How is a Hypothesis Related to an Observations Models in Nature Study

In studying nature, scientists make observation and form and test hypotheses

Science is a way of knowing an approach to understanding the natural world. It developed out of our curiosity about ourselves, other life forms, our planet, and the universe. The word science is derived from a Latin verb meaning “to know.” Striving to understand seems to be one of our basic urges.

At the heart of science is inquiry, a search for information and explanations of natural phenomena. There is no formula for successful scientific inquiry, no single scientific method that researchers must rigidly follow. As in all quests, science includes following elements of challenge, adventure, and luck, along with careful planning, reasoning, creativity, patience, and the persistence to overcome setbacks. Such diverse elements of inquiry make science far less structured than most people realize. That said, it is possible to highlight certain following characteristics that help to distinguish science from other ways of describing and explaining nature/environment.

Scientists use a following process of inquiry that includes making an observations, forming logical, testable explanations (hypotheses), and testing them. The process is necessarily repetitive: In testing a hypothesis, more observations may inspire revision of the original hypotheses or formation of a new one, thus leading to further testing. In this way, scientists circle closer and closer to their best estimation of the laws governing nature.

Exploration and Observation

Our innate curiosity often stimulates us to pose questions about the natural basis for the phenomena we observe in the world. For example, what related causes the roots of a plant seedling to grow downward? In fine tuning their questions, biologists rely heavily on the scientific literature, the published contributions of fellow scientists. By reading about and understanding past studies, scientists can build on the foundation of existing knowledge, focusing their investigations on observations that are original and on hypotheses that are consistent with previous findings. Identifying publications relevant to a new line of research is now easier than at any point in the past, thanks to indexed and searchable electronic databases.

In the course of their work, biologists make careful to observations. In gathering information, they often use tools such as microscopes, precision thermometers, or high speed cameras that extend their senses or facilitate careful measurement.

Observations can reveal valuable information about the natural world. For example, a series of detailed observation have shaped our understanding of cell structure, and another set of observations is currently expanding our databases of genome sequences from diverse species and databases of genes whose expression is altered in related various diseases.

Recorded in observations are called data. Put another way, data are items of information on which scientific inquiry is based. The term data implies is numbers to many people. But some data are qualitative, often in the form of recorded descriptions rather than numerical measurements. For example, Jane Goodall spent decades recording her related observation of chimpanzee behavior during field research in a Tanzanian jungle (Figure below).

Figure Jane Goodall collecting qualitative data on chimpanzee behavior. Goodall recorded her observations in field notebooks, often with sketches of the animals’ behavior.

In her studies, Goodall also enriched the field of animal behavior with volumes of quantitative data, such as the frequency and duration of specific behaviors for different members of a group of chimpanzees in a variety of situations. Quantitative data are generally expressed as numerical measurements and often organized into tables and graphs. Scientists analyze their data using a type of mathematics called statistics to test whether their results are significant or merely due to random fluctuations. All results presented in this text have been shown to be statistically significant.

Collecting and analyzing observations can lead to important conclusions based on a type of logic called inductive reasoning. Through induction, we derive generalizations from a large number of specific observations. “The sun always rises in the east” is an example. And so is “All organisms are made of cells.” Careful observation and data analyses, along with generalizations reached by induction, are fundamental to our understanding of nature.

Forming and Testing Hypotheses

After carrying out preliminary observation and collecting and analyzing data, scientists begin to form tentative answers to their original questions and to test their hypothetical explanations that is, their hypotheses. In science, a hypotheses is an explanation, based on observations and assumptions, that leads to a testable prediction. Said another way, a hypothesis is an explanation on trial. The hypotheses is usually a rational accounting for a set of observations, based on the available data and guided by inductive reasoning.

A scientific hypothesis must lead to predictions that can be tested by making additional observations or by performing experiments. An experiment is a scientific test, carried out under controlled conditions.

We all make observations and develop questions and hypotheses in solving everyday problems. Let’s say, for example, that your desk lamp is plugged in and turned on but the bulb isn’t lit. That’s an observation. The question is obvious: Why doesn’t the lamp work? Two reasonable hypotheses based on your experience are that (1) the bulb is not screwed in properly or (2) the bulb is burnt out. Each of these alternative hypotheses leads to predictions you can test with experiments. For example, the improperly screwed in bulb hypothesis predicts that carefully re installing the bulb will fix the problem. Figure diagrams this informal inquiry. Figuring things out in this way by trial and error is a hypothesis based approach.

Figure A simplified view of the scientific process. The idealized process sometimes called the “scientific method” is shown in this flow chart, which illustrates hypothesis testing for a desk lamp that doesn’t work.

Deductive Reasoning

A type of logic called deduction is also built into the use of hypotheses in science. While induction entails reasoning from a set of specific observations to reach a general conclusion, deductive reasoning involves logic that flows in the opposite direction, from the general to the specific. From general premises, we extrapolate to the specific results we should expect if the premises are true. In the scientific process, deductions usually take the form of predictions of results that will be found if a particular hypothesis (premise) is correct. We then test the hypothesis by carrying out experiments or observations to see which whether or not the results are as predicted. This deductive testing takes the form of “If . . . then” logic. In the case of the desk lamp example: If the burnt out bulb hypothesis is correct, then the lamp should work if you replace the bulb with a new one.

We can use the desk lamp example to illustrate the two other key points about the use of hypotheses in science. First, one can always devise additional hypotheses to explain a set of observations. For instance, another hypothesis to explain our nonworking desk lamp is that the electrical socket is broken. Although you could observation the design models an experiment to test this hypothesis, you can never test all possible hypotheses.

Second, we can never prove that a hypothesis is true. Based on the experiments shown in Figure diagram, the burnt out bulb hypothesis is the most likely explanation, but testing supports that hypothesis not by proving that it is correct, but rather by failing to prove it incorrect. For example, even if replacing the bulb fixed the desk lamp, it might have been because there was a temporary power outage that just happened to end while the bulb was being changed.

Although a hypothesis can never be proved beyond the shadow of a doubt, testing it in various ways can significantly increase our confidence in its validity. Often, rounds of hypothesis formulation and testing lead to a scientific consensus the shared conclusion of many scientists that a particular hypothesis explains the known data well and stands up to experimental testing.

Questions That Can and Cannot Be Addressed by Science

Scientific inquiry is a powerful way to learn about nature, but there are limitations to the kinds of questions it can answer. A scientific hypothesis must be testable; there must be some observation or experiment that could reveal if such an idea is likely to be true or false. The hypothesis that a burnt-out bulb is the sole reason the lamp doesn’t work would not be supported if replacing the bulb with a new one didn’t fix the lamp.

Not all hypotheses meet the criteria of science: You wouldn’t be able to test the theory that invisible ghosts are fooling with your desk lamp! Because science only deals with natural, testable explanations for natural phenomena, it can neither support nor contradict the invisible ghost hypothesis, nor whether theory spirits or elves cause storms, rainbows, or illnesses. Such supernatural explanations are simply outside the bounds of science, as are religious matters, which are issues of personal faith. Science and religion are not mutually exclusive or contradictory; they are simply concerned with different issues.

The Flexibility of the Scientific Process

The desk lamp example of Figure diagram at top traces an idealized process of inquiry sometimes called the scientific method. However, very few scientific inquiries adhere rigidly to the sequence of steps that are typically used to describe this approach. For example, a scientist may start to design models an experiment, but then backtrack after realizing that more preliminary observations are necessary. In other cases, observations remain too puzzling to prompt well defined questions until further study observation provides a new context in which to view those observations. For example, scientists could not unravel the details of genes encode proteins until after the discovery of the structure of DNA (an event that took place in 1953).

A more realistic following models of the scientific process is shown in Figure below. The focus of this model, shown in the central circle in the figure, is the forming and testing of hypotheses. This core set of activities is the reason that science does so well in explaining phenomena in the natural world. These activities, however, are shaped by exploration and discovery (the upper circle in Figure) and influenced by interactions with other scientists and with society more generally (lower circles). For example, the community of scientists influences which hypotheses are tested, how test results are interpreted, and what value is placed on the findings. Similarly, societal needs such as the push to cure cancer or understand the process related of climate change may help shape what research projects are funded and how extensively the results are discussed.

Figure The process of science: A realistic model. In reality, the process of science is not linear, but is more circular, involving backtracking, repetitions, and interactions of different parts of the process. This illustration is based on a models (How Science Works) from the website Understanding Science (www.understandingscience.org).

Now that we have highlighted the key features of scientific inquiry making observations and forming and testing hypotheses you should be able to recognize these features in a case study of actual scientific research.

A Case Study in Scientific Inquiry: Investigating Coat Coloration in Mouse Populations

Our case study begins with a set of observations and inductive generalizations. Color patterns of animals vary widely in nature, sometimes even among members of the same species. What accounts for such variation? As you may recall, the two mice depicted at the beginning of this plengdut.com article post are members of the same species (Peromyscus polionotus), but they have different color patterns and which reside in different environments. The beach mouse lives along the Florida seashore, a habitat of brilliant white sand dunes with sparse clumps of beach grass.

The inland mouse lives on darker, more fertile soil farther inland (Figure below). Even a brief glance at the photographs in following Figure reveals a striking match of mouse coloration to its habitat. The natural predators of these mice, including hawks, owls, foxes, and coyotes, are all visual hunters (they use their eyes to look for prey). It was logical, therefore, for Francis Bertody Sumner, a naturalist studying observation populations of these mice in the 1920s, to form the hypothesis that their coloration patterns had evolved as adaptations that camouflage the mice in their native environments, protecting them from predation.

Figure Different coloration in beach and inland populations of Peromyscus polionotus.

As obvious as the camouflage theory may seem, it still required testing. In 2010, biologist Hopi Hoekstra of Harvard University and a group of her students headed to Florida to test the prediction that mice with coloration that did not match their habitat would be preyed on more heavily than the native, well matched mice. Figure below summarizes this following field experiment.

Figure: Inquiry Does camouflage affect predation rates on two populations of mice? Experiment Hopi Hoekstra and colleagues tested the hypothesis that coat coloration provides camouflage that protects beach and inland populations of Peromyscus polionotus mice from predation in their habitats. The researchers spray-painted mouse models with light or dark color patterns that matched those of the beach and inland mice and placed models with each of the patterns in both habitats. The next morning, they counted damaged or missing models. Results For each habitat, the researchers calculated the percentage of attacked models that were camouflaged or non-camouflaged. In both habitats, the models whose pattern did not match their surroundings suffered much higher “predation” than did the camouflaged models. Conclusion The results are consistent with the researchers’ prediction: that mouse models with camouflage coloration would be preyed on less often than non-camouflaged mouse models. Thus, the experiment supports the camouflage hypothesis. Data from S. N. Vignieri, J. G. Larson, and H. E. Hoekstra, The selective advantage of crypsis in mice, Evolution 64:2153–2158 (2010). INTERPRET THE DATA The bars indicate the percentage of the attacked models that were either light or dark. Assume 100 mouse models were attacked in each habitat. For the beach habitat, how many were light models? Dark models? Answer the same questions for the inland habitat.

The researchers built hundreds of models of mice and spraypainted them to resemble either beach or inland mice, so that the models differed only in their color patterns. The researchers placed equal numbers of these model mice randomly in both habitats and left them overnight. The mouse models resembling the native mice in the habitat were the control group (for instance, light colored mouse models in the beach habitat), while the mouse models with the non-native coloration were the experimental group (for example, darker models in the beach habitat). The following morning, the team counted and recorded signs of predation events, which ranged from bites and gouge marks on some models to the outright disappearance of others. Judging by the shape of the predators’ bites and the tracks surrounding the experimental sites, the predators appeared to be split fairly evenly between mammals (such as foxes and coyotes) and birds (such as owls, herons, and hawks).

For each environment, the researchers then calculated the percentage of predation events that targeted camouflaged models. The results were clear cut: Camouflaged models showed much lower predation rates than those lacking camouflage in both the beach habitat (where light mice were less vulnerable) and the inland habitat (where dark mice were less vulnerable). The data thus fit the key prediction of the camouflage hypothesis.

Experimental Variables and Controls

In carrying out an experiment, a researcher often manipulates one factor in a system and observes the effects of this change. The mouse camouflage experiment described in Figure at top is an example of a following controlled experiment, one that is designed to compare an experimental group (the noncamouflaged mice models, in this case) with a control group (the camouflaged models). Both the factor that is manipulated and the factor that is subsequently measured are types of experimental variables a feature or quantity that varies in an experiment. In our example, the color of the mouse model was the independent variable the factor being manipulated by the researchers. The dependent variable is the factor which being measured that is predicted to be affected by the independent variable; in this case, the researchers measured the predation rate in response to variation in color of the mouse model. Ideally, the experimental and control groups differ in only one independent variable in the mouse experiment, color.

Without the following control group, the researchers would not have been able to rule out other factors as causes of the more frequent attacks on the non-camouflaged mice such as different numbers of predators or different temperatures in the different test areas. The clever experimental models design left coloration as the only factor that could account for the low predation rate on models camouflaged with respect to the surrounding environment.

A common misconception is that the term controlled experiment means that scientists control all features of the experimental environment. But that’s impossible in field research and can be very difficult even in highly regulated laboratory environments. Researchers usually “control” unwanted variables not by eliminating them through environmental regulation, but by canceling out their effects by using control groups.

Theories in Science

“It’s just a theory!” Our everyday use of the term theory often implies an untested speculation. But the term theory has a different meaning in science. What is a scientific theory, and how is it different from a hypothesis or from mere speculation?

First, a scientific theory is much broader in scope than a hypothesis. This is a hypothesis: “Coat coloration wellmatched to their habitat is an adaptation that protects mice from predators.” But this is a theory: “Evolutionary adaptations arise by natural selection.” This theory proposes that natural selection is the evolutionary mechanism that accounts for an enormous variety of adaptations, of which coat color in mice is but one example.

Second, a theory is general enough to spin off many new, testable hypotheses. For example, the theory of natural selection motivated two researchers at Princeton of University, Peter and Rosemary Grant, to test the specific hypothesis that the beaks of Galápagos finches evolve in related response to changes in the types of available food.

And third, compared to any one hypothesis, a theory is generally supported by a much greater body of evidence. The theory of natural selection has been supported by a vast quantity of evidence, with more being found every day, and has not been contradicted by any scientific data. Those theories that become widely adopted in science (such as the theory of natural selection and the theory of gravity) explain a great diversity of observations and are supported by a vast accumulation of evidence.

In spite of the body of evidence supporting a widely accepted theory, scientists will sometimes modify or even reject theories when new research produces results that don’t fit. For example, biologists once lumped bacteria and archaea together related as a kingdom of prokaryotes. When new methods for comparing cells and molecules could be used to test such relationships, the evidence led scientists to reject the theory that bacteria and archaea are members of the same kingdom. If there is “truth” in science, it is at best conditional, based on the weight of available evidence.