources.

In a magazine article (links to PDF) published last spring, I combined peer-reviewed literature and historical accounts with a field trip with researchers into 2,000 words about a fish.

Research institutions, government agencies, and nonprofits often produce synthesis reports. One recent report from Dartmouth College used some 86 scientific sources, nearly all of them peer-reviewed, as well as interviews and datasets for a 26-page synthesis of scientific knowledge on mercury in the marine environment.



Reading science stories

Questions to ask yourself when reading science stories:

(adapted from Carol Rogers, University of Maryland; Don Gibb, Ryerson Polytechnic; and B. Kovach and T. Rosensteil, authors of Blur)

Basics: Who where when why how what is the story about?

Lead (Lede): How does the story begin? Does the lead/lede/opening paragraph contain too much detail? Is it too vague, too routine or cliched? Is the lead buried? Is it adequately supported by the rest of the story?

Appeal: What attracted you to the story?

Audience: Who is the intended or presumed audience for the story?

News aspect: Where did the story originate (research paper, meeting, press release, etc.)? What is the news hook or angle of the story?

Explanation: Did the reporter explain complex concepts? How (through use of analogies, metaphor, etc.)? Is there too much explanation, or too little? Is the story easy to understand (including presence or absence of jargon and graphics)?

Source material: Who or what are the sources? Are sources identified (are you able to access them yourself?) Are there quotes? Are quotes too long or ineffective? Do the quotes add “voice” to the story?

Validity: What evidence is presented and how was it tested or vetted? What might be an alternative explanation or understanding?

Supplementary media: Does the story contain or link to visuals, blogs, podcasting, etc. Is the story multi-media?

Detail: Are there too many generalizations (“most” or “many”) and not enough specifics? Does the writer include observational details like sights, sounds, tastes, etc.?

Organization: How is the story structured? How do the paragraphs flow or relate? Does the story have a traditional news (inverted pyramid) structure or a more narrative format?

Context: how does this story relate to what has come before or what might come after?

Public Understanding of Science

Not only do science writers need to know something about their subject matter and how to describe it in truthful and interesting ways, but they need to know who needs to hear or read or watch the story. Writing is always a two-way process. When we are beginning as writers we tend to think one-sidedly, only about what is inside our own minds and our own words. But part of our growth as writers is to think more about the people on the other side—our readers, our audience.

Why is audience important? The usual answer is that science knowledge is important to the audience—they need to know and understand the information being communicated.

Matthew Nisbet, a professor of communication at American University, classifies dimensions of science knowledge.

1. Practical or utilitarian: It is often stated that science in everyday life is invisible, taken for granted. But science knowledge is used daily when you make decisions, like fixing your car, interpreting packaging on food, what to wear for the weather. Making such decisions might require a limited knowledge of basic scientific terms, concepts, and facts.

2. Then there is civic or democratic knowledge, sufficient to make sense of a news report, or interpret competing arguments about a policy decision. The public is often asked to make decisions about new technologies that could have far-reaching effects, both on its own wellbeing and on the rest of the world. To make these decisions, people need knowledge so that they can reason well about issues involving science.

3. Nisbet’s third type of understanding is institutional, about the politics and workings of science: who funds it, how is it regulated, etc. This level of understanding also means a capacity to distinguish science from pseudoscience—to know how science works. Maine’s Governor LePage has said he won’t remove rules that are based on science. But how will we know if a rule is “science-based” or not?

All of these theories about scientific literacy and public understanding are based on the idea of a gap between science and the people who need the knowledge that science provides. Here’s a representation of what that gap might look like (thanks to Rob Helpy-Chalk):


Scientists communicate to each other and share knowledge through presentations and publications. The public, the ultimate target audience or the users of the information, could be policy makers, town officials, citizens. The gap between these two realms is well-accepted and often mentioned in conversations about science communication. But rather than accepting the gap, take a closer look. Is it real? Where did it come from?

Bernadette Bensaude-Vincent (2002) pointed out that the gap between scientists and the public is ancient and originated in the different requirements of theoretical and practical knowledge. In ancient times, however, both kinds of knowledge were valued, and it was not expected that ordinary citizens should become like philosophers or naturalists (the predecessors of today’s scientists). For centuries, it was thought and language only that separated them. Members of the public with an interest in science were encouraged to interact with scientists. Over time, as scientists became more professional and more specialized (think quantum physics),the enlightened public of amateurs, a term that still retained a strong positive connotation in the nineteenth century, was transformed into a “mass of gullible, irrational and ignorant people” in the twentieth century… In a relatively short period of time, public knowledge became irrelevant and scientists held a monopoly on legitimate knowledge.

In industrializing nations such as the U.S., science was idealized as the preferred route to economic expansion and social emancipation. The more citizens knew about science, the more they would support this view. As Boyce Rensberger has pointed out, the work of most science reporters in those days consisted largely of translating scientific jargon and explaining the statements of scientists and medical leaders. In the 1930s and ‘40s, science journalists believed that it was their job to persuade the public to accept science as the [economic] salvation of society.

So what have we learned? Does the American public understand and “accept” science?

The National Science Foundation surveys public attitudes and understanding of science every two years, and for several decades Americans have been asked the same series of true-false questions. The number of correct answers to these questions has remained flat—the average American adult does not “know” any more “science” today than he or she did twenty years ago.


Only 51% of Americans knew that electrons are smaller than atoms. One-quarter of Americans don’t know that the Earth revolves around the sun. And 47% believe that human beings developed from earlier species of animals. Four out of five Americans do not understand the concept of a scientific study (Miller 2004).

But Americans are not necessarily smarter about other topics, and even scientists get many of these questions wrong (Stocklmayer and Bryant 2011). As many have pointed out, including Cornelia Dean and Jon Miller, most people leave science behind when they graduate high school, and the science we consider as citizens is not the facts collected in textbooks, but science that will not occur for another twenty years. The science we consider as citizens is more recent, unfolding every day.

So where do people get their information? How is the knowledge gap being so unsuccessfully filled?

According to the Pew Research Center for People and the Press, the Internet is slowly closing in on television as Americans’ main source of news. Television remains the most widely used source for national and international news  but, the percentage saying they regularly watch local TV news has dipped below 50% for the first time (48%).


Another Pew study found that the days of loyalty to a particular news organization on a particular piece of technology in a particular form are gone. The overwhelming majority of Americans (92%) use multiple platforms to get news on a typical day, including national TV, local TV, the internet, local newspapers, radio, and national newspapers. Some 46% of Americans say they get news from four to six media platforms on a typical day. Just 7% get their news from a single media platform on a typical day, mostly older, well educated, upper middle class whites (Purcell et al. 2010).

Yet more evidence has emerged that newspapers (whether accessed in print or digitally) are the primary source people turn to for news about government and civic affairs. Nearly three quarters (72%) of adults are quite attached to following local news and information, and local newspapers are by far the source they rely on for much of the local information they need (Miller et al. 2012).

Online and digital news consumption, meanwhile, continues to increase, with many more people now getting news on cell phones, tablets or other mobile platforms. And perhaps the most dramatic change in the news environment has been the rise of social networking sites. The percentage of Americans saying they saw news or news headlines on a social networking site yesterday has doubled – from 9% to 19% – since 2010. Among adults younger than age 30, as many saw news on a social networking site the previous day (33%) as saw any television news (34%), with just 13% having read a newspaper either in print or digital form (Pew Research Center 2012).

The social media trends may mean that the 44% of adults who don’t follow the news regularly may be getting information via social media and other online sources.

What about science news specifically? Sources for science news parallel the general news findings from the Pew studies, with the Internet surpassing television as the dominant source for science and technology news. When it comes to specific scientific issues, more people turn to the Internet.


The most popular online news subjects are the weather (followed by 81% of internet news users), national events (73%), health and medicine (66%), business and the economy (64%), international events (62%), and… science and technology (60%).

Slide27And people say they want more coverage of science. Asked what subjects they would like to receive more coverage, 44% said scientific news and discoveries (Horrigan 2006).

A study of the New York Times most-emailed articles in 2009 found that readers preferred e-mailing articles with a positive theme, including long articles on intellectually challenging subjects. They shared stories that inspired awe, including science stories (Tierney 2010).

So, we know that people want science-based information, that they actively seek it, and they aren’t necessarily deterred by length or complexity.

How skillfully or how often Americans engage in the search for scientific information, whether on the Internet or elsewhere, remains unknown. In a January 4, 2013 commentary in Science, Dominique Brossard and Dietram Scheufele note that among the U.S. public, time spent on the World Wide Web has been linked to more positive attitudes toward science. Online science sources may be helping to narrow knowledge gaps caused partly by science coverage in traditional media that tends to be tailored to highly educated audiences. Yet one of the challenges of the current situation is the sheer volume of information available on the Internet.  The social environment of the web influences the context for science stories. Just the tone of the comments following balanced science stories can significantly alter how audiences think about the subject matter.


Bensaude-Vincent, B. 2002. A genealogy of the increasing gap between science and the public. Public Understanding of Science 10:99–113.

Allum, N., P. Sturgis, D. Tabourazi and I. Brunton-Smith. 2008. Science knowledge and attitudes across cultures: a meta-analysis. Public Understanding of Science 17: 35.

Horrigan, J.B. 2006. The Internet as a resource for news and information about science. Pew Internet and American Life Project.

Inglehart, R. 1990. Culture Shift in Advanced Societies. Princeton: Princeton University Press.

Miller, C., K. Purcell, and T. Rosenstiel. 2012. 72% of Americans follow local news closely. Pew Research Center.

Miller, J. 2004. Public understanding of, and attitudes toward, scientific research: what we know and what we need to know. Public Understanding of Science 13:273-294. Jon D. Miller has been studying public interactions with science for more than 20 years. A recent summary of his work can be found in Science and the Media, a report from the American Academy of Arts and Sciences.

Nisbet, M. 2005. The multiple meanings of public understanding. Committee for Skeptical Inquiry.

Pew Research Center for People and the Press. 2012. Trends in News Consumption: 1991-2012.

Purcell, K., L. Rainie, A. Mitchell, T. Rosenstiel, and K. Olmstead. 2010. Understanding the participatory news consumer. Pew Research Center.

Stocklmayer, S.M., and C. Bryant. 2011. Science and the public—what should people know? International Journal of Science Education, Part B: Communication and Public Engagement 2:81-101.

Literary Science Writing: A Return to Narrative

We do have literary and narrative science writing before World War II: Rachel Carson, Joseph Mitchell, John Steinbeck, etc. After mid-century, the change from private to public science had enormous consequences, and one of those was the birth of science writing as a distinct field (Franklin).

There also was a change in literature at this same time, a proclaimed “death of fiction,” of the great novel. Some argue that nonfiction writers stepped in to fill the void: Truman Capote, Norman Mailer, Joan Didion, Tom Wolfe, Hunter S. Thompson. Meanwhile, John McPhee started writing for The New Yorker.

So, what is literary journalism, narrative, creative nonfiction, etc.? You can lump these together or tease them apart. In essence, the forms of writing are often said to “borrow the tools of fiction” to craft true stories. Others would argue that true stories are the original stories. Here are some elements that can make your science writing “literary.”

Scene-by-scene construction

Immersion: participate, listen, learn, bear witness.

Voice/Narration: voice of self, of others.

Interdisciplinary perspective: “The liveliness of literary journalism comes from combining personal engagement with perspectives from sociology and anthropology, memoir writing, fiction, history, and standard reporting. Literary journalists are boundary-crossers” (Sims).

Investigative journalism


Complicated Structure (essay, digression, threads).


Story (Narrative Arc, Mythic Journey, Hero’s Tale): Stories are collaborative–the listener paints the backdrop. Narrative isn’t merely a technique for communicating, its how we make sense of the world. The human brain has evoloved to enable the construction and comprehension of narrative (Achenbach). Story is the fundamental unit of communication. Humans tend to believe story and reject data. People compare their story to ones that are presented, and favor the story the most resembles their own. (Goodman).


Achenbach, Joel. 2009. The Vestigal Tale. Washington Post, 29 October.

Dillard, Annie. 2005. “Introduction: Notes for Young Writers” in In Fact: The Best of Creative Nonfiction. New York: W.W. Norton.

Franklin, Jon. 1986. “Humanizing Science through Literary Writing” in Scientists and Journalists: Reporting Science as News. New York: The Free Press.

Goodman, Andy.

Gutkind, Lee. 2006. “Creative Nonfiction: A Movement, not a Moment” in Creative Nonfiction Issue 29: The ABCs of CNF.

Kanigel, Robert. 2006. “The Science Essay” pp. 145-150 in A Field Guide for Science Writers, Second Edition. New York: Oxford University Press.

Kramer, Mark. 1995. “Breakable Rules for Journalists” pp. 21-34 in Literary Journalism. New York: Ballantine Books.

Shreeve, Jamie. 2006. “Narrative Writing” pp. 138-144 in A Field Guide for Science Writers, Second Edition. New York: Oxford University Press.

Sims, Norman. 1995. “The Art of Literary Journalism” pp. 3-20 in Literary Journalism. New York: Ballantine Books.

Zinsser, William. 2006. “Nonfiction as Literature” pp. 95-99 in On Writing Well, 30th Anniversary Edition. New York: Collins.

Reading Literary Science Writing

I’ve selected one or two articles for each student to read. Please come to class prepared to discuss your reading and read your favorite paragraph aloud. You can write up your response as your journal entry for the week. In your journal and your presentation, think about how the story is different from what you’ve been reading in your news outlet. Questions to consider include:

Where and when was the story published?

Who wrote the story?

Does the author have a science background?

How does the story begin?

What is the point of view? (first person “I”, “We”; second person “You”; third person)

Do you like the story? Why or why not?

Here are the articles:

Annie Dillard. “Spring” from Pilgrim at Tinker Creek, 1988.

Elizabeth Kolbert. “Stung” from The New Yorker, 2007.

Lisa Couturier. “A Banishment of Crows” from The Hopes of Snakes, 2005.

Rebecca Skloot. Excerpt from The Immortal Life of Henrietta Lacks, 2010.

Rachel Carson. “Undersea” from Atlantic Monthly, 1937.

David Gessner. “Learning to Surf” from Orion, 2006.

John McPhee. Excerpt from Basin & Range, 1982.

Hank Steuver. “What Exit? Fifty Years Later, the New Jersey Turnpike Finds a Little Respect,” 2001; David Remnick, “The New Jersey Turnpike: A Love Story,” 1984, both from The Washington Post.

Jon Franklin. “Mrs. Kelly’s Monster,” from The Evening Sun, 1978.

Jennifer Lunden. “The Butterfly Effect: Finding Sanctuary in Butterfly Town, USA,” Creative Nonfiction, 2010.

Robin Cody. “Miss Ivory Broom,” University of Portland Maagazine, 2003.

Alan Weisman. “Earth without People,” Discover, 2005.

Michael Pollan. “Dream Pond: Just add water,” in The New York Times, 1998; “Natural Narratives,” Nieman Narrative Digest, 2007.

Barry Lopez. “A Presentation of Whales,” Crossing Open Ground, 1985.

Data alert! A bit about graphs, maps, images, risk, statistics and uncertainty

Graphics and visuals like maps, charts, and timelines make information easy to understand and process. What might take paragraphs can be summarized in one image. Now, online graphics can be interactive, allowing readers an opportunity to explore data. Graphics can also be misleading.

Is the time scale appropriate for the trend being presented? Does the graph show all of the data, or a narrow window to convey a skewed picture? There is always more evidence than what is presented or published, but the key issue is whether evidence selection has compromised the true account of the underlying data (Tufte 07).

In maps, “large scale” means zoomed-in, detailed. “Small scale” means zoomed out, general. Is the type of map appropriate for the data being presented?

Look at the categories and the legend. Maps can be manipulated to show what you want.

Photos are easily mismatched to the text and the headline.

Risk is a possibility that something might happen or bring about some result.

High probability = high predictability (event more likely).

Low probability = low predictability (event less likely).

People (and sometimes the media) tend to overestimate the danger of rare events yet underestimate dangers of more common events. People tend to misjudge the relative risks from food safety issues, for example ranking pesticide residues as posing a much greater threat to human health than harmful microorganisms or an unhealthy lifestyle (lack of exercise, poor diet). Yet the statistics show that people are far more likely to die from lifestyle-related diseases such as coronary heart disease and cancers.

In fact, the top causes of death in US, according to the Centers for Disease Control and Prevention, are 1. heart disease, 2. cancer, 3. stroke.

Perceptions and knowledge of risk depend on whether the risk is individual, community, or societal. People tend to overestimate the role of forces inside the individual, such as personality, ability, disposition, and motivation, as causes for human behavior and to underestimate the role of environmental or situational factors, such as the varied opportunites and obstacles that exist for people in different social classes. When applied to whole groups, these attribution errors become the basis for sterotypes.

People tend to assume that if they can control a situation they are safer. We fear dying on airplane more than in a car crash, yet the number of traffic accident fatalities is much higher. Perhaps this is why we fear man-made disasters (radiation) more than natural disasters (tsunami). Trace amounts of radioactive iodine are being detected in rain over the US (CA and VT), but each news story is quick to point out how the levels are low and not a risk, but few offer any comparison to everyday risk.

People are more worried by dramatic but infrequent events than by “boring” risks like slipping on a wet floor. And alarmist, dramatic media coverage contributes to false risk perception. Take, for example, the shark attack. Fueled by Jaws and now Shark Week, our fears of sharks are conditioned. Bees, wasps and snakes are responsible for far more fatalities each year. In the United States the annual risk of death from lightning is 30 times greater than that from shark attack. For most people, any shark-human interaction is likely to occur while swimming or surfing in nearshore waters. From a statistical standpoint the chances of dying in this area are markedly higher from many other causes (such as drowning and cardiac arrest) than from shark attack. Many more people are injured and killed on land while driving to and from the beach than by sharks in the water. Shark attack trauma is also less common than such beach-related injuries as spinal damage, dehydration, jellyfish and stingray stings and sunburn. Indeed, many more sutures are expended on sea shell lacerations of the feet than on shark bites! (International Shark Attack File)

Second example: Avian flu caused 200 deaths in 5 years, with an unlikely possible mutation (from guts of birds to lungs of humans) resulting in a horrendous pandemic, hence alarmist media coverage. But as many as 40,000 people die each year from common seasonal flu. (Wulf 2010).

Risk is the result of events, conditions and situations, called “risk factors.” Where a risk factor has been consistently linked to an event or situation, the factor is said to “cause” death or illness: HIV causes AIDS, asbestos causes mesothelioma, cigarette smoking causes lung cancer.

With these well-proven exceptions, it is difficult to show that any one thing “causes” cancer because cancer doesn’t appear immediately after exposure, providing time for other factors to come into play. Without a direct cause and effect relationship, there are only associations, strong relationships, between a result/disease and an agent/situation, or risk factor. A risk factor is not a guarantee, not a cause, just an association, like high cholesterol and heart disease.

An association does not, by itself, indicate causation. Additional evidence is needed: the event must come before the result, and that other explanations are considered and ruled out. As humans, we seem wired to look for patterns, to want to explain things, hence our tendency to assume causation. But remember: Correlation does not equal causation.

Statistics attempt to quantify risk. But statistics are frequently misused and abused. All research involves choosing what to study and how to study it. Statistics, when applied to data, measure the strength of relationships. The greater the significance, the stronger the relationship, or the less chance that some other factor is important in explaining the relationship.

Where we have considerable knowledge of outcomes, we have an objective probability for a given outcome. In a coin toss, we do not know which face will turn up when it is tossed, but we have objective probabilities of what it will likely be. In complex systems, with many interconnected parts, scientists are often uncertain about the extent and magnitude of the connections. As a result, they have to make judgements about their strength, which is a subjective probability (Stephen Schneider, in Friedman et al.).

The most believable results will have certain characteristics: (Cohn)

Replication: They have been successfully repeated

Reevaluation: They have been tested by more than one method (mathematical technique)

Common attacks on statistics create the impression of numerous errors. Something is wrong with every sample, and pointing this out can begin the unraveling of any argument: the data are outdated, unrepresentative, missing, outliers. The r-squared value x only explains 100-x of the data. The scientist chose the wrong model (linear, non-linear, random, etc.). When additional variables are included, the results become insignificant. Other factors can result in the same effect. Any inconsistency or complication in the data are deliberately obscured or omitted–cast the perception of doubt. (Murray)

With our minds and our worlds filled with uncertainties and our days filled with only 24 hours, we often fall back on judgemental shortcuts, called heuristics, to make sense of things. People reconcile what they see and hear with what they already know from personal experience, friends and family, religious beliefs, political orientation, values, etc.

If someone tells us that things are uncertain, we think that means that the science is muddled. Uncertainty is everywhere, and leads to errors in interpretation. All too often, health benefit and risk statements are presented as if they were authoritative, definitive, and based on clear and compelling evidence. The result? An Illusion of Certainty.

Scientists do not just reduce uncertainty, they actively construct it. They look for problems in their own work by asking questions and probing for gaps and alternative explanations. Uncertainty is different than indeterminacy (when all the parameters of a system and their interactions are not known) and ignorance (when it is not known what is not known). Uncertainty means that the parameters are sufficiently known to make a qualitative judgement or attempt a conclusion; there is no such thing as absolute proof. Doubt (or curiousity or skepticism) is crucial to science (to a scientist, claiming or acknowledging uncertainty maintains an appearance of objectivity) but it also makes science vulnerable to misrepresentation. Uncertainty can appear as controversy, because it is easy to take uncertainties out of context and create the impression that everything is unresolved and thus plant seeds of doubt in the reader’s mind (Oreskes and Conway).

Another contributor to the illusion, as we’ve seen, is the habit of the news media to report research as “news,” presenting research findings out of historical and scientific context as new, very preliminary, and potentially groundbreaking. Reports can celebrate the finding, and downplay uncertainty. The accounts of each new project makes it appear to readers that scientists are much more uncertain than they actually are. Today’s news is easily contradicted by tomorrow’s reports. Other reports may emphasize early differences of opinion among scientists, highlighting uncertainty. Science is portrayed as a triumphant quest for certainty: the answer to a question, the solution to a puzzle, keys to unlock the door to knowledge, clues to a mystery. Often, the public is offered a view of the future in which scientific certaintly returns: “Researchers hope to be able to predict the behavior of hurricanes more precisely”; “By improving their understanding of X, researchers will solve problem Y.” (Zehr and Stocking, both in Friedman et al.)

Watch out for these phrases, or at least think about it before you use them. This is the challenge: how to communicate the ‘so what’ without claiming future certainty?

– Think about the outlet and the audience, and select your topic carefully. If the so what is a stretch, maybe don’t write the story.

– Interview others. A caution: the presence of multiple voices in a media story about emergent science allows the reader to glimpse the degree of consensus, yet it may be difficult for readers to evaluate. Are the uncertainties so great that reasonable people cannot come to a resolution? Is the finding so novel that other scientists simply have no useful expertise? With the Internet, readers can assemble meaning themselves by cobbling together stories about the same topic from a variety of places and times. If you cannot tell who is telling the truth or where the consensus lies, then the best you can do is accurately capture the message and attribute it. Or, you can present an array of viewpoints and let the reader decide (or feel overwhelmed) “This focus on the journalist as a passive transmitter allows us to make accuracy the most important characteristic of a story and often to bypass issues of validity all together…the objectivity norm urges journalists to leave their own analytical skills at home and to concentrate, instead, on conveying what they see and hear…if journalists are normatively limited to reporting rather than interpreting, then audiences are left to sift through the dueling representations of uncertainty themselves” (Friedman et al.).

– Explain changes in certainty or consensus. This requires historical context and knowledge of particular fields, and may be harder for a science generalist than for someone who specializes in certain subjects.

– Look at why people may be promoting or challenging uncertainty. We will look at this issue in more detail in a few weeks. If you say, ‘There is no evidence’, do you mean, ‘There are no studies done on X’, or, ‘There are lots of studies out there, and they show no risk of X causing Y’?

– Watch the use of anecdotes and false “trendsetting”. Anecdotes can be fine examples, but they are usually poor evidence. To a social scientist, what seems like a great interview with printable quotes is a convenience survey of an unrepresentative sample. Vivid anecdotes can interfere with a person’s judgement of risks (Griffin, in Friedman et al.) Make sure your examples are representative.


Best, Joel. 2001. Damned Lies and Statistics. Berkeley: University of California Press.

Best, Joel. 2004. More Damned Lies and Statistics. Berkeley: University of California Press.

Best, Joel. 2005. Lies, calculations and constructions: beyond How to Lie with Statistics. Statistical Science 20 (3):210-214.

Cohn, V. 1989. News and Numbers. Ames, IA: Iowa University Press.

Cope, Lewis. 2006. Understanding and using statistics, pp. 18-25 in A Field Guide for Science Writers, 2nd edition.

Drum, Kevin. 2010. Statistical Zombies.

Friedman, S.F., S. Dunwoody, and C.L. Rogers. 1999. Communicating Uncertainty. Mahwah, NJ:Lawrence Erlbaum Associates.

Gould, Stephen Jay. The Median Isn’t the Message.

Huff, Darrell. 1954. How to Lie with Statistics. New York: W.W. Norton

Monmonier, Mark. 1996. How to Lie with Maps (2nd Ed.) Chicago: The University of Chicago Press.

Monmonier, Mark. 2005. Lying with maps. Statistical Science 20(3):215-222.

Murray, C. 2005. How to accuse the other guy of lying with statistics. Statistical Science 20(3): 239-241.

Niles, Robert.

Oreskes, Naomi, and Erik M. Conway. 2010. Merchants of Doubt. New York: Bloomsbury Press.

Rifkin, Erik, and Edward Bouwer. 2007. The Illusion of Certainty. New York: Springer.

Tufte, Edward R. 1983. The Visual Display of Quantitative Information. Cheshire, CT: Graphics Press.

Tufte, Edward R. 2006. Beautiful Evidence. Cheshire, CT: Graphics Press.

Tufte, Edward R. 1997. Visual Explanations. Cheshire, CT: Graphics Press.


Science Stereotypes

“Scientific discoveries are made by people; they don’t just happen,” wrote Ruth Levy Guyer in our textbook. Who are these people?

madscientistThe scientist is often portrayed as an isolated man in a laboratory driven by insanity, greed, or selfishness. He’s the awkward nerd spewing physics jargon in the face of a pretty girl. He’s the scatterbrained teacher who accidentally creates a monster or invents a miracle. She’s the hysterical woman whose message will not be heard. How can writers make scientist characters more accurate, diverse, interesting, and effective? Why should writers care about the truthfulness of their real or imagined scientists? One of ways to improve the coverage of science in the news media is to focus stories on how science works. And one of the best ways to illustrate how science works is to show scientists doing science. This approach also appeals to the human desire for narrative structure, for a protagonist, for action in stories.

The Harris Poll consistently finds scientists near the top of the “most prestigious occupations,” after firefighters and above doctors, nurses, teachers, and military officers. The percentage of Americans who say scientists are ‘odd and peculiar’ has dropped, although one-quarter still agree (Losh 2010).

So why do scientists have such a bad image?

According to Chris Mooney and Sheril Kirshenbaum in their book Unscientific America, there’s something about scientists that triggers a particular kind of stereotyping, and that this reflects our society’s uneasiness with the power they can sometimes wield.

As Stephen Shapin recently noted, “the modern American scientist is held in some esteem, valued as a useful sort of person, but there is little understanding of what it might be to engage in scientific inquiry for its own sake and little evident approval of such a thing.”

alchemistRoslynn Haynes, a professor of English in Australia, identified seven primary stereotypes of scientists. According to Haynes, “the master narrative of the scientist is of an evil maniac and a dangerous man.” These stereotypes provide a useful framework for thinking about humanizing science writing.

The evil alchemist. Alchemy began with metalworkers in Egypt. When it was translated from Arabic writings to medieval Europe, it was associated with heresy and the black arts: the sinister magician, the devil’s worker, illegal, proud, arrogant, secretive, power-hungry. Dr. Faustus and Victor Frankenstein continue to provide metaphors for modern, cutting-edge research. The alchemist appears in plots that depend on the supernatural and the paranormal—stories in which the credulous believer is always right and the scientist-skeptic is always wrong.

spockThe noble scientist. The first literary work to depict scientists in a positive light was Sir Francis Bacon’s utopian vision, New Atlantis (1627), which depicted the scientist as an altruistic idealist. Star Trek’s rational Mr. Spock brings order that often saves the Enterprise. Dennis Quaid’s character in The Day After Tomorrow advocates on behalf of all the ignorant people, risks his own life to save those caught in an unprecedented, climate change-driven storm. Bad Science author Ben Goldacre is critical of such portraits of scientists: “The media work around their inability to deliver scientific evidence by using authority figures, the very antithesis of what science is about, as if they were priests, or politicians, or parent figures. ‘Scientists today said…scientists revealed…scientists warned.’ And if they want balance, you’ll get two scientists disagreeing, although with no explanation of why (scientists are ‘divided’). One scientist will ‘reveal’ something, and then another will ‘challenge’ it. A bit like Jedi knights. The danger of authority figure coverage, in the absence of real evidence, is that it leaves the field wide open for questionable authority figures to waltz in.”

profThe foolish scientist (a.k.a. absent minded professor). Satires depict scientists as foolish, cultish, comic. The laughable, lovable absent minded professor clumsily creates flubber and wins hearts with his eccentricity. “Scientists are unusually insightful and intuitive people who have a strong need for organization and application of concepts. They might have difficulty expressing their ideas, because they don’t think linearly, and their life of the mind could lead others to regard them as aloof” wrote Steve Bunk. Gary Larsen loved the foolish scientist. I like to think his mocking comes from a place of affection and respect.

The inhuman researcher. As science and society evolved, so did science stereotypes. Dr. Frankenstein fits here, too, as do the atomic scientists working on the bomb during World War II. Then the Cold War gave added weight to this stereotype with their documented declarations of unconcern about the human cost of their inventions. What does it mean when we apply this metaphor to others, as in the “Frankenstein economy” of the 2008 financial meltdown on Wall Street?

The scientist as adventurer (e.g., Indiana Jones), as brave, optimistic explorer, traveler of space and time. Jon Palfreman (Nieman Reports 2002) sees television science documentaries as being drawn from a small handful of approved genres, one of which is “archaeology and legends: expeditions, lost treasures, mummies, dinosaur bones, mammoths, the use of forensic methods to uncover the past.” The other genres he identified are “forces of nature,” “modern history,” and “boys and their toys.”

farmersThe mad, bad, dangerous scientist. As science increased in power, so did the stereotypes, evolving from the alchemist tradition to the cataclysmic. In Unscientific America, Chris Mooney and Sheril Kirshenbaum wrote, “The uncaring scientist, unconcerned about consequences, pursuing knowledge at all costs—this is the ugliest scientist stereotype, and also the most deeply rooted. It hails from a long literary tradition, before Frankenstein to Greek stories that depict the search for knowledge as forbidden and dangerous, and leading to disastrous consequences. In this narrative, knowledge leads the scientist to play God, interfere with nature, and attempt to thwart fate by determining who lives and who dies.” The mad scientist can be easily exposed as a wannabe, a fraud, a rogue.

The helpless scientist. The seventh and last stereotype identified by Haynes is the scientist who becomes a victim of his or her own discovery. This is science out of control.

How pervasive are these stereotypes?

bigbangIn the Big Bang Theory, scientists are unattractive, socially inept, indifferent, uncool, but smart. Scientists are distant, long-winded, incomprehensible. The lead scientist in Bones, while female and attractive, adheres to the stereotypes of rationality (to a fault), atheism, and a cold lack of emotion. In fact, the cracks that emerge in this façade are a major plot that runs through the series.

Movies and television portray scientists that are absorbed in the details of their work, ‘wedded to the job.’ A a recent analysis of scientist portrayals on TV (Dudo 2010) found that of 2,868 characters, one percent were portrayed as scientists, mostly white males. Scientists are more likely to be characterized as good, although science as an activity is portrayed as dangerous and violent.

As David Kirby described in the 2011 book Lab Coats in Hollywood, filmmakers are not unaware of these stereotypes.  Science consultants are frequently brought in to comment on scientific matters involving the script, the actors, the sets, the props, and any other relevant factor during production. Concerns about science in the movies has led several scientific advocacy organizations to develop programs to facilitate more scientific involvement in the production of television programs and films, including the National Academy of Science’s Science & Entertainment Exchange, the Creative Science Studio, and the Sloan Foundation’s Film Development program.

Why should any of this matter? As Kirby wrote, “Popular films impact scientific culture by effecting public controversies, enhancing funding opportunities, promoting research agendas, and stimulating the public into political action…Moreover, entertainment texts can influence scientific thought by foregrounding specific scientific ideas and providing narrative reasons to accept them as representing them as reality.”

As science writers, we have an obligation to write truthfully about science, and to portray scientists not as stereotypes, but as real people, just like you and me.


Steve Bunk, “The Natural History of Science Personalities” in Science Writers Spring 2003.

Anthony Dudo, 2010. Science on television in the 21st century: recent trends in portrayals and their contributions to public attitudes toward science. Comm. Res.

Ben Goldacre,

Roslynn Haynes, From alchemy to artifical intelligence: stereotypes of the scientist in Western literature. Public Understanding of Science 12 (2003):243-253.

Kirby, David A., 2011. Lab Coats in Hollywood. Cambridge, MA: MIT Press.

Losh, Susan Carol. 2010. Stereotypes about scientists over time among US adults: 1983 and 2001. PuoS 19:372-382.

Chris Mooney and Sheril Kirshenbaum, Unscientific America (see Chapter 7).

Jon Palfreman, “Bringing science to a television audience” Nieman Reports 2002.