2: The Process of Science
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)Like other natural sciences, environmental science gathers knowledge about the natural world. Science is more than just a body of knowledge, science provides a means to evaluate and create new knowledge. The discoveries of environmental science are made by a community of researchers who work individually and together using agreed-on methods. In this sense, science is a social enterprise like politics or the arts.
The methods of science include careful observation, record keeping, logical and mathematical reasoning, experimentation, and submitting conclusions to the scrutiny of others. Science also requires considerable imagination and creativity; a well-designed experiment is commonly described as elegant, or beautiful. Science has considerable practical implications and some science is dedicated to practical applications, such as the prevention of disease (Figure \(\PageIndex{2}\)). Other science proceeds largely motivated by curiosity. Whatever its goal, there is no doubt that science, including biology, has transformed human existence and will continue to do so.
Science is a very specific way of learning, or knowing, about the world. The history of the past 500 years demonstrates that science is a very powerful way of knowing about the world; it is largely responsible for the technological revolutions that have taken place during this time. There are however, areas of knowledge and human experience that the methods of science cannot be applied to. These include such things as answering purely moral questions, aesthetic questions, or what can be generally categorized as spiritual questions. Science has cannot investigate these areas because they are outside the realm of material phenomena, the phenomena of matter and energy, and cannot be observed and measured.
The scientific method is a method of research with defined steps that include experiments and careful observation. The steps of the scientific method will be examined in detail later, but one of the most important aspects of this method is the testing of hypotheses. A hypothesis is a suggested explanation for an event, which can be tested. Hypotheses, or tentative explanations, are generally produced within the context of a scientific theory. A scientific theory is a generally accepted, thoroughly tested and confirmed explanation for a set of observations or phenomena. Scientific theory is the foundation of scientific knowledge. In addition, in many scientific disciplines (less so in biology) there are scientific laws, often expressed in mathematical formulas, which describe how elements of nature will behave under certain specific conditions. There is not an evolution of hypotheses through theories to laws as if they represented some increase in certainty about the world. Hypotheses are the day-to-day material that scientists work with and they are developed within the context of theories. Laws are concise descriptions of parts of the world that are amenable to formulaic or mathematical description.
Natural Sciences
What would you expect to see in a museum of natural sciences? Frogs? Plants? Dinosaur skeletons? Exhibits about how the brain functions? A planetarium? Gems and minerals? Or maybe all of the above? Science includes such diverse fields as astronomy, biology, computer sciences, geology, logic, physics, chemistry, and mathematics (Figure \(\PageIndex{3}\)). However, those fields of science related to the physical world and its phenomena and processes are considered natural sciences. Thus, a museum of natural sciences might contain any of the items listed above.
There is no complete agreement when it comes to defining what the natural sciences include. For some experts, the natural sciences are astronomy, biology, chemistry, earth science, and physics. Other scholars choose to divide natural sciences into life sciences, which study living things and include biology, and physical sciences, which study nonliving matter and include astronomy, physics, and chemistry. Some disciplines such as biophysics and biochemistry build on two sciences and are interdisciplinary.
Evidence, Measurements, and Observations
Scientists use objective evidence over subjective evidence, to reach sound and logical conclusions. An objective observation is without personal bias and the same by all individuals. Bias refers to favoring one thing over another, and it can lead to inaccurate results. Humans are biased by nature, so they cannot be completely objective; the goal is to be as unbiased as possible. A subjective observation is based on a person’s feelings and beliefs and is unique to that individual (figure \(\PageIndex{4}\)).
Figure \(\PageIndex{4}\): This is Grand Canyon of the Yellowstone in Yellowstone National Park. An objective statement about this would be, “The picture is of a waterfall.” A subjective statement would be, “The picture is beautiful.”
Another way scientists avoid bias is by using quantitative over qualitative measurements whenever possible. A quantitative measurement is expressed with a specific numerical value. Qualitative observations are general or relative descriptions. For example, describing a rock as red or heavy is a qualitative observation. Determining a rock’s color by measuring wavelengths of reflected light or its density by measuring the proportions of minerals it contains is quantitative. Numerical values are more precise than general descriptions, and they can be analyzed using statistical calculations. This is why quantitative measurements are much more useful to scientists than qualitative observations.
Inductive and Deductive Reasoning
One thing is common to all forms of science: an ultimate goal “to know.” Curiosity and inquiry are the driving forces for the development of science. Scientists seek to understand the world and the way it operates. Two methods of logical thinking are used: inductive reasoning and deductive reasoning.
Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. This type of reasoning is common in descriptive science. A life scientist such as a biologist makes observations and records them. These data can be qualitative (descriptive) or quantitative (consisting of numbers), and the raw data can be supplemented with drawings, pictures, photos, or videos. From many observations, the scientist can infer conclusions (inductions) based on evidence. Inductive reasoning involves formulating generalizations inferred from careful observation and the analysis of a large amount of data. Surveying land use (which areas are forested, agricultural, urban, etc.) across the United States and then concluding that forested areas are concentrated in the West is an example of descriptive science.
Deductive reasoning or deduction is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning. Deductive reasoning is a form of logical thinking that uses a general principle or law to forecast specific results. From those general principles, a scientist can extrapolate and predict the specific results that would be valid as long as the general principles are valid. For example, a prediction would be that if the climate is becoming warmer in a region, the distribution of plants and animals should change. Comparisons have been made between distributions in the past and the present, and the many changes that have been found are consistent with a warming climate. Finding the change in distribution is evidence that the climate change conclusion is a valid one. Deductive reasoning, or deduction, is the type of logic used in hypothesis-based science (see below).
To summarize, inductive reasoning moves from the specific (an observation) to general (conclusion), and deductive reasoning moves from the general (a hypothesis or principle) to the specific (results).
Both types of logical thinking are related to the two main pathways of scientific study: descriptive science and hypothesis-based science. Descriptive (or discovery) science aims to observe, explore, and discover, while hypothesis-based science begins with a specific question or problem and a potential answer or solution that can be tested. The boundary between these two forms of study is often blurred, because most scientific endeavors combine both approaches. Observations lead to questions, questions lead to forming a hypothesis as a possible answer to those questions, and then the hypothesis is tested. Thus, descriptive science and hypothesis-based science are in continuous dialogue.
Hypothesis Testing and The scientific Method
The scientific method is a process of research with defined steps that include data collection and careful observation. The scientific method was used even in ancient times, but it was first documented by England’s Sir Francis Bacon (1561–1626) (Figure \(\PageIndex{5}\)), who set up inductive methods for scientific inquiry.
Observation
Scientific advances begin with observations. This involves noticing a pattern, either directly or indirectly from the literature. An example of a direct observation is noticing that there have been a lot of toads in your yard ever since you turned on the sprinklers, where as an indirect observation would be reading a scientific study reporting high densities of toads in urban areas with watered lawns.
During the Vietnam War (figure \(\PageIndex{6}\)), press reports from North Vietnam documented an increasing rate of birth defects. While this credibility of this information was initially questioned by the U.S., it evoked questions about what could be causing these birth defects. Furthermore, increased incidence of certain cancers and other diseases later emerged in Vietnam veterans who had returned to the U.S. This leads us to the next step of the scientific method, the question.
Figure \(\PageIndex{6}\): A map of Vietnam 1954-1975. Image from Bureau of Public Affairs U.S. Government Printing Office (public domain).
Question
The question step of the scientific method is simply asking, what explains the observed pattern? Multiple questions can stem from a single observation. Scientists and the public began to ask, what is causing the birth defects in Vietnam and diseases in Vietnam veterans? Could it be associated with the widespread military use of the herbicide Agent Orange to clear the forests (figure \(\PageIndex{7-8}\)), which helped identify enemies more easily?
Figure \(\PageIndex{7}\): Agent Orange drums in Vietnam. Image by U.S. Government (public domain).
Figure \(\PageIndex{8}\): A healthy mangrove forest (top), and another forest after application of Agent Orange. Image by unknown author (public domain).
Hypothesis and Prediction
The hypothesis is the expected answer to the question. The best hypotheses state the proposed direction of the effect (increases, decreases, etc.) and explain why the hypothesis could be true.
- OK hypothesis: Agent Orange influences rates of birth defects and disease.
- Better hypothesis: Agent Orange increases the incidence of birth defects and disease.
- Best hypothesis: Agent Orange increases the incidence of birth defects and disease because these health problems have been frequently reported by individuals exposed to this herbicide.
If two or more hypotheses meet this standard, the simpler one is preferred.
Predictions stem from the hypothesis. The prediction explains what results would support hypothesis. The prediction is more specific than the hypothesis because it references the details of the experiment. For example, "If Agent Orange causes health problems, then mice experimentally exposed to TCDD, a contaminant of Agent Orange, during development will have more frequent birth defects than control mice" (figure \(\PageIndex{9}\)).
Figure \(\PageIndex{9}\): The chemical structure of TCDD (2,3,7,8-tetrachlorodibenzo-p-dioxin), which is produced when synthesizing the chemicals in Agent Orange. It contaminates Agent Orange at low but harmful concentrations. Image by Emeldir (public domain).
Hypotheses and predictions must be testable to ensure that it is valid. For example, a hypothesis that depends on what a bear thinks is not testable, because it can never be known what a bear thinks. It should also be falsifiable, meaning that they have the capacity to be tested and demonstrated to be untrue. An example of an unfalsifiable hypothesis is “Botticelli’s Birth of Venus is beautiful.” There is no experiment that might show this statement to be false. To test a hypothesis, a researcher will conduct one or more experiments designed to eliminate one or more of the hypotheses. This is important. A hypothesis can be disproven, or eliminated, but it can never be proven. Science does not deal in proofs like mathematics. If an experiment fails to disprove a hypothesis, then we find support for that explanation, but this is not to say that down the road a better explanation will not be found, or a more carefully designed experiment will be found to falsify the hypothesis.
Hypotheses are tentative explanations and are different from scientific theories. A scientific theory is a widely-accepted, thoroughly tested and confirmed explanation for a set of observations or phenomena. Scientific theory is the foundation of scientific knowledge. In addition, in many scientific disciplines (less so in biology) there are scientific laws, often expressed in mathematical formulas, which describe how elements of nature will behave under certain specific conditions, but they do not offer explanations for why they occur.
Design an Experiment
Next, a scientific study (experiment) is planned to test the hypothesis and determine whether the results match the predictions. Each experiment will have one or more variables. The explanatory variable is what scientists hypothesize might be causing something else. In a manipulative experiment (see below), the explanatory variable is manipulated by the scientist. The response variable is the response, the variable ultimately measured in the study. Controlled variables (confounding factors) might affect the response variable, but they are not the focus of the study. Scientist attempt to standardize the controlled variables so that they do not influence the results. In our previous example, exposure to Agent Orange is the explanatory variable. It is hypothesized to cause a change in health (likelihood of having children with birth defects or developing a disease), the response variable. Many other things could affect health, including diet, exercise, and family history. These are the controlled variables.
There are two main types of scientific studies: experimental studies (manipulative experiments) and observational studies.
In a manipulative experiment, the explanatory variable is altered by the scientists, who then observe the response. In other words, the scientists apply a treatment. An example would be exposing developing mice to TCDD and comparing the rate of birth defects to a control group. The control group is group of test subjects that are as similar as possible to all other test subjects, with the exception that they don’t receive the experimental treatment (those that do receive it are known as the experimental, treatment, or test group). The purpose of the control group is to establish what the dependent variable would be under normal conditions, in the absence of the experimental treatment. It serves as a baseline to which the test group can be compared. In this example, the control group would contain mice that were not exposed to TCDD but were otherwise handled the same way as the other mice (figure \(\PageIndex{10}\))
Figure \(\PageIndex{10}\): Laboratory mice. In a proper scientific study, the treatment would be applied to multiple mice. Another group of mice would not receive the treatment (the control group). Image by Aaron Logan (CC-BY).
In an observational study, scientists examine multiple samples with and without the presumed cause. An example would be monitoring the health of veterans who had varying levels of exposure to Agent Orange.
Scientific studies contain many replicates. Multiple samples ensure that any observed pattern is due to the treatment rather than naturally occurring differences between individuals. A scientific study should also be repeatable, meaning that if it is conducted again, following the same procedure, it should reproduce the same general results. Additionally, multiple studies will ultimately test the same hypothesis.
Results
Finally, the data are collected and the results are analyzed. As described in the Math Blast chapter, statistics can be used to describe the data and summarize data. They also provide a criterion for deciding whether the pattern in the data is strong enough to support the hypothesis.
The manipulative experiment in our example found that mice exposed to high levels of 2,4,5-T (a component of Agent Orange) or TCDD (a contaminant found in Agent Orange) during development had a cleft palate birth defect more frequently than control mice (figure \(\PageIndex{11}\)). Mice embryos were also more likely to die when exposed to TCDD compared to controls.
Figure \(\PageIndex{11}\): Cleft lip and palate, a birth defect in which these structures are split. Image by James Heilman, MD (CC-BY-SA).
An observational study found that self-reported exposure to Agent Orange was positively correlated with incidence of multiple diseases in Korean veterans of the Vietnam War, including various cancers, diseases of the cardiovascular and nervous systems, skin diseases, and psychological disorders. Note that a positive correlation simply means that the independent and dependent variables both increase or decrease together, but further data, such as the evidence provided by manipulative experiments is needed to document a cause-and-effect relationship. (A negative correlation occurs when one variable increases as the other decreases.)
Conclusion
Lastly, scientists make a conclusion regarding whether the data support the hypothesis. In the case of Agent Orange, the data, that mice exposed to TCDD and 2,4,5-T had higher frequencies of cleft palate, matches the prediction. Additionally, veterans exposed to Agent Orange had higher rates of certain diseases, further supporting the hypothesis. We can thus accept the hypothesis that Agent Orange increases the incidence of birth defects and disease.
Scientific Method in Practice
In practice, the scientific method is not as rigid and structured as it might first appear. Sometimes an experiment leads to conclusions that favor a change in approach; often, an experiment brings entirely new scientific questions to the puzzle. Many times, science does not operate in a linear fashion; instead, scientists continually draw inferences and make generalizations, finding patterns as their research proceeds (figure \(\PageIndex{12}\)). Even if the hypothesis was supported, scientists may still continue to test it in different ways. For example, scientists explore the impacts of Agent Orange, examining long-term health impacts as Vietnam veterans age.
Scientific findings can influence decision making. In response to evidence regarding the effect of Agent Orange on human health, compensation is now available for Vietnam veterans who were exposed to Agent Orange and develop certain diseases. The use of Agent Orange is also banned in the U.S. Finally, the U.S. has began cleaning sites in Vietnam that are still contaminated with TCDD.
As another simple example, an experiment might be conducted to test the hypothesis that phosphate limits the growth of algae in freshwater ponds. A series of artificial ponds are filled with water and half of them are treated by adding phosphate each week, while the other half are treated by adding a salt that is known not to be used by algae. The variable here is the phosphate (or lack of phosphate), the experimental or treatment cases are the ponds with added phosphate and the control ponds are those with something inert added, such as the salt. Just adding something is also a control against the possibility that adding extra matter to the pond has an effect. If the treated ponds show lesser growth of algae, then we have found support for our hypothesis. If they do not, then we reject our hypothesis. Be aware that rejecting one hypothesis does not determine whether or not the other hypotheses can be accepted; it simply eliminates one hypothesis that is not valid (Figure \(\PageIndex{12}\)). Using the scientific method, the hypotheses that are inconsistent with experimental data are rejected.
Science is also a Social Process
Scientists must share their findings for other researchers to expand and build upon their discoveries. Communication and collaboration within and between sub disciplines of science are key to the advancement of knowledge in science. For this reason, an important aspect of a scientist’s work is disseminating results and communicating with peers. Scientists share their ideas with peers at conferences, seeking guidance and feedback (figure \(\PageIndex{13}\)), but this approach can reach only the limited few who are present.
Figure \(\PageIndex{13}\): Scientists share information by publishing and attending conferences. The conference shown here focuses on peanut and mycotoxin (harmful chemicals produced by fungi) research. Image by Sharon Dowdy (CC-BY-NC).
Peer Review
Most scientists present their results in peer-reviewed articles that are published in scientific journals (figure \(\PageIndex{14}\)). Peer-reviewed articles are scientific papers that are reviewed, usually anonymously by a scientist’s colleagues, or peers. These colleagues are qualified individuals, often experts in the same research area, who judge whether or not the scientist’s work is suitable for publication. The process of peer review helps to ensure that the research described in a scientific paper or grant proposal is original, significant, logical, and thorough. Grant proposals, which are requests for research funding, are also subject to peer review. Scientists publish their work so other scientists can reproduce their experiments under similar or different conditions to expand on the findings. The experimental results must be consistent with the findings of other scientists.
Figure \(\PageIndex{14}\): The findings of scientific studies are published in peer-reviewed scientific journals. Image by free svg (public domain).
There are many journals and the popular press that do not use a peer-review system. A large number of online open-access journals, journals with articles available without cost, are now available many of which use rigorous peer-review systems, but some of which do not. Results of any studies published in these forums without peer review are not reliable and should not form the basis for other scientific work. In one exception, journals may allow a researcher to cite a personal communication from another researcher about unpublished results with the cited author’s permission.
The scientific review process aims to weed out misinformation, invalid research results, and wild speculation. Thus, it is slow, cautious, and conservative. Scientists tend to wait until a hypothesis is supported by an overwhelming amount of evidence from many independent researchers before accepting it as a scientific theory.
As you review scientific information, whether in an academic setting or as part of your day-to-day life, it is important to think about the credibility of that information. You might ask yourself: has this scientific information been through the rigorous process of peer review? Are the conclusions based on available data and accepted by the larger scientific community? Scientists are inherently skeptical, especially if conclusions are not supported by evidence (and you should be too).
Building on the Work of Others
Only rarely does a scientific discovery spring full-blown on the scene. When it does, it is likely to create a revolution in the way scientists perceive the world around them and to open up new areas of scientific investigation. Darwin's theory of evolution and Mendel's rules of inheritance are examples of such revolutionary developments. Most science, however, consists of adding another brick to an edifice that has been slowly and painstakingly constructed by prior work.
The development of a new technique often lays the foundation for rapid advances along many different scientific avenues. Just consider the advances in biology that discovery of the light microscope and, later, the electron microscope have made possible. Throughout these pages, there are many examples of experimental procedures. Each was developed to solve a particular problem. However, each was then taken up by workers in other laboratories and applied to their problems.
In a similar way, the creation of a new explanation (hypothesis) in a scientific field often stimulates workers in related fields to reexamine their own field in the light of the new ideas. Darwin's theory of evolution, for example, has had an enormous impact on virtually every subspecialty in biology as well as environmental science. To this very day, scientists in specialties as different as biochemistry and conservation biology are guided in their work by evolutionary theory (figure \(\PageIndex{15}\)).
Figure \(\PageIndex{15}\): Understanding the endangered mountain yellow-legged frog through the lens of evolution and genetics has aided its conservation. Image by Iasaac Chellman/NPS (CC-BY).
Structure of Scientific Papers
Scientific papers are usually divided into several sections (not necessarily in this order).
Summary or Abstract: This section includes only the essence of the other sections. It should be as brief as possible, telling the reader what the goal of the experiment was, what was found, and the significance of the findings. The abstract is often placed at the beginning of the paper rather than at its end.
Introduction: This section of the paper describes the scientific question or problem that was the subject of the investigation. The introduction also includes references to earlier reports of these and other scientists that have served as the foundation for the present work. Finally, the introduction states the hypothesis.
Materials and Methods: Here are precisely described the materials used (e.g., strains of organism, source of the reagents) and all the methods followed. The goal of this section is to give all the details necessary for workers in other laboratories to be able to repeat the experiments exactly. When many complex procedures are involved, it is acceptable to refer to earlier papers describing these methods in greater detail.
Results: Here the authors report what happened in their experiments. This report is usually supplemented with graphs, tables, and photographs.
Discussion: Here the authors point out what they think is the significance of their findings. This is the place to show that the results are compatible with certain hypotheses and less compatible, or even incompatible, with others. If the results contradict the results of similar experiments in other laboratories, the discrepancies are noted here, and an attempt may be made to reconcile the differences.
Acknowledgments: In this brief but important section, the authors give credit to those who have assisted them in the work. These usually include technicians (who may have actually performed most of the experiments!) and other scientists who donated materials for the experiments and/or gave advice about them.
References: This section gives a careful listing of all earlier scientific work referred to in the main body of the paper. Most of the references are to other scientific papers. Each reference should provide enough information so that another person can locate the document. This means that each reference should include the name(s) of the author(s), the journal or book in which the report appears, and the year of publication. In the case of scientific journals, the volume number in which the paper appears and the page number on which the paper begins should be included. A digital object identifier (DOI) or url is often included. The full title of the article is often included as well, although some citation styles omit the title from the reference.
Basic and Applied Science
The scientific community has been debating for the last few decades about the value of different types of science. Is it valuable to pursue science for the sake of simply gaining knowledge, or does scientific knowledge only have worth if we can apply it to solving a specific problem or bettering our lives? This question focuses on the differences between two types of science: basic science and applied science.
Basic science or “pure” science seeks to expand knowledge regardless of the short-term application of that knowledge. It is not focused on developing a product or a service of immediate public or commercial value. The immediate goal of basic science is knowledge for knowledge’s sake, though this does not mean that in the end it may not result in an application.
In contrast, applied science or “technology,” aims to use science to solve real-world problems, making it possible, for example, to improve a crop yield, find a cure for a particular disease, or save animals threatened by a natural disaster. In applied science, the problem is usually defined for the researcher.
Some individuals may perceive applied science as “useful” and basic science as “useless.” A question these people might pose to a scientist advocating knowledge acquisition would be, “What for?” A careful look at the history of science, however, reveals that basic knowledge has resulted in many remarkable applications of great value. Many scientists think that a basic understanding of science is necessary before an application is developed; therefore, applied science relies on the results generated through basic science. Other scientists think that it is time to move on from basic science and instead to find solutions to actual problems. Both approaches are valid. It is true that there are problems that demand immediate attention; however, few solutions would be found without the help of the knowledge generated through basic science.
One example of how basic and applied science can work together to solve practical problems occurred after the discovery of DNA structure led to an understanding of the molecular mechanisms governing DNA replication. Strands of DNA, unique in every human, are found in our cells, where they provide the instructions necessary for life. During DNA replication, new copies of DNA are made, shortly before a cell divides to form new cells. Understanding the mechanisms of DNA replication enabled scientists to develop laboratory techniques that are now used to identify genetic diseases, pinpoint individuals who were at a crime scene, and determine paternity. Without basic science, it is unlikely that applied science would exist.
Another example of the link between basic and applied research is the Human Genome Project, a study in which each human chromosome was analyzed and mapped to determine the precise sequence of DNA subunits and the exact location of each gene. (The gene is the basic unit of heredity; an individual’s complete collection of genes is his or her genome.) Other organisms have also been studied as part of this project to gain a better understanding of human chromosomes. The Human Genome Project (Figure \(\PageIndex{16}\)) relied on basic research carried out with non-human organisms and, later, with the human genome. An important end goal eventually became using the data for applied research seeking cures for genetically related diseases.
While research efforts in both basic science and applied science are usually carefully planned, it is important to note that some discoveries are made by serendipity, that is, by means of a fortunate accident or a lucky surprise. Penicillin was discovered when biologist Alexander Fleming accidentally left a petri dish of Staphylococcus bacteria open. An unwanted mold grew, killing the bacteria. The mold turned out to be Penicillium, and a new antibiotic was discovered. Even in the highly organized world of science, luck—when combined with an observant, curious mind—can lead to unexpected breakthroughs.
Predictability
A few phenomena are considered to have a universal character and are consistent wherever and whenever they are accurately measured. One of the best examples of such a universal constant is the speed of light, which always has a value of 2.998 × 108 meters per second, regardless of where it is measured or of the speed of the body from which the light is emitted. Similarly, certain relationships describing transformations of energy and matter, known as the laws of thermodynamics, always give reliable predictions.
However, most natural phenomena are not so consistent—depending on circumstances, there are exceptions to general predictions about them. This circumstance is particularly true of biology and ecology, related fields of science in which almost all general predictions have exceptions. In fact, laws or unifying principles of biology or ecology have not yet been discovered, in contrast to the several esteemed laws and 11 universal constants of physics. For this reason, biologists and ecologists have great difficulties making accurate predictions about the responses of organisms and ecosystems to environmental change. This is why biologists and ecologists are sometimes said to have “physics envy.”
In large part, the inaccuracies of biology and ecology occur because key functions are controlled by complexes of poorly understood, and sometimes unidentified, environmental influences. Consequently, predictions about future values of biological and ecological variables or the causes of changes are seldom accurate. For example, even though ecologists in eastern Canada have been monitoring the population size of spruce budworm (an important pest of conifer forests) for some years, they cannot accurately predict its future abundance in particular stands of forest or in larger regions. This is because the abundance of this moth is influenced by a complex of environmental factors, including tree-species composition, age of the forest, abundance of its predators and parasites, quantities of its preferred foods, weather at critical times of year, and insecticide use to reduce its populations. Biologists and ecologists do not fully understand this complexity, and perhaps they never will.
Variability
Many natural phenomena are highly variable in space and time. This is true of physical and chemical variables as well as of biological and ecological ones. Within a forest, for example, the amount of sunlight reaching the ground varies greatly with time, depending on the hour of the day and the season of the year. It also varies spatially, depending on the density of foliage over any place where sunlight is being measured. Similarly, the density of a particular species of fish within a river typically varies in response to changes in habitat conditions and other influences. Most fish populations also vary over time, especially migratory species such as salmon. In environmental science, replicated (or independently repeated) measurements and statistical analyses are used to measure and account for these kinds of temporal and spatial variations.
Accuracy and Precision
Accuracy refers to the degree to which a measurement or observation reflects the actual, or true, value of the subject. For example, the insecticide DDT and the metal mercury are potentially toxic chemicals that occur in trace concentrations in all organisms, but their small residues are difficult to analyze chemically. Some of the analytical methods used to determine the concentrations of DDT and mercury are more accurate than others and therefore provide relatively useful and reliable data compared with less accurate methods. In fact, analytical data are usually approximations of the real values – rigorous accuracy is rarely attainable.
Precision is related to the degree of repeatability of a measurement or observation. For example, suppose that the actual number of caribou in a migrating herd is 10,246 animals. A wildlife ecologist might estimate that there were about 10,000 animals in that herd, which for practical purposes is a reasonably accurate reckoning of the actual number of caribou. If other ecologists also independently estimate the size of the herd at about 10,000 caribou, there is a good degree of precision among the values. If, however, some systematic bias existed in the methodology used to count the herd, giving consistent estimates of 15,000 animals (remember, the actual population is 10 246 caribou), these estimates would be considered precise, but not particularly accurate.
Precision is also related to the number of digits with which data are reported. If you were using a flexible tape to measure the lengths of 10 large, wriggly snakes, you would probably measure the reptiles only to the nearest centimetre. The strength and squirminess of the animals make more precise measurements impossible. The reported average length of the 10 snakes should reflect the original measurements and might be given as 204 cm and not a value such as 203.8759 cm. The latter number might be displayed as a digital average by a calculator or computer, but it is unrealistically precise.
Significant figures are related to accuracy and precision and can be defined as the number of digits used to report data from analyses or calculations (see also Appendix A). Significant figures are most easily understood by examples. The number 179 has three significant figures, as does the number 0.0849 and also 0.000794 (the zeros preceding the significant integers do not count). However, the number 195,000,000 has nine significant figures (the zeros following are meaningful), although the number 195 × 106 has only three significant figures.
It is rarely useful to report environmental or ecological data to more than 2-4 significant figures. This is because any more would generally exceed the accuracy and precision of the methodology used in the estimation and would therefore be unrealistic. For example, the approximate population of Canada in 2015 was 35.1 million people (or 35.1 × 106; both of these notations have three significant figures). However, the population should not be reported as 33,100,000, which implies an unrealistic accuracy and precision of eight significant figures.
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Contributors and Attributions
- Modified by Kyle Whittinghill (University of Pittsburgh)
Samantha Fowler (Clayton State University), Rebecca Roush (Sandhills Community College), James Wise (Hampton University). Original content by OpenStax (CC BY 4.0; Access for free at https://cnx.org/contents/b3c1e1d2-83...4-e119a8aafbdd).
- Modified by Melissa Ha
- 1.2: The Process of Science by OpenStax, is licensed CC BY
- What is Science? from An Introduction to Geology by Chris Johnson et al. (licensed under CC-BY-NC-SA)
- The Process of Science from Environmental Biology by Matthew R. Fisher (licensed under CC-BY)
- Scientific Methods from Biology by John W. Kimball (licensed under CC-BY)
- Scientific Papers from Biology by John W. Kimball (CC-BY)
- Environmental Science: A Canadian perspective by Bill Freedman Chapter 2: Science as a Way of Understanding the Natural World