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How science works The Scientific Method is traditionally presented in the first chapter of science text- books as a simple recipe for performing scientific investigations. Though many use- ful points are embodied in this method, it can easily be misinterpreted as linear and “cookbook”: pull a problem off the shelf, throw in an observation, mix in a few ques- tions, sprinkle on a hypothesis, put the whole mixture into a 350° experiment—and voila, 50 minutes later you’ll be pulling a conclusion out of the oven! That might work if science were like Hamburger Helper®, but science is complex and cannot be re- duced to a single, prepackaged recipe.

The linear, stepwise representation of the process of science is simplified, but it does get at least one thing right. It captures the core logic of science: testing ideas with evidence. However, this version of the scientific method is so simplified and rigid that it fails to accurately portray how real science works. It more accurately describes how science is summarized after the fact—in textbooks and journal articles—than how sci- ence is actually done.

The simplified, linear scientific method implies that scientific studies follow an unvarying, linear recipe.

But in reality, in their work, scientists engage in many different activities in many different sequences. Scientific investigations often involve repeating the same steps many times to account for new information and ideas.

The simplified, linear scientific method implies that science is done by individual scientists working through these steps in isolation.

But in reality, science depends on interactions within the scientific community. Dif- ferent parts of the process of science may be carried out by different people at differ- ent times.

The simplified, linear scientific method implies that science has little room for creativity.

But in reality, the process of science is exciting, dynamic, and unpredictable. Science relies on creative people thinking outside the box!

The simplified, linear scientific method implies that science concludes.

But in reality, scientific conclusions are always revisable if warranted by the evi- dence. Scientific investigations are often ongoing, raising new questions even as old ones are answered.

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A blueprint for scientific investigations The process of science involves many layers of complexity, but the key points of that process are straightforward:

There are many routes into the process—from serendipity (e.g., being hit on the head by the proverbial apple), to concern over a practical problem (e.g., finding a new treatment for diabetes), to a technological development (e.g., the launch of a more advanced telescope)—and scientists often begin an investigation by plain old poking around: tinkering, brainstorming, trying to make some new observations, chatting with colleagues about an idea, or doing some reading.

Scientific testing is at the heart of the process. In science, all ideas are tested with evidence from the natural world, which may take many different forms—from Antarctic ice cores, to particle accelerator experiments, to detailed descriptions of sed- imentary rock layers. You can’t move through the process of science without examin- ing how that evidence reflects on your ideas about how the world works—even if that means giving up a favorite hypothesis.

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The scientific community helps ensure science’s accuracy. Members of the sci- entific community (i.e., researchers, technicians, educators, and students, to name a few) play many roles in the process of science, but are especially important in gen- erating ideas, scrutinizing ideas, and weighing the evidence for and against them. Through the action of this community, science is self-correcting. For example, in the 1990s, John Christy and Roy Spencer reported that temperature measurements taken by satellite, instead of from the Earth’s surface, seemed to indicate that the Earth was cooling, not warming. However, other researchers soon pointed out that those mea- surements didn’t correct for the fact that satellites slowly lose altitude as they orbit and that once these corrections are made, the satellite measurements were much more consistent with the warming trend observed at the surface. Christy and Spencer immediately acknowledged the need for that correction.

The process of science is intertwined with society. The process of science both influences society (e.g., investigations of X-rays leading to the development of CT scanners) and is influenced by society (e.g., a society’s concern about the spread of HIV leading to studies of the molecular interactions within the immune system).

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Exploration and discovery

The early stages of a scientific investigation often rely on making observations, ask- ing questions, and initial experimentation—essentially poking around—but the routes to and from these stages are diverse. Intriguing observations sometimes arise in surprising ways, as in the discovery of radioactivity, which was inspired by the obser- vation that photographic plates (an early version of camera film) stored next to ura- nium salts were unexpectedly exposed. Sometimes interesting observations (and the investigations that follow) are suddenly made possible by the development of a new technology. For example, the launch of the Hubble Space Telescope in 1990 allowed astronomers to make deeper and more focused observations of our universe than were ever before possible. These observations ultimately led to breakthroughs in ar- eas as diverse as star and planet formation, the nature of black holes, and the expan- sion of the universe.

Observations like this image from the Hubble Telescope can lead to further breakthroughs.

Sometimes, observations are clarified and questions arise through discussions with colleagues and reading the work of other scientists—as demonstrated by the discovery of the role of chlorofluorocarbons (CFCs) in ozone depletion …

Hubble image provided by NASA, ESA, and A. Nota (STScI/ESA)

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EXPLORING AEROSOLS

In 1973, chemists had observed that CFCs were being re- leased into the environment from aerosol cans, air condi- tioners, and other sources, but it was discussions with his colleague and advisor, Sherwood Rowland, that led Mario Molina to ask what their ultimate fate was. Since CFCs were rapidly accumulating in the atmosphere, the question was intriguing, but before he could tackle the issue (which would ultimately lead to a Nobel Prize and an explanation for the hole in the ozone layer), Molina needed more infor- mation. He had to learn more about other scientists’ stud- ies of atmospheric chemistry, and what he learned pointed to the disturbing fate of CFCs.

Furthermore, though observation and questioning are essential to the process of sci- ence, on their own, they are not enough to launch a scientific investigation; generally, scientists also need scientific background knowledge—all the information and under- standings they’ve picked up from their scientific training in school, supplemented by discussions with colleagues and reviews of the scientific literature. As in Mario Molina’s story, an understanding of what other scientists have already figured out about a par- ticular topic is critical to the process. This background knowledge allows scientists to recognize revealing observations for what they are, to make connections between ideas and observations, and to figure out which questions can be fruitfully tackled with avail- able tools. The importance of content knowledge to the process of science helps explain why science is often mischaracterized as a static set of facts contained in textbooks— science is a process, but one that relies on accumulated knowledge to move forward.

THE SCIENTIFIC STATE OF MIND

Some scientific discoveries are chalked up to the ser- endipity of being in the right place at the right time to make a key observation—but rarely does seren- dipity alone lead to a new discovery. The people who turn lucky breaks into breakthroughs are generally those with the background knowledge and scientific ways of thinking needed to make sense of the lucky observation. For example, in 1896, Henri Becquerel made a surprising observation. He found that pho- tographic plates stored next to uranium salts were spotted, as though they’d been exposed to light rays—even though they had been kept in a dark drawer. Someone else, with a less scientific state of mind and less background knowledge about physics, might have cursed their bad luck and thrown out the ruined plates. But Becquerel was intrigued by the ob- servation. He recognized it as something scientifically interesting, went on to perform follow-up experiments that traced the source of the exposure to the urani- um, and in the process, discovered radioactivity. The key to this story of discovery lies partly in Becquerel’s instigating observation, but also in his way of thinking. Along with the relevant background knowledge, Becquerel had a scientific state of mind. Sure, he made some key observations — but then he dug into them further, inquiring why the plates were exposed and trying to eliminate different potential causes of the ex- posure to get to the physical explanation behind the happy accident.

Mario Molina

The ruined photo plate that got Becquerel thinking

Henri Becquerel

Mario Molina photo by Donna Coveney/MIT

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Observation beyond our eyes We typically think of observations as having been seen “with our own eyes,” but in science, observations can take many forms. Of course, we can make observations di- rectly by seeing, feeling, hearing, and smelling, but we can also extend and refine our basic senses with tools: thermometers, microscopes, telescopes, radar, radiation sen- sors, X-ray crystallography, mass spectroscopy, etc. And these tools do a better job of observing than we can! Further, humans cannot directly sense many of the phenom- ena that science investigates (no amount of staring at this computer screen will ever let you see the atoms that make it up or the UV radiation that it emits), and in such cases, we must rely on indirect observations facilitated by tools. Through these tools, we can make many more observations much more precisely than those our basic senses are equipped to handle.

Tools like the Hubble Space Telescope, microscopes and submersibles help us to observe the natural world.

Observations yield what scientists call data. Whether the observation is an experimen- tal result, radiation measurements taken from an orbiting satellite, an infrared record- ing of a volcanic eruption, or just noticing that a certain bird species always thumps the ground with its foot while foraging — they’re all data. Scientists analyze and inter- pret data in order to figure out how those data inform their hypotheses and theories. Do they support one idea over others, help refute an idea, or suggest an entirely new explanation? Though data may seem complex and be represented by detailed graphs or complex statistical analyses, it’s important to remember that, at the most basic level, they are simply observations.

Observations inspire, lend support to, and help refute scientific hypotheses and theo- ries. However, theories and hypotheses (the fundamental structures of scientific knowledge) cannot be directly read off of nature. A falling ball (no matter how detailed our observations of it may be) does not directly tell us how gravity works, and collect- ing observations of all the different finch species of the Galapagos Islands does not di- rectly tell us how their beaks evolved. Scientific knowledge is built as people come up with hypotheses and theories, repeatedly test them against observations of the natu- ral world, and continue to refine those explanations based on new ideas and observa- tions. Observation is essential to the process of science, but it is only half the picture.

Hubble image provided by NASA; microscope photo from Scott Bauer/USDA; submersible photo from NOAA Ocean Explorer

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Testing scientific ideas

Testing hypotheses and theories is at the core of the process of science. Any aspect of the natural world could be explained in many different ways. It is the job of science to collect all those plausible explanations and to use scientific testing to filter through them, retaining ideas that are supported by the evidence and discarding the others. You can think of scientific testing as occurring in two logical steps: (1) if the idea is correct, what would we expect to see, and (2) does that expectation match what we actually observe? Ideas are supported when actual observations (i.e., results) match expected observations and are contradicted when they do not match.

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The logic of scientific arguments Taken together, the expectations generated by a scientific idea and the actual observa- tions relevant to those expectations form what we’ll call a scientific argument. This is a bit like an argument in a court case—a logical description of what we think and why we think it. A scientific argument uses evidence to make a case for whether a scientif- ic idea is accurate or inaccurate. For example, the idea that illness in new mothers can be caused by doctors’ dirty hands generates the expectation that illness rates should go down when doctors are required to wash their hands before attending births. When this test was actually performed in the 1800s, the results matched the expectations, forming a strong scientific argument in support of the idea—and hand-washing!

Though the elements of a scientific argu- ment (scientific idea, expectations gener- ated by the idea, and relevant observations) are always related in the same logical way, in terms of the process of science, those ele- ments may be assembled in different orders. Sometimes the idea comes first and then scientists go looking for the observations that bear on it. Sometimes the observations are made first, and they suggest a particular idea. Sometimes the idea and the observa- tions are already out there, and someone comes along later and figures out that the two might be related to one another.

Testing ideas with evidence may seem like plain old common sense—and at its core, it is!—but there are some subtleties to the process:

• Ideas can be tested in many ways. Some tests are relatively straightforward (e.g., raising 1000 fruit flies and counting how many have red eyes), but some re- quire a lot of time (e.g., waiting for the next appearance of Halley’s Comet), effort (e.g., painstakingly sorting through thousands of microfossils), and/or the devel- opment of specialized tools (like a particle accelerator).

• Evidence can reflect on ideas in many different ways.

• There are multiple lines of evidence and many criteria to consider in eval- uating an idea.

• All testing involves making some assumptions.

Despite these details, it’s important to remember that, in the end, hypotheses and theories live and die by whether or not they work—in other words, whether they are useful in explaining data, generating expectations, providing satisfying explanations, inspiring research questions, answering questions, and solving problems. Science fil- ters through many ideas and builds on those that work!

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Digging into data Evaluating an idea in light of the evidence should be simple, right? Either the results match the expectations generated by the idea (thus, supporting it) or they don’t (thus, refuting it). Sometimes the process is relatively simple (e.g., drilling into a coral atoll either reveals a thick layer of coral or a thin veneer), but often it is not. The real world is messy and complex, and often, in- terpreting the evidence relating to an idea is not so clear-cut. To complicate things further, we often have to weigh multiple lines of evidence that are all relevant to the validity of a particular idea.

Tests typically generate what scientists think of as raw data—unaltered observations, descriptions, or measure- ments—but those must be analyzed and interpreted. Data become evidence only when they have been interpreted in a way that reflects on the accuracy or inaccuracy of a scientific idea. For example, an investigation of the evolutionary relationships among crustaceans, insects, millipedes, spiders, and their relatives might tell us the genetic sequence of a particular gene for each organism. This is raw data, but what does it mean? A long series of the As, Ts, Gs, and Cs that make up genetic sequences don’t, by themselves, tell us whether insects are more closely related to crustaceans or to spiders. Instead, those data must be analyzed through statistical calcula- tions, tabulations, and/or visual representations. In this case, a biologist might begin to analyze the genetic data by aligning the different sequences, highlighting similari- ties and differences, and performing calculations to com- pare the different sequences. Only then can she interpret the results and figure out whether or not they support the hypothesis that insects are more closely related to crusta- ceans than to spiders.

Furthermore, the same data may be interpreted in different ways. So another scientist could analyze the same genetic data in a new way and come to a different conclusion about the relationships between insects, crustaceans, and spiders. Ultimately, the scien- tific community will come to a consensus about how a set of data should be interpreted, but this process may take some time and usually involves additional lines of evidence.

CALCULATING CONFIDENCE

Interpreting test results often means dealing with uncertainty and error. “Now, hold on,” you might be thinking, “I thought that science was supposed to build knowledge and decrease uncertainty and error.” And that’s true; however, when scientists draw a conclusion or make a calculation, they frequently try to give a statistical indication of how confident they are in the result. In everyday lan- guage, uncertainty and error mean that the answer is unclear or that a mistake has been made. However, when scientists talk about uncertainty and error, they are usually indicating their level of confidence in a number. So reporting a tem- perature to be 98.6° F (37° C) with an uncertainty of plus or minus 0.4° F actu- ally means that we are highly confident that the true temperature falls between 98.2 and 99.0° F.

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Competing ideas: A perfect fit for the evidence We’ve seen that evaluating an idea in science is not always a matter of one key ex- periment and a definitive result. Scientists often consider multiple ideas at once and test those ideas in many different ways. This process generates multiple lines of evi- dence relevant to each idea. For example, two competing ideas about coral atoll for- mation (island subsidence vs. formation on debris-topped underwater mountains) were evaluated based on multiple lines of evidence, including observations of reef and atoll shapes, island geology, stud- ies of the distribution of plank- tonic debris, and reef drilling. Furthermore, different lines of evidence are assembled cumulatively over time as dif- ferent scientists work on the problem and as new technolo- gies are developed. Because of this, the evaluation of sci- entific ideas is provisional. Science is always willing to resurrect or reconsider an idea if warranted by new evidence.

It’s no wonder then that the evaluation of scientific ideas is iterative and depends upon interactions within the scientific community. Ideas that are accepted by that community are the best explanations we have so far for how the natural world works. But what makes one idea better than another? How do we judge the accuracy of an explanation? The most important factors have to do with evidence—how well our actu- al observations fit the expectations generated by the hypothesis or theory. The better the match, the more likely the hypothesis or theory is accurate.

• Scientists are more likely to trust ideas that more closely explain the ac- tual observations. For example, the theory of general relativity explains why Mercury’s orbit around the Sun shifts as much as it does with each lap (Mercury is close enough to the Sun that it passes through the area where space-time is dim- pled by the Sun’s mass). Newtonian mechanics, on the other hand, suggests that this aberration in Mercury’s orbit should be much smaller than what we actually observe. So general relativity more closely explains our observations of Mercury’s orbit than does Newtonian mechanics.

Mercury’s orbit around the sun shifts a bit with each lap, which can be explained by the theory of general relativity.

• Scientists are more likely to trust ideas that explain more disparate ob- servations. For example, many scientists in the 17th and 18th centuries were

Atoll satellite image by NASA/Goddard Space Flight Center; coral core sample photo by Jeff Anderson, Florida Keys National Marine Sanctuary

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puzzled by the presence of marine fossils high in the Alps of Europe. Some tried to explain their presence with a massive flood, but this didn’t address why these fossils were of animals that had gone extinct. Other scientists suggested that sea level had risen and dropped several times in the past, but had no explanation for the height of the mountains. However, the theory of plate tectonics helped explain all these disparate observations (high mountains, uplifted chunks of the seafloor, and rocks so ancient that they contained the fossils of long extinct organisms) and many more, including the locations of volcanoes and earthquakes, the shapes of the continents, and huge rifts in the ocean’s floor.

• Scientists are more likely to trust ideas that explain observations that were previously inexplicable, unknown, or unexpected. For an example, see Rudolph Marcus’s story below …

JUMPING ELECTRONS!

As chemical reactions go, electron transfers might seem to be minor players: an elec- tron jumps between molecules without even breaking a chemical bond. Nevertheless, such reactions are essential to life. Photo- synthesis, for example, depends on pass- ing electrons from one molecule to another to transfer energy from light to molecules that can be used by a cell. Some of these reactions proceed at breakneck speeds, and others are incredibly slow—but why should two reactions, both involving a single electron transfer, vary in speed?

In the 1950s, Rudolph Marcus and his colleagues developed a simple mathemati- cal explanation for how the rate of the reaction changes based on the amount of free energy absorbed or released by the system. The explanation fit well with actual observations that had been made at the time, but it also generated an unintuitive expectation—that some reactions, which release a lot of energy, should proceed surprisingly slowly, and should slow down as the energy released increases. It was a bit like suggesting that for most ski slopes, a steeper incline means faster speeds, but that on the very steepest slopes, skiers will slide down slowly! The expectation generated by Marcus’s idea was entirely unanticipated, but nevertheless, almost 25 years later, experiments confirmed the surprising ex- pectation, supporting the idea and winning Marcus the Nobel Prize.

What happens when science can’t immediately produce the evidence relevant to an idea? Absence of evidence isn’t evidence of absence. Science doesn’t reject an idea just because the relevant evidence isn’t readily available. Sometimes, we have to wait for an event (e.g., the next solar eclipse), hope for a key discovery (e.g., transitional whale fossils in the deserts of Pakistan), or try to develop a new technology (e.g., a more powerful telescope), and until then, must suspend our judgment of an idea.

Rudolph Marcus

Rudolph Marcus image provided by the California Institute of Technology

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Competing ideas: Other considerations In evaluating scientific ideas, evidence is the main arbiter; however, sometimes the available evidence supports several different hypotheses or theories equally well. In those cases, science often applies other criteria to evaluate the explanations. Though these are more like rules of thumb than firm standards, scientists are more likely to put their trust in ideas that:

• generate more specific expectations (i.e., are more testable). For example, a hypothesis about hurricane formation that generates more specific expectations about the conditions under which they are likely to form might be preferred over one that just suggests what time of year they should be common.

• can be more broadly applied. For example, a theory about the nature of force that applies to both macroscopic interactions (e.g., the pull of Earth’s gravity on an apple) and subatomic interactions (e.g., between protons and electrons) might be preferred over one that only applies to interactions between large objects.

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• are more parsimonious. For example, a hypothesis about the evolutionary rela- tionships among hummingbird species that involves only 70 evolutionary changes might be preferred over one that postulates 200 changes.

THE PRINCIPLE OF PARSIMONY

The principle of parsimony suggests that when two explanations fit the observa- tions equally well, a simpler explanation should be preferred over a more convo- luted and complex explanation. For a hypothetical illustration, imagine that we have only a few lines of evidence in a case of cookie jar pilfering: a broken and empty cookie jar, a crumb trail leading to the doggie door, and Fido’s bellyache. Perhaps Fido stole the cookies, or perhaps it was all a set-up: the parrot knocked the jar off the table and ate the cookies, the cat tracked the crumbs to the door, and Fido has a bellyache because he got into the neighbor’s garbage can. Both explanations fit all the available evidence—but which is more parsimonious?

• are more consistent with well-established theories in neighboring fields. For example, a major argument against the theory of evolution when Darwin first proposed it was that the theory didn’t mesh with what was known about the age of the Earth at the time. Physicists had estimated the Earth to be just 100 million years old, a length of time that was deemed insufficient for evolution to account for the diversity of life on Earth today. However, as our understanding of geol- ogy and physics have improved, the age of the Earth has been more accurately pegged at several billion years old—a view that squares well with the idea that all life on Earth evolved from a common ancestor.

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• generate more new ideas. For example, evolutionary biology not only helps us understand the history of life on Earth, but also generates useful ideas that can be applied to many fields—most notably in medicine, agriculture, and conserva- tion. The power of evolution to generate fruitful ideas in many other fields rein- forces its value as a theory.

All this might seem complex, but it’s important to keep the main point in mind. These criteria are just guidelines for identifying ideas that work—ideas that fit the evidence, generate new expectations, inspire further research, and seem to be accurate expla- nations for how the world works!

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Analysis within the scientific community The stereotype of a scientist (a recluse who speaks in a jumble of technical jargon) doesn’t exactly paint a picture of someone whose work depends on communication and community, but in fact, interactions within the scientific community are essential components of the process of science. Scientists don’t work in isolation. Though they sometimes work alone (fussing over an experiment in the lab, trekking through the Amazon, scribbling on a notepad at a desk), scientists are just as likely to be found emailing colleagues, arguing with other scientists over coffee, sitting in on a lab meet- ing, or preparing conference presentations and journal articles. In science, even those few working entirely on their own must ultimately share their work for it to become part of the lasting body of scientific knowledge.

In terms of the process of science, members of the community play several essential and direct roles:

Fact checker/critic: the community evaluates evidence and ideas. The scrutiny of the scientific community helps ensure that evidence meets high standards of quality, that all rel- evant lines of evidence are explored, and that judgments are not based on flawed reasoning.

Innovator/visionary: the community generates new ideas. Interactions within a diverse and creative community spark ideas about new lines of evidence, new interpretations of ex- isting data, new applications, new questions, and alternate explanations—all of which help science move forward.

Watchdog/whistleblower: the community helps eliminate bias and fraud by keeping watchful eye. Though fraud is rare and bias often unintentional, the occasional cases of such of- fenses are detected through the scrutiny and ongoing work of the scientific community.

Cheerleader/taskmaster: the community motivates sci- entists. The community offers the prospects of recognition, esteem, and a scientific legacy—payoffs which help motivate many scientists in their investigations.

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Interactions within the scientific community and the scrutiny they entail take time and can slow the process of science. However, these interactions are crucial because they help ensure that science provides us with more and more accurate and useful descrip- tions of how the world works.

So how, exactly, does the scientific community manage to play all these challenging roles? To learn more about key features of community analysis—publication, peer re- view, and replication—read on …

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Publish or perish? Among academics, the maxim “publish or perish” (i.e., publish your research or risk losing your job) is a threatening reminder of the importance of publication. Despite its cynicism, the phrase makes an impor- tant point: publishing findings, hypoth- eses, theories, and the lines of reasoning and evidence relevant to them is critical to the progress of science. The scientific community can only fulfill its roles as fact checker, visionary, whistleblower, and cheerleader if it has trusted information about the work of community members. Scientists distribute information about their ideas in many ways—informally com- municating with colleagues, making pre- sentations at conferences, writing books, etc.—but among these different modes of communication, peer-reviewed journal ar- ticles are especially important.

What’s in a scientific journal article?

A journal article is a formal, souped-up version of the standard high school lab report. In journal articles, scientists (usually a group of collaborators) describe a study and report any details one might need to evaluate that study—background information, data, statistical results, graphs, maps, explanations of how the study was performed and how the researchers drew their conclusions, etc. These articles are published in scientific journals either in print or on the internet. Print journals look much like any magazine, except that they are chock full of firsthand reports of scientific research. Journals distribute scientific information to researchers all around the world so that they can keep current in their fields and evaluate the work of their peers.

Journal articles neaten up the messy process of science, presenting ideas, evidence, and reasoning in a way that’s easy to understand—in contrast to the often circuitous (and sometimes tedious) process of science. For an example, check out Walter Alva- rez’s story below …

UNTANGLING A TWISTED PATH

In 1980, in the journal Science, Walter Alvarez and his colleagues published a scientific article describing their controversial new hypothesis that the dinosaur extinction was triggered by a massive asteroid im- pact. Despite its splashy and novel topic, the article laid out its hypothesis and evidence in the conven- tional way—linearly—which allowed colleagues in geology and paleontology to quickly understand and evaluate the research. Though helpful for scientific communication, this linear presentation can give the impression that an investigation has been plotted out

from the beginning—but in fact, Alvarez’s study was far from linear. He stumbled onto his hypothesis unexpectedly, originally setting out to study the tectonic movements of the Italian peninsula. After an intriguing series of twists, turns, false starts, inspirations, and rejected hypotheses, he and his colleagues found that they had completed a rather different, but compelling, investigation.

Walter Alvarez

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Scrutinizing science: Peer review Peer review does the same thing for science that the “inspected by #7” sticker does for your t-shirt: provides assurance that someone who knows what they’re doing has double-checked it. In science, peer review typically works something like this:

1) A group of scientists completes a study and writes it up in the form of an article. They submit it to a journal for publication.

2) The journal’s editors send the article to several other scientists who work in the same field (i.e., the “peers” of peer review).

3) Those reviewers provide feedback on the article and tell the editor whether or not they think the study is of high enough quality to be published.

4) The authors may then revise their article and resubmit it for consideration.

5) Only articles that meet good scientific standards (e.g., acknowledge and build upon other work in the field, rely on logical reasoning and well-designed studies, back up claims with evidence, etc.) are accepted for publication.

Peer review and publication are time-consuming, frequently involving more than a year between submission and publication. The process is also highly competitive. For example, the highly-regarded journal Science accepts less than 8% of the ar- ticles it receives, and The New England Journal of Medicine publishes just 6% of its submissions.

Peer-reviewed articles provide a trusted form of scientific communication. Even if you are unfamiliar with the topic or the scientists who authored a particular study, you can

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trust peer-reviewed work to meet certain standards of scientific quality. Since scientif- ic knowledge is cumulative and builds on itself, this trust is particularly important. No scientist would want to base their own work on someone else’s unreliable study! Peer- reviewed work isn’t necessarily correct or conclusive, but it does meet the standards of science. And that means that once a piece of scientific research passes through peer review and is published, science must deal with it somehow—perhaps by incorpo- rating it into the established body of scientific knowledge, building on it further, figur- ing out why it is wrong, or trying to replicate its results.

PEER REVIEW: NOT JUST SCIENCE

Many fields outside of science use peer review to ensure quality. Philosophy jour- nals, for example, make publication decisions based on the reviews of other phi- losophers, and the same is true of scholarly journals on topics as diverse as law, art, and ethics. Even those outside the research community often use some form of peer review. Figure-skating championships may be judged by former skaters and coaches. Wine-makers may help evaluate wine in competitions. Artists may help judge art contests. So while peer review is a hallmark of science, it is not unique to science.

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Copycats in science: The role of replication Scientists aim for their studies’ findings to be replicable—so that, for example, an experiment testing ideas about the attraction between electrons and protons should yield the same results when repeated in different labs. Similarly, two different re- searchers studying the same dinosaur bone in the same way should come to the same conclusions regarding its measurements and composition. This goal of replicability makes sense. After all, science aims to reconstruct the unchanging rules by which the universe operates, and those same rules apply, 24 hours a day, seven days a week, from Sweden to Saturn, regardless of who is studying them. If a finding can’t be repli- cated, it suggests that our current understanding of the study system or our methods of testing are insufficient.

Does this mean that scientists are constantly repeating what others before them have already done? No, of course not—or we would never get anywhere at all. The process of science doesn’t require that every experiment and every study be repeated, but many are, especially those that produce surprising or particularly important results. In some fields, it is standard procedure for a scientist to replicate his or her own results before publication in order to ensure that the findings were not due to some fluke or factors outside the experimental design.

The desire for replicability is part of the reason that scientific papers almost always in- clude a methods section, which describes exactly how the researchers performed the study. That information allows other scientists to replicate the study and to evaluate its quality, helping ensure that occasional cases of fraud or sloppy scientific work are weeded out and corrected.

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Science at multiple levels The process of science works at multiple levels—from the small scale (e.g., a compari- son of the genes of three closely related North American butterfly species) to the large scale (e.g., a half-century-long series of investigations of the idea that geographic isolation of a population can trigger speciation). The process of science works in much the same way whether embodied by an individual scientist tackling a specific problem, question, or hypothesis over the course of a few months or years, or by a community of scientists coming to agree on broad ideas over the course of decades and hundreds of individual experiments and studies. Similarly, scientific explanations come at differ- ent levels:

Hypotheses

Hypotheses are proposed explanations for a fairly narrow set of phenomena. These reasoned explanations are not guesses—of the wild or educated variety. When scien- tists formulate new hypotheses, they are usually based on prior experience, scientific background knowledge, preliminary observations, and logic. For example, scientists observed that alpine butterflies exhibit characteristics intermediate between two spe- cies that live at lower elevations. Based on these observations and their understanding of speciation, the scientists hypothesized that this species of alpine butterfly evolved as the result of hybridization between the two other species living at lower elevations.

Theories

Theories, on the other hand, are broad explanations for a wide range of phenomena. They are concise (i.e., generally don’t have a long list of exceptions and special rules), coherent, systematic, predictive, and broadly applicable. In fact, theories often inte- grate and generalize many hypotheses. For example, the theory of natural selection broadly applies to all populations with some form of inheritance, variation, and differ- ential reproductive success—whether that population is composed of alpine butterflies, fruit flies on a tropical island, a new form of life discovered on Mars, or even bits in a computer’s memory. This theory helps us understand a wide range of observations (from the rise of antibiotic-resistant bacteria to the physical match between pollinators and their preferred flowers), makes predictions in new situations (e.g., that treating AIDS patients with a cocktail of medications should slow the evolution of the virus), and has proven itself time and time again in thousands of experiments and observa- tional studies.

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“JUST” A THEORY?

Occasionally, scientific ideas (such as biological evolution) are written off with the putdown “it’s just a theory.” This slur is misleading and conflates two separate meanings of the word theory: in common usage, the word theory means just a hunch, but in science, a theory is a powerful explanation for a broad set of ob- servations. To be accepted by the scientific community, a theory (in the scientific sense of the word) must be strongly supported by many different lines of evi- dence. So biological evolution is a theory (it is a well-supported, widely accepted, and powerful explanation for the diversity of life on Earth), but it is not “just” a theory.

Words with both technical and everyday meanings often cause confusion. Even scientists sometimes use the word theory when they really mean hypothesis or even just a hunch. Many technical fields have similar vocabulary problems — for example, both the terms work in physics and ego in psychology have specific meanings in their technical fields that differ from their common uses. However, context and a little background knowledge are usually sufficient to figure out which meaning is intended.

Over-arching theories

Some theories, which we’ll call over-arching theories, are particularly important and reflect broad understandings of a particular part of the natural world. Evolutionary theory, atomic theory, gravity, quantum theory, and plate tectonics are examples of this sort of over-arching theory. These theories have been broadly supported by mul- tiple lines of evidence and help frame our understanding of the world around us.

Such over-arching theories encompass many subordinate theories and hypotheses, and consequently, changes to those smaller theories and hypotheses reflect a refine- ment (not an overthrow) of the over-arching theory. For example, when punctuated equilibrium was proposed as a mode of evolutionary change and evidence was found supporting the idea in some situations, it represented an elaborated reinforcement of evolutionary theory, not a refutation of it. Over-arching theories are so important be- cause they help scientists choose their methods of study and mode of reasoning, con- nect important phenomena in new ways, and open new areas of study. For example, evolutionary theory highlighted an entirely new set of questions for exploration: How did this characteristic evolve? How are these species related to one another? How has life changed over time?

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A MODEL EXPLANATION

Hypotheses and theories can be complex. For example, a particular hypothesis about meteorological interactions or nuclear reactions might be so complex that it is best described in the form of a computer program or a long mathematical equation. In such cases, the hypothesis or theory may be called a model.

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Even theories change Accepted theories are the best explanations available so far for how the world works. They have been thoroughly tested, are supported by multiple lines of evidence, and have proved useful in generating explanations and opening up new areas for research. However, science is always a work in progress, and even theories change. How? We’ll look at some over-arching theories in physics as examples:

• Classical mechanics In the 1600s, building on the ideas of others, Isaac Newton constructed a theory (sometimes called classical mechanics or Newtonian mechanics) that, with a sim- ple set of mathematical equations, could explain the movement of objects both in space and on Earth. This single explanation helped us understand both how a thrown baseball travels and how the planets orbit the sun. The theory was power- ful, useful, and has proven itself time and time again in studies; yet it wasn’t per- fect …

• Special relativity Classical mechanics was one-upped by Albert Einstein’s theory of special relativity. In contrast to the assumptions of classical mechanics, special relativity postulated that as one’s frame of reference (i.e., where you are and how you are moving) changes, so too do measurements of space and time—so that, for example, a person speeding away from Earth in a spacecraft will perceive the distance of the spacecraft’s travel and the elapsed time of the trip to be different than would a person sitting at Cape Canaveral. Special relativity was preferred because it ex- plained more phenomena: it accounted for what was known about the movement of large objects (from baseballs to planets) and helped explain new observations relating to electricity and magnetism.

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• General relativity Even special relativity was superseded by another theory. General relativity helped explain everything that special relativity did, as well as our observations of gravitational forces.

• Our next theory … General relativity has been enormously successful and has generated unique ex- pectations that were later borne out in observations, but it too seems up for a change. For example, general relativity doesn’t mesh with what we know about the interactions between extremely tiny particles (which the theory of quantum mechanics addresses). Will physicists develop a new theory that simultaneously helps us understand the interactions between the very large and the very small? Time will tell, but they are certainly working on it!

All the theories described above worked—that is, they generated accurate expecta- tions, were supported by evidence, opened up new avenues of research, and offered satisfying explanations. Classical mechanics, by the way, is still what engineers use to design airplanes and bridges, since it is so accurate in explaining how large (i.e., macroscopic) and slow (i.e., substantially slower than light) objects interact. Never- theless, the theories described above did change. How? A well-supported theory may be accepted by scientists, even if the theory has some problems. In fact, few theories fit our observations of the world perfectly. There is usually some anomalous observa- tion that doesn’t seem to fit with our current understanding. Scientists assume that by working at such anomalies, they’ll either disentangle them to see how they fit with the current theory or contribute to a new theory. And eventually that does happen: a new or modified theory is proposed that explains everything that the old theory ex- plained plus other observations that didn’t quite fit with the old theory. When that new or modified theory is proposed to the scientific community, over a period of time (it might take years), scientists come to understand the new theory, see why it is a supe- rior explanation to the old theory, and eventually, accept the new theory.

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Theory change is a community process of feedback, experiment, observation, and com- munication. It usually involves interpreting existing data in new ways and incorporating those views with new results. It may depend on a single definitive experiment or observa- tion to change people’s views, or it may involve many separate studies, eventually tipping the balance of evidence in favor of the new theory. The process may take some time since scien- tists don’t always recognize good ideas right away, but eventually the scientific explanation that is more accurate will win out. This process of theory change often involves true scientific controversy, which is healthy, sparks additional research, and helps science move forward. True scientific controversy involves disagreements over how data should be interpreted, over which ideas are best supported by the avail- able evidence, and over which ideas are worth investigating further.

SCIENTIFIC CONTROVERSY: TRUE OR FALSE?

Here, we’ve discussed true scientific controversy—a debate within the scientific community over which scientific idea is more accurate and should be used as the basis of future research. True scientific controversy involves competing scien- tific ideas that are evaluated according to the standards of science—i.e., fitting the evidence, generating accurate expectations, offering satisfying explanations, inspiring research, etc. However, occasionally, special interest groups try to mis- represent a non-scientific idea, which meets none of these standards, as inspiring scientific controversy.

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Summing up the process In this section, we’ve seen that the real process of science is not much like The Scientific Method often portrayed in textbooks. As opposed to the simple recipe of the linear scientific method, the real process of science is exciting, iterative, nonlinear, nuanced, depends upon the scientific community, and is intertwined with the society at large. The real process of science proceeds at multiple levels and sorts through many ideas, retaining and building upon those that work. However, despite all these complications, the core of that process, checking ideas against evidence from the natural world, is straightforward.

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