How do we go about trying to understand the vastness and complexity of nature? For most scientists, studies of the natural world involve the complementary processes of observation and experimentation. Observation is the act of viewing the world around us. Experimentation is a disciplined and controlled way of asking and answering questions about the world in an unbiased manner.

Observations allow us to ask focused questions about nature. Let’s say you observe a hummingbird like the one pictured in Fig. 1.1 hovering near a red flower, occasionally dipping its long beak into the bloom. What motivates this behavior? Is the bird feeding on some substance within the flower? Is it drawn to the flower by its vivid color? What benefit, if any, does the flower derive from this busy bird?

Observations such as these, and the questions they raise, allow us to propose tentative explanations, or hypotheses. We might, for example, hypothesize that the hummingbird is carrying pollen from one flower to the next, facilitating reproduction in the plant. Or we might hypothesize that nectar produced deep within the flower provides nutrition for the hummingbird—that the hummingbird’s actions reflect the need to take in food. Both hypotheses provide a reasonable explanation of the behavior we observed, but they may or may not be correct. To find out, we have to test them.

Charles Darwin’s classic book, On the Origin of Species, published in 1859, beautifully illustrates how we can piece together individual observations to construct a working hypothesis. In this book, Darwin discussed a wide range of observations, from pigeon breeding to fossils and from embryology to the unusual animals and plants found on islands. Darwin noted the success of animal breeders in selecting specific individuals for reproduction and thereby generating new breeds for agriculture or show. He appreciated that selective breeding is successful only if specific features of the animals can be passed from one generation to the next by inheritance. Reading economic treatises by the English clergyman Thomas Malthus, he understood that limiting environmental resources could select among the variety of different individuals in populations in much the way that breeders select among cows or pigeons.

Gathering together all these seemingly disparate pieces of information, Darwin argued that life has evolved over time by means of natural selection. Since its formulation, Darwin’s initial hypothesis has been tested by experiments, many thousands of them. Our knowledge of many biological phenomena, ranging from biodiversity to the way the human brain is wired, depends on direct observation followed by careful inferences that lead to models of how things work.

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Not just any idea qualifies as a hypothesis. Two features set hypotheses apart from other ways of attacking problems. First, a good hypothesis makes predictions about observations not yet made or experiments not yet run. Second, because hypotheses make predictions, we can test them. That is, we can devise an experiment to see whether the predictions made by the hypothesis actually occur, or we can go into the field to try to make further observations predicted by the hypothesis. A hypothesis, then, is a statement about nature that can be tested by experiments or by new observations. Hypotheses are testable because, even as they suggest an explanation for observations made previously, they make predictions about observations yet to be made.

Quick Check 1 Mice that live in sand dunes commonly have light tan fur. Develop a hypothesis to explain this coloration.

Once we have a hypothesis, we can test it to see if its predictions are accurate. Returning to the hummingbird and flower, we can test the hypothesis that the bird is transporting pollen from one flower to the next, enabling the plant to reproduce. Observation provides one type of test: If we catch and examine the bird just after it visits a flower, do we find pollen stuck to its beak or feathers? If so, our hypothesis survives the test.

Note, however, that we haven’t proved the case. Pollen might be stuck on the bird for a different reason—perhaps it provides food for the hummingbird. However, if the birds didn’t carry pollen from flower to flower, we would reject the hypothesis that they facilitate pollination. In other words, a single observation or experiment can lead us to reject a hypothesis, or it can support the hypothesis, but it cannot prove that a hypothesis is correct. To move forward, then, we might make a second set of observations. Does pollen that adheres to the hummingbird rub off when the bird visits a second flower of the same species? If so, we have stronger support for our hypothesis.

We might also use observations to test a more general hypothesis about birds and flowers. Does red color generally attract birds and so facilitate pollination in a wide range of flowers? To answer this question, we might catalog the pollination of many red flowers and ask whether they are pollinated mainly by birds. Or we might go the opposite direction and catalog the flowers visited by many different birds—are they more likely to be red than chance alone might predict?

Finally, we can test the hypothesis that the birds visit the flowers primarily to obtain food, spreading pollen as a side effect of their feeding behavior. We can measure the amount of nectar in the flower before and after the bird visits and calculate how much energy has been consumed by the bird during its visit. Continued observations over the course of the day will tell us whether the birds gain the nutrition they need by drinking nectar, and whether the birds have other sources of food.

In addition to observations, in many cases we can design experiments to test hypotheses. One of the most powerful types of experiment is called a controlled experiment. In a controlled experiment, the researcher sets up several groups to be tested, keeping the conditions and setup as similar as possible from one group to the next. Then, the researcher deliberately introduces something different, known as a variable, into one group that he or she hypothesizes might have some sort of an effect. This is called the test group. In another group, the researcher does not introduce this variable. This is a control group, and the expectation is that no effect will occur in this group.

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Controlled experiments are extremely powerful. By changing just one variable at a time, the researcher is able to determine if that variable is important. If many variables were changed at once, it would be difficult, if not impossible, to draw conclusions from the experiment because the researcher would not be able to figure out which variable caused the outcome. The control group plays a key role as well. Having a group in which no change is expected ensures that the experiment works as it is supposed to and provides a baseline against which to compare the results of the test groups.

For example, we might test the hypothesis that hummingbirds facilitate pollination by doing a controlled experiment. In this case, we could set up groups of red flowers that are all similar to one another. For one group, we could surround the flowers with a fine mesh that allows small insects access to the plant but keeps hummingbirds away. For another group, we would not use a mesh. The variable, then, is the presence of a mesh; the test group is the flowers with the mesh; and the control group is the flowers without the mesh since the variable was not introduced in this group.

Will the flowers be pollinated? If only the group without the mesh is pollinated, this result lends support to our initial hypothesis. In this case, the hypothesis becomes less tentative and more certain. If both groups are pollinated, our hypothesis is not supported, in which case we may discard it for another explanation or change it to account for the new information.

Quick Check 2 Design a controlled experiment that tests the hypothesis that cigarette smoke causes lung cancer.

Using observations to generate a hypothesis and then making predictions based on that hypothesis that can be tested experimentally are the first two steps in the scientific method, outlined in Fig. 1.2. The scientific method is a deliberate and careful way of asking questions about the unknown. We make observations, collect field or laboratory samples, and design and carry out experiments or analyses to make sense of things we initially do not understand. The scientific method has proved to be spectacularly successful in helping us to understand the world around us. We explore several aspects of the scientific method, including experimental design, data and data presentation, probability and statistics, and scale and approximation on LaunchPad.

To emphasize the power of the scientific method, we turn to a famous riddle drawn from the fossil record (Fig. 1.3). Since the nineteenth century, paleontologists have known that before mammals expanded to their current ecological importance, other large animals dominated Earth. Dinosaurs evolved about 210 million years ago and disappeared abruptly 66 million years ago, along with many other species of plants, animals, and microscopic organisms. In many cases, the skeletons and shells of these creatures were buried in sediment and became fossilized. Layers of sedimentary rock therefore record the history of Earth.

Working in Italy, the American geologist Walter Alvarez collected samples from the precise point in the rock layers that corresponds to the time of the extinction. Careful chemical analysis showed that rocks at this level are unusually enriched in the element iridium. Iridium is rare in most rocks on continents and the seafloor, but is relatively common in rocks that fall from space—that is, in meteorites. From these observations, Alvarez and his colleagues developed a remarkable hypothesis: 66 million years ago, a large (11-km diameter) meteor slammed into Earth, and in the resulting environmental havoc, dinosaurs and many other species became extinct. This hypothesis makes specific predictions, described in Fig. 1.3, which turned out to be supported by further observations. Thus, observational tests support the hypothesis that nearly 150 million years of dinosaur evolution were undone in a moment.

As already noted, a hypothesis may initially be tentative. Commonly, in fact, it will provide only one of several possible ways of explaining existing data. With repeated observation and experimentation, however, a good hypothesis gathers strength, and we have more and more confidence in it. When a number of related hypotheses survive repeated testing and come to be accepted as good bases for explaining what we see in nature, scientists articulate a broader explanation that accounts for all the hypotheses and the results of their tests. We call this statement a theory, a general explanation of the world supported by a large body of experiments and observations (see Fig. 1.2).

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Note that scientists use the word “theory” in a very particular way. In general conversation, “theory” is often synonymous with “hypothesis,” “idea,” or “hunch”—“I’ve got a theory about that.” But in a scientific context, the word “theory” has a specific meaning. Scientists speak in terms of theories only if hypotheses have withstood testing to the point where they provide a general explanation for many observations and experimental results. Just as a good hypothesis makes testable predictions, a good theory both generates good hypotheses and predicts their outcomes. Thus, scientists talk about the theory of gravity—a set of hypotheses you test every day by walking down the street or dropping a fork. Similarly, the theory of evolution is not one explanation among many for the unity and diversity of life. It is a set of hypotheses that has been tested for more than a century and shown to provide an extraordinarily powerful explanation of biological observations that range from the amino acid sequences of proteins to the diversity of ants in a rain forest. In fact, as we discuss throughout this book, evolution is the single most important theory in all of biology. It provides the most general and powerful explanation of how life works.