The “how” and “why” of information use by individuals, groups of individuals, and various professional groups is a subject that continues to intrigue and challenge academics in various fields and disciplines as well as practitioners (Vakkari et al. 1997). The importance of this problem is clear: to support the information needs of these audiences, we must first understand the audiences. For almost a century, academics and practitioners, often with very different motives and goals, have attempted to define terminology, develop models, advance theories, and undertake studies designed to understand information use and the factors that precipitate information use. Ultimately, the goal is to develop operationalized theories that can predict information use and help us communicate that information more successfully.
Sputnik, the “Cold War”, and the influx of money by the Federal government into science and technology that began in the 1960s directed the focus of academics and practitioners towards information use and the “information-seeking behavior” of engineers and scientists. This article focuses on the role of technical uncertainty in information use and on information-seeking behavior as a function of increasing and decreasing levels of technical uncertainty. The results of research conducted by me and my colleagues provide a means of understanding information use by engineers and scientists.
Definitions of information abound, and several do not include uncertainty. Although uncertainty may not be satisfactory as a basis for defining information, it is nevertheless important for studying and understanding information use and information-seeking behavior. One can make a strong argument for considering uncertainty as the beginning stage of seeking information, but on the other hand, it is difficult to examine uncertainty without considering what is already known or certain (Case 2002).
In this article, I make the case for using an understanding of the level of technical uncertainty to investigate the search for and use of information; present the results from a study that investigated technical uncertainty as a factor that shapes information use by aerospace engineers and scientists; and propose the implications of these results for communicators and for further research.
Uncertainty as a variable
The study of uncertainty as a central concept in organizational research reached its peak in the early 1980s. One assumption put forth by several experts is that organizations are open systems that collect, process, and use information to reduce work-related uncertainty (Katz and Kahn 1966, Thompson 1967, Weick 1969). Other experts have proposed that organizations must deal with sources of internal (i.e., organizational or work-related) and external (i.e., environmentally based) uncertainty (see, for example, Zaltman et al. 1973). Still others have defined uncertainty as the difference between information that is possessed and missing information that is necessary to complete a task. Adding to the work of others, Tushman and Nadler (1978) proposed three sources of work-related uncertainty: the characteristics of sub-tasks, the environment in which these sub-tasks are completed, and the interdependence between tasks and between sub-tasks. Stating that organizations must develop information-processing mechanisms capable of dealing with external and internal sources of uncertainty (Tushman and Nadler 1980), they proposed an information-processing model as a means of designing and structuring organizations that are involved in technological innovation and research and development (R&D). Some experts have reported that the greater the degree of uncertainty, the greater the information-processing requirements and the greater the use of information external to the organization (see, for example, Gifford et al. 1979). Others have reported that as an information processor, the engineer or scientist involved in technological innovation and R&D uses information to moderate (i.e., reduce) uncertainty.
Uncertainty has also been used to explain the nature of the relationship between organizations and their environments. Several theories treat perceived environmental uncertainty as something that intervenes between environmental characteristics (i.e., the amount and specificity of information) and information search. Another opinion is that uncertainty involves a perceived inability to control or accurately predict outcomes of the interaction between an organization and its environment, which implies a lack of information about future events—in other words, the belief that alternatives and their outcomes are unpredictable. Duncan (1972) identified five sources of uncertainty related to the external environment: customers, suppliers, competitors, the sociopolitical climate, and the technological milieu.
In 1985, two researchers concluded that individuals who saw the external environment as uncertain sought greater contact with sources of information outside their organizations than did individuals who did not see the external environment as uncertain (Brown and Utterback 1985). In other words, the higher the degree of perceived uncertainty, the more likely an organization is to collect, process, and use external information. According to many theorists (e.g., Kuhlthau 1993), organizations use a variety of techniques to maintain contact with the external environment and to acquire information that is external to the organization.
Technical uncertainty in context
Organizations involved in technological innovation and R&D can be considered open systems that must deal with complexity and sources of work-related uncertainty. Many organizations, especially those involved in technical innovation and R&D, use both internally and externally derived information to limit uncertainty. In the aerospace industry, particularly in the large commercial-aircraft sector, there is a high degree of systemic complexity embodied in the research, design, and production (RD&P) of products. This sector must contend with both technical uncertainty, which is largely internally centered, and marketplace uncertainty, which is largely external to the organization.
To survive and grow, organizations must successfully manage uncertainty. According to one expert, information sources that are primarily outside the organization can moderate and reduce this uncertainty (Miller 1971). However, because organizations need stability and control to protect their intellectual property and competitive advantage, there is a tendency for organizations involved in technological innovation and R&D to isolate themselves from their external environment. Many believe that these organizations frequently erect barriers that prohibit or limit access to information that resides in the external environment, especially if the organizations are involved in work that is classified or proprietary. Many believe there is a real danger that organizations involved in technological innovation and R&D can become isolated from their external environment and from information that is external to the organization.
Research design, methodology, and analysis
To address these issues, I conducted research with several colleagues (Pinelli et al. 1993) to examine the relationship between technical (task) uncertainty and information use by industry-affiliated U.S. aerospace engineers and scientists. (Although this research focused on a specific industrial sector, I believe that is has clear relevance to scientific communicators in general, as I’ll demonstrate later in this article.) Because the study investigated the relationship between task uncertainty and information use, we used a perceptual measure of uncertainty. (Study participants reported their perceived level of uncertainty using a five-point scale, with 5 being “very uncertain”.) Our study addressed three research questions. The first asked whether U.S. aerospace engineers and scientists could make accurate assessments of the uncertainty they face in a project. The second question looked at the relationship between perceived uncertainty and personal or task characteristics. The third question flowed from the first two, and addressed the effect of uncertainty on information-seeking behavior in general and on the use of federally funded aerospace R&D in particular.
We used a random sample of 750 members of the American Institute of Aeronautics and Astronautics (AIAA) as the study population. Participants returned 341 usable surveys by the established cut-off date. Of the 341 respondents, about 91% held engineering degrees, and 34% were doing engineering work. About 52% percent had master’s degrees, about 17% held a doctorate, and about 1% had post-graduate education. Their average work experience in aerospace was 21.9 years. We asked survey participants to focus on the most important project, task, or problem they had worked on in the past 6 months. We asked them the following question: “On a scale of 1 (little uncertainty) to 5 (great uncertainty), how would you rate the overall technical uncertainty of this project, task, or problem?” We defined two groups: one with low uncertainty and one with high uncertainty.
Although both groups used the same information sources (e.g., personal stores of technical information and co-workers within the organization), those working on projects with greater technical uncertainty made greater use of the formal literature (e.g., conference papers and journals), electronic databases, colleagues outside the organization, and librarians and technical information specialists.
- We found no significant differences between technical uncertainty and the number of hours that survey respondents spent sending or receiving technical information orally or between technical uncertainty and the number of hours respondents spent sending or receiving written technical communications. However, respondents who reported working on projects with the greatest amount of technical uncertainty spent significantly more time communicating orally than did respondents who reported working on projects with less technical uncertainty. (The same finding held for the number of hours that respondents spent receiving technical communications orally.)
- Users of formal information products (e.g., journal articles, NASA technical reports, and conference papers) reported significantly higher levels of technical uncertainty.
- About 73% of the respondents reported using the results of federally funded aerospace R&D in their work. Some 60% of the survey respondents reported using the results of federally funded aerospace R&D in completing their projects. About 64% of the respondents who used this information to complete their projects found it in published Department of Defense (DoD) or National Aeronautics and Space Administration (NASA) technical reports.
- To examine the relationship between technical uncertainty and nine factors thought to influence the use of DoD and NASA technical reports, we compared the low- and high-uncertainty groups:
- DoD technical reports: Respondents in the high-uncertainty group reported higher influence for four of the nine factors—easy to physically obtain, technical quality, good prior experience using these reports, and important to their work. However, “important to their work” was the most significant factor.
- NASA technical reports: Respondents in the high-uncertainty group reported higher influence scores for two of the nine factors—easy to use or read, and important to their work. Again, “important to their work” was most significant.
Summary and implications
The study data suggests that technical uncertainty does indeed affect the information-seeking behavior of engineers and scientists, as has been shown in the previous research literature. On the other hand, the results also suggest that the quality, reliability, readability, and relevance of the information also influence its use. What remains unknown and should thus prove fertile ground for academics and practitioners alike is how has the Internet has influenced the information-seeking behavior of engineers and scientists.
From the standpoint of scientific communication, we must determine how the significant factors detected in the study should guide our work: information must be physically easy to obtain, of high technical quality, of consistent technical quality, easy to read and understand, and of clear and direct relevance (importance) to the work of our audiences. Each of these is a familiar concept to skilled communicators, but the study results also provide considerable justification for our role in the creation, revision, and distribution of the information that we produce. What may be less familiar is the importance of oral communication, and its significance in the present study suggests that by relying exclusively on traditional methods (e.g., printed publications), we may be missing an opportunity to improve the success of our communication efforts. Particularly given the ability of the Internet to facilitate or simulate oral communication (e.g., Internet-based telephony vs. chat rooms and e-mail, respectively), there are clear opportunities for us to explore.
Brown, J.W.; Utterback, J.M. 1985. Uncertainty and technical communications. Management Science 31:301–311.
Case, D.O. 2002. Looking for information: a survey of research on information seeking, needs, and behavior. Academic Press, New York, NY.
Duncan, R.B. 1972. Characteristics of organizational environments and perceived environmental uncertainty. Administrative Science Quarterly 17:313–327.
Gifford, W.; Bobbitt, H.; Slocum, S. 1979. Message characteristics and perceptions on uncertainty by organizational decision makers. Academy of Management Review 22(1):458–481.
Katz, D.; Kahn, R.L. 1966. The social psychology of organizations. John Wiley, New York, NY.
Kuhlthau, C.C. 1993. Seeking meaning: a process approach to library and information services. Ablex Press, Greenwich, CT.
Miller, R.E. 1971. Innovation, organization, and environment: a study of sixteen American and western European steel firms. University of Sherbrooke Press, Sherbrooke, PQ.
Pinelli, T.E.; Glassman, N.A.; Affedler, L.O.; Hecht, L.M.; Kennedy, J.M.; Barclay, R.O. 1993. Technical uncertainty and project complexity as correlates of information use by U.S. industry-affiliated aerospace engineers and scientists. National Aeronautics and Space Administration, Washington, DC. NTIS: N94-17291.
Thompson, J.D. 1967. Organizations in action. McGraw-Hill, New York, NY.
Tushman, M.L.; Nadler, D.A. 1978. Information processing as an integrating concept in organizational design. Academy of Management Review 3(1):613–624.
Tushman, M.L.; Nadler, D.A. 1980. Communication and technical roles in R&D laboratories: an information processing model. p. 91–112 in: Dean, B.V. and Goldhar, J.L. (eds.) Management of Research and Innovation. North-Holland Publishing, Amsterdam.
Vakkari, P.; Savolainen, R.; Dervin, B. (Eds.) 1997. Information seeking in context. Taylor Graham, London.
Weick, K.E. 1969. The social psychology of organizing. Addison-Wesley, Reading, MA.
Zaltman, G.; Duncan, R.; Holbek, J. 1973. Innovations and organizations. John Wiley, New York, NY.
Dr. Thomas E. Pinelli is the Distance Learning Officer, Office of Education, NASA Langley Research Center (Hampton, Virginia) and manages the NASA Langley Center for Distance Learning (dlcenter.larc.nasa.gov). He is responsible for incorporating a variety of instructional technologies in the planning and implementation of distance-learning programs designed to serve educators, students, faculty, and adult learners. He is also the Emmy®-award-winning executive producer of six distance learning programs: NASA’s Kids Science News Network™, Noticiencias NASA™, the NASA SCIence Files™, NASA CONNECT™, NASA LIVE™, and NASA’s Destination Tomorrow™. Prior to assuming his current position, Dr. Pinelli was the co-principal director of the NASA/DoD Aerospace Knowledge Diffusion Research Project, a 10-year project devoted to understanding the diffusion of aerospace knowledge at the individual, organizational, national, and international levels.
Editorial: Binary thinking
by Geoff Hart (email@example.com)
It’s often said that there are two types of people in the world: those who divide everything into two categories, and those who don’t. Scientists and those of us who write about them and their endeavors tend, by our nature, to fall into the former group. Perhaps we adopt this pattern as a result of our ongoing experience with science and technology. After all, the more we study science, the more we see how the universe comes in convenient pairs:
- Magnetism arises from only two poles—north and south—and its fraternal twin, electricity, comes in only two polarities: positive and negative. Indeed, the twins together unite to produce electromagnetism.
- The substance of the universe itself is divided neatly in twain: into matter and antimatter or into matter and energy, depending on which particular dichotomy you’re thinking of. (In fact, there are two such dichotomies!)
- Ironically enough, even the computer I’m using to write this essay is binary, with my thoughts translated into strings of 0’s and 1’s that magically become words again whenever I need them to be. (Yet another dual nature: computer-readable binary or human-readable text, depending on my perspective.)
- Those elusive quarks come in pairs (also called “flavors”): up and down, top and bottom, and the more poetically named strange and charm. Of course, there are three pairs of quarks, a divergence from the rule of two that hints at something very interesting indeed that we’ll get to presently.
Even the basis for scientific “proof”, the statistical test of probability, divides neatly in two: something is either true or false—never true “to some extent”—depending on which side of an arbitrary statistical divide it falls on, and many scientists frown on reporting results as “nearly significant” or “somewhat significant”. Yet when it comes to the really interesting issues of science, this binary thinking can distract us from important discoveries, and can be profoundly misleading. A few examples:
Scientists and philosophers have debated the definition of life for as long as there have been scientists and philosophers, and for long before either profession bore a formal name. Every definition that has been proposed thus far has been deeply flawed. For example:
- If life is that which binds energy in such a way as to create order from seeming chaos, what then do we call crystals that grow in a chemical solution?
- If life is characterized by the ability to reproduce near-exact copies of itself, with errors or differences introduced by mutations, what then do we call some of the more advanced, self-modifying computer viruses? (Search the Web for “Dark Avenger Mutation Engine” if this concept interests you.)
- If life is characterized by the ability to respond to its environment, what then do we call biological viruses?
If you accept the notion that life evolved out of non-living matter, then we clearly have a problem: At the far extreme, we have a solution of miscellaneous organic chemicals that nobody would characterize as living; at the other extreme we have ourselves, clearly alive—at least by our own definition. Somewhere in between these two extremes, self-organizing, energy-binding, environmentally responsive matter crossed an invisible threshold and became alive. The lack of success in finding a universally accepted definition of life suggests to me that the location of that threshold depends more on personal preference than on any objective criteria.
The solution to the problem may thus be so simple that it has been overlooked by those struggling to define a binary, alive/dead threshold: Our attempts to clearly distinguish between the living and the non-living misses the real point. Wouldn’t it be more useful to ask the question of how alive something is rather than dividing a non-binary world neatly in two? Using “alive” and “not alive” as two extremes of a broad spectrum is a much more interesting exercise.
An even more vexing problem arises when we attempt to define sentience. Clearly, as the ones who are doing the defining, we consider ourselves sentient. As a result of this chauvinism, science has a long tradition of defining sentience based on purely human characteristics, so that the world falls into two groups: sentient humans and everything else. Even the mind–body problem (whether consciousness exists independent of the body or is indissociably tied to it) becomes a binary issue. Historically, the definition of sentience has become progressively more anthropocentric and ever-narrower as our steadily improving knowledge of animal behavior has eroded many of the distinctions between us and our fellow animals.
But this approach flies in the face of what each of us knows from firsthand observation. Any pet owner knows that their dog, cat, or parrot—and even less clearly sentient creatures such as reptiles and amphibians—shows all the hallmarks of intelligence. Our pets have personalities, moods, and fears, just as we do. Arguing whether they have these aspects to the same degree that we do is a mug’s game. Again, the issue comes down to a matter of degree, not an absolute either/or distinction. Defining extremes of sentient and not is more interesting for narrowing the scope of the discussion than for assessing the sentience of any organism.
Nomenclature: a case study of binary thinking
Carolus Linnaeus, the Swedish taxonomist, carried the urge to classify things to its logical maximum. Not satisfied with groups of two, he created the whole Kingdom/Phylum/Class/Order/Family/Genus/Species system so well known to biologists. This system, which is based largely on anatomical similarities (including reproductive structures) works wonderfully for grouping living organisms. Since “form follows function”, organisms with similar forms typically resemble each other in their ecological niches too. Unfortunately, Linnaeus completed his work before genetics became a science, and thus created a system that worked far less well in terms of meeting the needs of evolutionary biologists. The problem with Linnaean taxonomy is that through the process of adaptation to their environment, organisms that are wholly unrelated in an evolutionary sense can end up with very similar characteristics; think of flying insects and birds, for example. (Please note that this particular example should not be extended to a criticism of Linnaean taxonomy, which does distinguish clearly between these and other winged organisms.)
To solve the problem encountered by the evolutionary biologists, Kevin de Queiroz and J. Gauthier proposed the Phylocode system of taxonomy, which groups organisms based on their evolutionary history. (For a discussion of the problem they set out to solve, see <http://www.ohiou.edu/phylocode/>.)
To geneticists and evolutionary biologists, Phylocode is a wonderful tool for organizing organisms in a useful way. For classical taxonomists and most field biologists, of course, it’s a direct challenge to the system that has served them well for centuries. Predictably, scientific binary thinkers being true to their nature, the debate over the relative merits of Linnaeus and Phylocode has been conducted with all the formal dignity and grace of a barroom brawl. Of course, someone like me, a pragmatist with no emotional stake in either system, feels obliged to ask the obvious question: why not do both? The Linnaean system has continued to meet the needs of field biologists for more than a century, and there’s little reason to discard a system that remains so broadly useful. At the same time, the Phylocode system meets a serious need of evolutionary biology, and to support this work, it should be welcomed with open arms—or at least tested to see whether it’s really as good as its proponents claim. And in the meantime, scientists who straddle both fields of research could simply add the Phylocode nomenclature to their existing Linnaean taxonomies, thereby doubling the utility of their classifications, while achieving the equally salutary effect of annoying both groups of binary partisans.
A non-binary challenge
There are two problems with binary thinking. The first is simple reality: far more of the universe is made up of things that are continuous in nature, with no neat distinctions and many intermediate values between any two points, than is made up things with discrete, binary characteristics. Magnetic and electrical fields may be inherently bipolar, but both types of field have an infinite range of magnitudes. The portion of the electromagnetic spectrum we can see (visible light) is a great example of how this works: the classic cartoon rainbow has only seven colors (red, orange, yellow, green, blue, indigo, and violet, with neat binary divisions between any two adjacent colors), but a closer look reveals an infinite range of intermediate shades. Truth itself is very similar, with most of life’s “truths” coming in a bewildering shades of grey rather than simple black and white. The second and more serious problem is that, as the debates over sentience and taxonomy demonstrate, binary thinking divides the universe into us and them, opposing camps who can only agree on the need to fight until one camp declares victory. Lost amidst the melee is the potential gain that comes from understanding the value of both sides in the debate and using each side’s tools whenever they’re most effective.
As scientific communicators, our responsibility must be to avoid the traps of binary thinking. Life, the universe, and everything are far more interesting and complex than binary logic acknowledges, and we do our readers a tremendous disservice when we oversimplify that reality. We may seem to have the choice of communicating in binary mode—or not—but that dichotomy too is a false one. A skilled communicator adopts different solutions for different problems, or perhaps for different aspects of the same problem. This suggests that we should use binary thinking where it makes the most sense, while still remembering to supplement it with a more nuanced approach should that prove more appropriate.
One of my favorite old jokes illustrates the problem of binary thinking and the benefits to be gained from learning when to temper it with that more nuanced approach: An engineer, a physicist, and a statistician were hunting moose in Canada, and after a short walk through the marshes they spotted a huge moose. The physicist raised his gun and fired at the moose, but missed; the splash from the bullet striking water revealed that the bullet had landed 3 metres to the right of the moose. The engineer, realizing that there was a substantial breeze that the scientist had failed to account for in his theoretical model of the moose, aimed to the left of the moose and fired. Coincidentally, the wind died at that precise moment and this bullet too missed—with a splash of water exactly 3 metres to the left of the moose. The statistician immediately jumped up and down screaming, “We got him! We got him!”
In science, as well as in moose hunting, the really interesting things usually lie somewhere between the obvious extremes.
A message from our new Webmaster
by Cory Koeppen (firstname.lastname@example.org)
Hello to all! I have taken up the Webmaster role for the Scientific communications SIG. I feel this will be an opportunity for me to develop some new skills in Web site maintenance and design. On a professional level I have most of my experience in technical graphics and documentation, digital imaging, and database manipulation. Occasionally, I do graphics design work on projects for businesses, organizations, and individuals. I also build custom computer workstations—specifically, for intensive graphics and data handling.
I live in the artsy little college town of Lawrence, Kansas. This area is where I grew up. I remain a farm boy at heart. As a kid I spent a lot of my time fishing and catching critters on the farm. Today, my interest in biology and science continues at the University of Kansas, where I am continuing these interests in the pursuit of a degree (or two).
I look forward to developing the Scientific Communication SIG’s site with some new features and resources. I have collected several ideas that I will post on the site soon. If anyone has some ideas about what they would like to see on the site, please e-mail me (email@example.com). Any input would be greatly appreciated. I am working out a few usability details on a new navigation system. Visitors to the site will probably notice new content appearing just before the introduction of a new “look”. There should always be something new to look forward to on our site. There are great opportunities for this Web site and for the Scientific Communication SIG to offer very useful tools and resources to STC members and other visitors. The time has come to begin integrating more into the site. With Geoff’s excellent newsletter as a starting point, the site should prove to be an enjoyable and worthwhile place to visit well into the future.
by Jean-luc Doumont
Previously published in the IEEE Prof. Commun. Soc. Newsletter 45:6, 12 (November/December 2001)
As I was strolling through the textile section of the lovely Chilean Museum of Pre-Columbian Art in Santiago de Chile a few weeks ago, my attention was caught by a red and black woven artifact that looked like a cap with four points unexpectedly sticking out of it. Intrigued, I looked at the caption and read, “Bichromatic cap with four points”. Ah, but of course: says it all, doesn’t it?
While probably well-meant, captions such as this one above are utterly uninformative. By describing what any audience member can plainly see and immediately recognize, they are ineffectively redundant. Stating the what and not the so what of the display, they fail to convey a message, as defined in my last column (“Indeed!” in the April 2005 issue of the Exchange). And yet the guides conducting tours of the museum have many interesting things to say about the “bichromatic cap with four points”. (Clearly, an explanatory caption sets other expectations than the mere title of a work of art. In Santiago’s National Museum of Fine Arts, I stopped to admire a painting by Chilean artist Sergio Montecino, representing some sort of dark landscape. The title, “Dark landscape”, did not add much either, but I did not expect more.)
The word “bichromatic” itself seems an unnecessarily complex way to say “of two colors”, with no real gain in conciseness measured by the number of letters or syllables, at least in the English version. If fewer words at any cost is the goal, the caption might as well replace “with four points” with “tetrapointed”: wouldn’t you agree it sounds a lot more scholarly?
The museum’s apparent but unexplained obsession with color multiplicity resulted in other surprising captions. I remember admiring a black vase that was labeled, not “Black vase”, but “Monochromatic vase”. Such a caption merely raises questions: why does it focus on monochromaticity and not on any other equally apparent feature of the vase, such as shape or size? Unfortunately, it did not say.
Extreme as they may seem, the museum’s captions are not so different from most of the ones I encounter in scientific articles or technical documents. Unbelievably, a training participant even came across a block diagram labeled… “Block diagram”. Can you imagine including the photograph of a device in your document and simply adding as a caption “Photograph”?
Titles of visual aids, such as slides, are often equally useless. A graph displaying the time on the horizontal scale and sales on the vertical one is typically titled “Evolution of sales as a function of time”—another case of ineffective redundancy: since audience members can presumably read the axes of a graph, the caption can more usefully tell them what they might not readily recognize, that is, what the creator is trying to convey in the graph. A better title may thus be “Sales doubled over the last two years.”
The Chicago Manual of Style actually distinguishes between a caption (the what, as a sentence fragment) and a legend (the so what, as one or more complete sentences), possibly in sequence. Descriptive captions, however, are redundant not only with well-labeled technical illustrations, but also with well-phrased, informative legends; they have little added value, if any.
The difference between what and so what captions was clearly illustrated by two of the museum’s figurines, similar at first sight, representing a sitting man. One was labeled “Sitting man”, when it was obvious he was sitting and (believe me) equally obvious he was a man. The other was labeled “Ball player”: though not an explicit message, this caption did tell me something I could not recognize by myself.
Dr. Jean-luc Doumont teaches and provides advice on professional speaking, writing, and graphing. For more than 19 years, he has helped audiences of all ages, backgrounds, and nationalities structure their thoughts and construct their communication.
by Matthew Stevens (firstname.lastname@example.org)
The phrase “x times smaller than” crops up often, in newspapers and on television, as well as in scientific texts. Unfortunately, it usually doesn’t mean what the writer thinks it means.
Consider first the converse (“x times larger than”), because it illustrates the problem more clearly. If my cat weighs 5 kg and my dog weighs 15 kg, my dog’s weight is 2 times larger than that of my cat. That’s right, 2 times, not 3 times. Why? You can understand most easily if you start with “0 times larger”. This means “not larger”: you add the cat’s weight 0 times. So “1 time larger” means “100% larger” (add the cat’s weight once); if the cat weighs 5 kg, then 100% more is another 5 kg, making a total of 10 kg. Therefore the 15-kg dog is 2 times heavier than the 5-kg cat. To put it another way, my dog is larger than my cat. How much larger? 10 kg—or 2 times the cat’s weight (2 times larger).
To tabulate this:
|0× larger than =||no larger than =||100% (original size) =||1× the size|
|1× larger than =||100% more =||200% =||2× the size|
|2× larger than =||200% more =||300% =||3× the size|
|3× larger than =||300% more =||400% =||4× the size|
Contrast this construction with “x times the size”. My 15-kg dog is 3 times the size of my 5-kg cat. Much clearer, no? The important thing to remember is that “times larger than” does not mean the same as “times”.
As another example, if a protein content started at 1 unit and increased to 18 units, it is not correct to write “The protein content in the treated plants was 18 times larger than that in the control. It is correct to write “The protein content in the treated plants was 18 times that in the control.” We could instead write “The protein content in the treated plants was 17 times larger than the control,” but it’s misleading: the key point is the ratio of 18, not 17, and if you concentrate on the ratio, no reader will ever be misled.
Now we can tackle the “x times smaller than” usage. We’ve all read something like “The fiber is 100 times smaller than the width of a human hair.” If a human hair is 100 µm in diameter, then 100 times this is 10 000 µm or 10 mm. So the statement means that the fibre is 10 000 µm smaller than 100 µm. This is, of course, nonsense. It might not be what the author meant, but it’s what the sentence says and what some readers will assume. That might not matter to many people, but to anyone communicating science, it should.
When authors say “100 times smaller”, they usually mean “the inverse of 100 times larger”, but this is not mathematically the same. Keep in mind that although language isn’t mathematics, this usage is mathematics.
The difference in interpretation can be striking: if one reader interprets “100 times smaller” as “1/100” and another interprets it as 100 – 10 000, they will calculate very different values. That’s often unacceptable in scientific communication.
There are various ways of rephrasing such statements:
- The fiber is only 1% of the width of a human hair.
- The fiber’s width is 99% less than that of a human hair.
- The fiber is one-hundredth [or 1/100] the width of a human hair.
The same problems arise with the constructions “x-fold larger than” and “x-fold smaller than”. What does ‑fold mean? It means “in an amount multiplied by” or “multiplied by a specified number”. There’s a bay on the South Coast of New South Wales, Australia, called Twofold Bay; it has two large sub-bays. If you hear someone say “The problems are twofold”, you know that there are two parts to the problems. But what does it mean to say “the yields increased 2-fold”? Let’s start again with 0: “The yields increased zero-fold” (they didn’t increase). So “the yields increased 1-fold” means they increased by 100% (they doubled). Similarly, “the yields increased 2-fold” means they increased by 200% (they tripled). Clearly, this is the same pattern as for “times larger than”, and entails the same problem of different interpretations. Such constructions are more clearly (and correctly) expressed in any of the following ways:
- The yield increased by 100%.
- The treatment yield was 2 times the control yield.
- The yield doubled.
You can probably think of other examples—and should try doing so to practise rewording text for clarity. Clear communication is too important to permit the use of wording that so easily allows for misinterpretations.
Matthew Stevens, ELS(D), is a freelance scientific editor in Australia who has more than 20 years of experience in the field, including many years working with ESL authors. This article has been adapted from Matthew’s book Subtleties of Scientific Style, which should be published by the end of 2005. Contact Matthew to pre-order a copy, or visit http://www.sciencescape.com.au/ to check the publication status and table of contents of the book. The book will be available for download as a PDF or for order in dead-tree form.
The power of clear communication
During a patient’s 2-week follow-up appointment with his cardiologist, he informed me, his doctor, that he was having trouble with one of his medications. “Which one?” I asked. “The patch. The nurse told me to put on a new one every 6 hours and now I’m running out of places to put it!” I had him quickly undress and discovered what I hoped I wouldn’t see. Yes, the man had more than 50 patches on his body! Now, the instructions include removal of the old patch before applying a new one.—attributed to Dr. Rebecca St. Clair
“No Limits” on information design
As any parents of a teenager knows, teaching adolescents science can represent quite a challenge. Julie Czerneda, a science fiction author and editor–educator, has attacked this problem by developing the No Limits program for improving scientific literacy and inspiring interest in science by taking advantage of the interest of young students in science fiction. For more information, see:
Hart, G.J. 2005. Information design in “No limits: developing scientific literacy using science fiction”. https://www.stcsig.org/id/id_articles/No_limits_hart_0507.htm
If you’re at all involved with the teaching of science at your local schools, why not bring this article to the attention of the science teachers?
“If they can get you asking the wrong questions, they don’t have to worry about the answers.”Thomas Pynchon, writer (1937- )
“Science is organized knowledge. Wisdom is organized life.”—Immanuel Kant, philosopher (1724–1804)
“Never mistake knowledge for wisdom. One helps you make a living; the other helps you make a life.”— Sandra Carey
“Our sun is one of 100 billion stars in our galaxy. Our galaxy is one of the billions of galaxies populating the universe. It would be the height of presumption to think that we are the only living things within that enormous immensity.”—Wernher von Braun, rocket engineer (1912–1977)
“Let proportion be found not only in numbers and measures, but also in sounds, weights, times, and positions, and what ever force there is.”—Leonardo Da Vinci, painter, engineer, musician, and scientist (1452–1519)
“On two occasions, I have been asked [by members of Parliament], ‘Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?’ I am not able to rightly apprehend the kind of confusion of ideas that could provoke such a question.”—Charles Babbage
“What people call their ‘consistency’ requires them to be as ignorant today as they were a year ago. We have to live today by what truth we can get today. And be ready tomorrow to call it falsehood. Between close-minded expertise on the one hand and close-minded ignorance on the other, there’s another way: open-minded uncertainty.”—Gordan Rohman
“Facts are not science—as the dictionary is not literature.”—Martin H. Fischer