a hypothesis is quizlet environmental science

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

The Process of Science

Matthew R. Fisher and Editor

Like other natural sciences, environmental science is a science that gathers knowledge about the natural world. 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 2). Other science proceeds largely motivated by curiosity. Whatever its goal, there is no doubt that science has transformed human existence and will continue to do so.

The Nature of Science

Biology is a science, but what exactly is science? What does the study of biology share with other scientific disciplines?  Science  (from the Latin scientia,  meaning “knowledge”) can be defined as a process of gaining knowledge about the natural world.

Science is a very specific way of learning about the world. The history of the past 500 years demonstrates that science is a very powerful way of gaining knowledge about the world; it is largely responsible for the technological revolutions that have taken place during this time. There are areas of knowledge, however, that the methods of science cannot be applied to. These include such things as morality, aesthetics, or spirituality. Science 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 an proposed explanatory statement, for a given natural phenomenon, that can be tested. Hypotheses, or tentative explanations, are different than a  scientific theory . 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.

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, computer sciences, psychology,biology, and mathematics. However, those fields of science related to the physical world and its phenomena and processes are considered  natural sciences and include the disciplines of physics, geology, biology, and chemistry. Environmental science is a cross-disciplinary natural science because it relies of the disciplines of chemistry, biology, and geology.

Scientific Inquiry

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. Brain studies often work this way. Many brains are observed while people are doing a task. The part of the brain that lights up, indicating activity, is then demonstrated to be the part controlling the response to that task.

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.

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.

“Scientists have become the bearers of the torch of discovery in our quest for knowledge.”  – Stephen Hawking and Leonard Mlodinov, in The Grand Design (2010), Bantam Books

Hypothesis Testing

Biologists study the living world by posing questions about it and seeking science-based responses. This approach is common to other sciences as well and is often referred to as the scientific method. The scientific method was used even in ancient times, but it was first documented by England’s Sir Francis Bacon (1561–1626) who set up inductive methods for scientific inquiry. The scientific method is not exclusively used by biologists but can be applied to almost anything as a logical problem-solving method.

The scientific process typically starts with an observation (often a problem to be solved) that leads to a question. Let’s think about a simple problem that starts with an observation and apply the scientific method to solve the problem. One Monday morning, a student arrives at class and quickly discovers that the classroom is too warm. That is an observation that also describes a problem: the classroom is too warm. The student then asks a question: “Why is the classroom so warm?”

Recall that a hypothesis is a suggested explanation that can be tested. To solve a problem, several hypotheses may be proposed. For example, one hypothesis might be, “The classroom is warm because no one turned on the air conditioning.” But there could be other responses to the question, and therefore other hypotheses may be proposed. A second hypothesis might be, “The classroom is warm because there is a power failure, and so the air conditioning doesn’t work.”

Once a hypothesis has been selected, a prediction may be made. A prediction is similar to a hypothesis but it typically has the format “If . . . then . . . .” For example, the prediction for the first hypothesis might be, “ If  the student turns on the air conditioning,  then  the classroom will no longer be too warm.”

A hypothesis 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 it can be disproven by experimental results. 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.

Each experiment will have one or more variables and one or more controls. Experimental   variables  are any part of the experiment that can vary or change during the experiment. Controlled variables are parts of the experiment that do not change. Lastly, experiments might have a control group : a 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 group ). For example, in a study testing a weight-loss drug, the control group would be test subjects that don’t receive the drug (but they might receive a placebo, such as sugar pill, instead). Look for these various things in the example that follows:

An experiment might be conducted to test the hypothesis that phosphate (a nutrient) promotes 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 non-nutritional mineral that is not used by algae. The experimental variable here is presence/absence of a nutrient (phosphate). One potential controlled variable would be the volume of water in each tank. The amount of water that algae have access to may influence the results, thus researchers want to control its influence on the results by making sure all test subjects get the same amount. The control group consists of the tanks that received a placebo (non-nutritional mineral) instead of the phosphate. If the ponds with phosphate show more algal growth, then we have found support for the 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 3). Using the scientific method, the hypotheses that are inconsistent with experimental data are rejected.

In the example below, the scientific method is used to solve an everyday problem. Which part in the example below is the hypothesis? Which is the prediction? Based on the results of the experiment, is the hypothesis supported? If it is not supported, propose some alternative hypotheses.

• My toaster doesn’t toast my bread.
• Why doesn’t my toaster work?
• There is something wrong with the electrical outlet.
• If something is wrong with the outlet, my coffeemaker also won’t work when plugged into it.
• I plug my coffeemaker into the outlet.
• My coffeemaker works.

In practice, the scientific method is not as rigid and structured as it might at 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. Scientific reasoning is more complex than the scientific method alone suggests.

Basic and Applied 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  aims to use science to solve real-world problems, such as improving 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 (through basic science) 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 (all examples of applied science). 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 the DNA code 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 5) 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 genetic diseases.

 Scientific Work is Transparent & Open to Critique

Whether scientific research is basic science or applied science, scientists must share their findings for other researchers to expand and build upon their discoveries. For this reason, an important aspect of a scientist’s work is disseminating results and communicating with peers. Scientists can share results by presenting them at a scientific meeting or conference, but this approach can reach only the limited few who are present. Instead, most scientists present their results in peer-reviewed articles that are published in scientific journals.  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, ethical, and thorough. 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.

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).

Suggested Supplementary Reading:

Sundermier, A. 2016. “ These 5 mind-melting thought experiments helped Albert Einstein come up with his most revolutionary scientific ideas .”  Business Insider . https://www.businessinsider.com/5-of-albert-einsteins-thought-experiments-that-revolutionized-science-2016-7

Attribution

Concepts of Biology by OpenStax is licensed under CC BY 3.0.   Modified from the original by Matthew R. Fisher.

The Process of Science Copyright © by Matthew R. Fisher and Editor is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.

Share This Book

• History & Society
• Science & Tech
• Biographies
• Animals & Nature
• Geography & Travel
• Arts & Culture
• Games & Quizzes
• On This Day
• One Good Fact
• New Articles
• Lifestyles & Social Issues
• Philosophy & Religion
• Politics, Law & Government
• World History
• Health & Medicine
• Browse Biographies
• Birds, Reptiles & Other Vertebrates
• Bugs, Mollusks & Other Invertebrates
• Environment
• Fossils & Geologic Time
• Entertainment & Pop Culture
• Sports & Recreation
• Visual Arts
• Demystified
• Image Galleries
• Infographics
• Top Questions
• Britannica Kids
• Saving Earth
• Space Next 50
• Student Center

• Where was science invented?
• When did science begin?

scientific hypothesis

Our editors will review what you’ve submitted and determine whether to revise the article.

• National Center for Biotechnology Information - PubMed Central - On the scope of scientific hypotheses
• LiveScience - What is a scientific hypothesis?
• The Royal Society - Open Science - On the scope of scientific hypotheses

scientific hypothesis , an idea that proposes a tentative explanation about a phenomenon or a narrow set of phenomena observed in the natural world. The two primary features of a scientific hypothesis are falsifiability and testability, which are reflected in an “If…then” statement summarizing the idea and in the ability to be supported or refuted through observation and experimentation. The notion of the scientific hypothesis as both falsifiable and testable was advanced in the mid-20th century by Austrian-born British philosopher Karl Popper .

The formulation and testing of a hypothesis is part of the scientific method , the approach scientists use when attempting to understand and test ideas about natural phenomena. The generation of a hypothesis frequently is described as a creative process and is based on existing scientific knowledge, intuition , or experience. Therefore, although scientific hypotheses commonly are described as educated guesses, they actually are more informed than a guess. In addition, scientists generally strive to develop simple hypotheses, since these are easier to test relative to hypotheses that involve many different variables and potential outcomes. Such complex hypotheses may be developed as scientific models ( see scientific modeling ).

Depending on the results of scientific evaluation, a hypothesis typically is either rejected as false or accepted as true. However, because a hypothesis inherently is falsifiable, even hypotheses supported by scientific evidence and accepted as true are susceptible to rejection later, when new evidence has become available. In some instances, rather than rejecting a hypothesis because it has been falsified by new evidence, scientists simply adapt the existing idea to accommodate the new information. In this sense a hypothesis is never incorrect but only incomplete.

The investigation of scientific hypotheses is an important component in the development of scientific theory . Hence, hypotheses differ fundamentally from theories; whereas the former is a specific tentative explanation and serves as the main tool by which scientists gather data, the latter is a broad general explanation that incorporates data from many different scientific investigations undertaken to explore hypotheses.

Countless hypotheses have been developed and tested throughout the history of science . Several examples include the idea that living organisms develop from nonliving matter, which formed the basis of spontaneous generation , a hypothesis that ultimately was disproved (first in 1668, with the experiments of Italian physician Francesco Redi , and later in 1859, with the experiments of French chemist and microbiologist Louis Pasteur ); the concept proposed in the late 19th century that microorganisms cause certain diseases (now known as germ theory ); and the notion that oceanic crust forms along submarine mountain zones and spreads laterally away from them ( seafloor spreading hypothesis ).

Want to create or adapt books like this? Learn more about how Pressbooks supports open publishing practices.

Chapter 2 ~ Science as a Way of Understanding the Natural World

Key concepts.

After completing this chapter, you will be able to

• Describe the nature of science and its usefulness in explaining the natural world.
• Distinguish among facts, hypotheses, and theories.
• Outline the methodology of science, including the importance of tests designed to disprove hypotheses.
• Discuss the importance of uncertainty in many scientific predictions, and the relevance of this to environmental controversies.

The Nature of Science

Science can be defined as the systematic examination of the structure and functioning of the natural world, including both its physical and biological attributes. Science is also a rapidly expanding body of knowledge, whose ultimate goal is to discover the simplest general principles that can explain the enormous complexity of nature. These principles can be used to gain insights about the of the natural world and to make predictions about future change.

Science is a relatively recent way of learning about natural phenomena, having largely replaced the influences of less objective methods and world views. The major alternatives to science are belief systems that are influential in all cultures, including those based on religion, morality, and aesthetics. These belief systems are primarily directed toward different ends than science, such as finding meaning that transcends mere existence, learning how people ought to behave, and understanding the value of artistic expression.

Modern science evolved from a way of learning called natural philosophy, which was developed by classical Greeks and was concerned with the rational investigation of existence, knowledge, and phenomena. Compared with modern science, however, studies in natural philosophy used unsophisticated technologies and methods and were not particularly quantitative, sometimes involving only the application of logic.

Modern science began with the systematic investigations of famous 16th- and 17th-century scientists, such as:

• Nicolaus Copernicus (1473-1543), a Polish astronomer who conceived the modern theory of the solar system
• William Gilbert (1544-1603), an Englishman who worked on magnetism
• Galileo Galilei (1564-1642), an Italian who conducted research on the physics of objects in motion, as well as astronomy
• William Harvey (1578-1657): an Englishman who described the circulation of the blood
• Isaac Newton (1642-1727): an Englishman who made important contributions to understanding gravity and the nature of light, formulated laws of motion, and developed the mathematics of calculus

Inductive and Deductive Logic

The English philosopher Francis Bacon (1561-1626) was also highly influential in the development of modern science. Bacon was not an actual practitioner of science but was a strong proponent of its emerging methodologies. He promoted the application of inductive logic, in which conclusions are developed from the accumulating evidence of experience and the results of experiments. Inductive logic can lead to unifying explanations based on large bodies of data and observations of phenomena. Consider the following illustration of inductive logic, applied to an environmental topic:

• Observation 1: Marine mammals off the Atlantic coast of Canada have large residues of DDT and other chlorinated hydrocarbons in their fat and other body tissues.
• Observation 2: So do marine mammals off British Columbia.
• Observation 3: As do those in the Arctic Ocean, although in lower concentrations.

Inductive conclusion:  There is a widespread contamination of marine mammals with chlorinated hydrocarbons. Further research may demonstrate that the contamination is a global phenomenon. This suggests a potentially important environmental problem.

In contrast, deductive logic involves making one or more initial assumptions and then drawing logical conclusions from those premises. Consequently, the truth of a deductive conclusion depends on the veracity of the original assumptions. If those suppositions are based on false information or on incorrect supernatural belief, then any deduced conclusions are likely to be wrong. Consider the following illustration of deductive logic:

• Assumption 1: TCDD, an extremely toxic chemical in the dioxin family, is poisonous when present in even the smallest concentrations in food and water—even a single molecule can cause toxicity.
• Assumption 2: Exposure to anything that is poisonous in even the smallest concentrations is unsafe.
• Assumption 3: No exposure that is unsafe should be allowed.

Deductive conclusion 1:  No exposure to TCDD is safe. Deductive conclusion 2:  No emissions of TCDD should be allowed.

The two conclusions are consistent with the original assumptions. However, there is disagreement among highly qualified scientists about those assumptions. Many toxicologists believe that exposures to TCDD (and any other potentially toxic chemicals) must exceed a threshold of biological tolerance before poisoning will result (see Chapter 15). In contrast, other scientists believe that even the smallest exposure to TCDD carries some degree of toxic risk. Thus, the strength of deductive logic depends on the acceptance and truth of the original assumptions from which its conclusions flow.

In general, inductive logic plays a much stronger role in modern science than does deductive logic. In both cases, however, the usefulness of any conclusions depends greatly on the accuracy of any observations and other data on which they were based. Poor data may lead to an inaccurate conclusion through the application of inductive logic, as will inappropriate assumptions in deductive logic.

Goals of Science

The broad goals of science are to understand natural phenomena and to explain how they may be changing over time. To achieve those goals, scientists undertake investigations that are based on information, inferences, and conclusions developed through a systematic application of logic, usually of the inductive sort. As such, scientists carefully observe natural phenomena and conduct experiments.

A higher goal of scientific research is to formulate laws that describe the workings of the universe in general terms. (For example, see Chapter 4 for a description of the laws of thermodynamics, which deal with the transformations of energy among its various states.) Universal laws, along with theories and hypotheses (see below), are used to understand and explain natural phenomena. However, many natural phenomena are extremely complex and may never be fully understood in terms of physical laws. This is particularly true of the ways that organisms and ecosystems are organized and function.

Scientific investigations may be pure or applied. Pure science is driven by intellectual curiosity – it is the unfettered search for knowledge and understanding, without regard for its usefulness in human welfare. Applied science is more goal-oriented and deals with practical difficulties and problems of one sort or another. Applied science might examine how to improve technology, or to advance the management of natural resources, or to reduce pollution or other environmental damages associated with human activities.

Facts, Hypotheses, and Experiments

A fact is an event or thing that is definitely known to have happened, to exist, and to be true. Facts are based on experience and scientific evidence. In contrast, a hypothesis is a proposed explanation for the occurrence of a phenomenon. Scientists formulate hypotheses as statements and then test them through experiments and other forms of research. Hypotheses are developed using logic, inference, and mathematical arguments in order to explain observed phenomena. However, it must always be possible to refute a scientific hypothesis. Thus, the hypothesis that “cats are so intelligent that they prevent humans from discovering it” cannot be logically refuted, and so it is not a scientific hypothesis.

A theory is a broader conception that refers to a set of explanations, rules, and laws. These are supported by a large body of observational and experimental evidence, all leading to robust conclusions. The following are some of the most famous theories in science:

• the theory of gravitation, first proposed by Isaac Newton (1642-1727)
• the theory of evolution by natural selection, published simultaneously in 1858 by two English naturalists, Charles Darwin (1809-1882) and Alfred Russel Wallace (1823-1913)
• the theory of relativity, identified by the German–Swiss physicist, Albert Einstein (1879-1955)

Celebrated theories like these are strongly supported by large bodies of evidence, and they will likely persist for a long time. However, we cannot say that these (or any other) theories are known with certainty to be true –some future experiments may yet falsify even these famous theories.

The scientific method begins with the identification of a question involving the structure or function of the natural world, which is usually developed using inductive logic (Figure 2.1). The question is interpreted in terms of existing theory, and specific hypotheses are formulated to explain the character and causes of the natural phenomenon. The research might involve observations made in nature, or carefully controlled experiments, and the results usually give scientists reasons to reject hypotheses rather than to accept them. Most hypotheses are rejected because their predictions are not borne out during the course of research. Any viable hypotheses are further examined through additional research, again largely involving experiments designed to disprove their predictions. Once a large body of evidence accumulates in support of a hypothesis, it can be used to corroborate the original theory.

Figure 2.1. Diagrammatic Representation of the Scientific Method. The scientific method starts with a question, relates that question to a theory, formulates a hypothesis, and then rigorously tests that hypothesis. Source: Modified from Raven and Johnson (1992).

The scientific method is only to investigate questions that can be critically examined through observation and experiment. Consequently, science cannot resolve value-laden questions, such as the meaning of life, good versus evil, or the existence and qualities of God or any other supernatural being or force.

An experiment is a test or investigation that is designed to provide evidence in support of, or preferably against, a hypothesis. A natural experiment is conducted by observing actual variations of phenomena in nature, and then developing explanations by analysis of possible causal mechanisms. A manipulative experiment involves the deliberate alteration of factors that are hypothesized to influence phenomena. The manipulations are carefully planned and controlled in order to determine whether predicted responses will occur, thereby uncovering causal relationships.

By far the most useful working hypotheses in scientific research are designed to disprove rather than support. A null hypothesis is a specific testable investigation that denies something implied by the main hypothesis being studied. Unless null hypotheses are eliminated on the basis of contrary evidence, we cannot be confident of the main hypothesis.

This is an important aspect of scientific investigation. For instance, a particular hypothesis might be supported by many confirming experiments or observations. This does not, however, serve to “prove” the hypothesis – rather, it only supports its conditional acceptance. As soon as a clearly defined hypothesis is falsified by an appropriately designed and well-conducted experiment, it is disproved for all time. This is why experiments designed to disprove hypotheses are a key aspect of the scientific method.

Revolutionary advances in understanding may occur when an important hypothesis or theory are rejected through discoveries of science. For instance, once it was discovered that the Earth is not flat, it became possible to confidently sail beyond the visible horizon without fear of falling off the edge of the world. Another example involved the discovery by Copernicus that the planets of our solar system revolve around the Sun, and the related concept that the Sun is an ordinary star among many – these revolutionary ideas replaced the previously dominant one that the planets, Sun, and stars all revolved around the Earth.

Thomas Kuhn (1922-1995) was a philosopher of science who emphasized the important role of “scientific revolutions” in achieving great advances in our understanding of the natural world. In essence, Kuhn (1996) said that a scientific revolution occurs when a well-established theory is rigorously tested and then collapses under the accumulating weight of new facts and observations that cannot be explained. This renders the original theory obsolete, to be replaced by a new, more informed paradigm (i.e., a set of assumptions, concepts, practices, and values that constitutes a way of viewing reality and is shared by an intellectual community).

A variable is a factor that is believed to influence a natural phenomenon. For example, a scientist might hypothesize that the productivity of a wheat crop is potentially limited by such variables as the availability of water, or of nutrients such as nitrogen and phosphorus. Some of the most powerful scientific experiments involve the manipulation of key (or controlling) variables and the comparison of results of those treatments with a control that was not manipulated. In the example just described, the specific variable that controls wheat productivity could be identified by conducting an experiment in which test populations are provided with varying amounts of water, nitrogen, and phosphorus, alone and in combination, and then comparing the results with a non-manipulated control.

In some respects, however, the explanation of the scientific method offered above is a bit uncritical. It perhaps suggests a too-orderly progression in terms of logical, objective experimentation and comparison of alternative hypotheses. These are, in fact, important components of the scientific method. Nevertheless, it is important to understand that the insights and personal biases of scientists are also significant in the conduct and progress of science. In most cases, scientists design research that they think will “work” to yield useful results and contribute to the orderly advancement of knowledge in their field. Karl Popper (1902-1994), a European philosopher, noted that scientists tend to use their “imaginative preconception” of the workings of the natural world to design experiments based on their informed insights. This means that effective scientists must be more than knowledgeable and technically skilled – they should also be capable of a degree of insightful creativity when forming their ideas, hypotheses, and research.

Image 2.1. An experiment is a controlled investigation designed to provide evidence for, or preferably against, a hypothesis about the working of the natural world. This laboratory experiment exposed test populations of a grass to different concentrations of a toxic chemical. 

Uncertainty

Much scientific investigation involves the collection of observations by measuring phenomena in the natural world. Another important aspect of science involves making predictions about the future values of variables. Such projections require a degree of understanding of the relationships among variables and their influencing factors, and of recent patterns of change. However, many kinds of scientific information and predictions are subject to inaccuracy. This occurs because measured data are often approximations of the true values of phenomena, and predictions are rarely fulfilled exactly. The accuracy of observations and predictions is influenced by various factors, especially those described in the following sections.

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 × 10 8  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 (Chapter 4), 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 (see Chapter 21). 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 × 10 6  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 × 10 6 ; 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.

A Need for Scepticism

Environmental science is filled with many examples of uncertainty—in present values and future changes of environmental variables, as well as in predictions of biological and ecological responses to those changes. To some degree the difficulties associated with scientific uncertainty can be mitigated by developing improved methods and technologies for analysis and by modelling and examining changes occurring in different parts of the world. The latter approach enhances our understanding by providing convergent evidence about the occurrence and causes of natural phenomena.

However, scientific information and understanding will always be subject to some degree of uncertainty. Therefore, predictions will always be inaccurate to some extent, and this uncertainty must be considered when trying to understand and deal with the causes and consequences of environmental changes. As such, all information and predictions in environmental science must be critically interpreted with uncertainty in mind (In Detail 2.1). This should be done whenever one is learning about an environmental issue, whether it involves listening to a speaker in a classroom, at a conference, or on video, or when reading an article in a newspaper, textbook, website, or scientific journal. Because of the uncertainty of many predictions in science, and particularly in the environmental realm, a certain amount of scepticism and critical analysis is always useful.

Environmental issues are acutely important to the welfare of people and other species. Science and its methods allow for a critical and objective identification of key issues, the investigation of their causes, and a degree of understanding of the consequences of environmental change. Scientific information influences decision making about environmental issues, including whether to pursue expensive strategies to avoid further, but often uncertain, damage.

Scientific information is, however, only one consideration for decision makers, who are also concerned with the economic, cultural, and political contexts of environmental problems (see Environmental Issues 1.1 and Chapter 27). In fact, when deciding how to deal with the causes and consequences of environmental changes, decision makers may give greater weight to non-scientific (social and economic) considerations than to scientific ones, especially when there is uncertainty about the latter. The most important decisions about environmental issues are made by politicians and senior bureaucrats in government, or by private managers, rather than by environmental scientists. Decision makers typically worry about the short-term implications of their decisions on their chances for re-election or continued employment, and on the economic activity of a company or society at large, as much as they do about the consequences of environmental damage (see also Chapter 27).

In Detail 2.1. Critical Evaluation of an Overload of Information More so than any previous society, we live today in a world of easy and abundant information. It has become remarkably easy for people to communicate with others over vast distances, turning the world into a “global village” (a phrase coined by Marshall McLuhan (1911-1980), a Canadian philosopher, to describe the phenomenon of universal networking). This global connectedness has been facilitated by technologies for transferring ideas and knowledge—particularly electronic communication devices, such as radio, television, computers, and their networks. Today, these technologies compress space and time to achieve a virtually instantaneous communication. In fact, so much information is now available that the situation is often referred to as an “information overload” that must be analyzed critically. Critical analysis is the process of sorting information and making scientific enquiries about data. Involved in all aspects of the scientific process, critical analysis scrutinizes information and research by posing sensible questions such as the following: Is the information derived from a scientific framework consisting of a hypothesis that has been developed and tested, within the context of an existing body of knowledge and theory in the field? Were the methodologies used likely to provide data that are objective, accurate, and precise? Were the data analyzed by statistical methods that are appropriate to the data structure and to the questions being asked? Were the results of the research compared with other pertinent work that has been previously published? Were key similarities and differences discussed and a conclusion deduced about what the new work reveals about the issue being investigated? Is the information based on research published in a refereed journal—one that requires highly qualified reviewers in the subject area to scrutinize the work, followed by an editorial decision about whether it warrants publication? If the analysis of an issue was based on incomplete or possibly inaccurate information, was a precautionary approach used in order to accommodate the uncertainty inherent in the recommendations? All users of published research have an obligation to critically evaluate what they are reading in these ways in order to decide whether the theory is appropriate, the methodologies reliable, and the conclusions sufficiently robust. Because so many environmental issues are controversial, with data and information presented on both sides of the debate, people need to be able to formulate objectively critical judgments. For this reason, people need a high degree of environmental literacy—an informed understanding of the causes and consequences of environmental damages. Being able to critically analyze information is a key personal benefit of studying environmental science.

Conclusions

The procedures and methods of science are important in the identifying, understanding, and resolving environmental problems. At the same time, however, social and economic issues are also vital considerations. Although science has made tremendous progress in helping us to understand the natural world, the extreme complexity of biology and ecosystems makes it difficult for environmental scientists to make reliable predictions about the consequences of many human economic activities and other influences. This context underscores the need for continued study of the scientific and socio-economic dimensions of environmental problems, even while practical decisions must be made to deal with obvious issues as they arise.

Questions for Review

• Outline the reasons why science is a rational way of understanding the natural world.
• What are the differences between inductive and deductive logic? Why is inductive logic more often used by scientists when formulating hypotheses and generalizations about the natural world?
• Why are null hypotheses an efficient way to conduct scientific research? Identify a hypothesis that is suitable for examining a specific problem in environmental science and suggest a corresponding null hypothesis that could be examined through research.
• What are the causes of variation in natural phenomena? Choose an example, such as differences in the body weights of a defined group of people, and suggest reasons for the variation.

Questions for Discussion

• What are the key differences between science and a less objective belief system, such as religion?
• What factors result in scientific controversies about environmental issues? Contrast these with environmental controversies that exist because of differing values and world views.
• Explain why there are no scientific “laws” to explain the structure and function of ecosystems.
• Many natural phenomena are highly variable, particularly ones that are biological or ecological. What are the implications of this variability for understanding and predicting the causes and consequences of environmental changes? How do environmental scientists cope with this challenge of a variable natural world?

Exploring Issues

• Devise an environmental question of interest to yourself. Suggest useful hypotheses to investigate, identify the null hypotheses, and outline experiments that you might conduct to provide answers to this question.
• During a research project investigating mercury, an environmental scientist performed a series of chemical analyses of fish caught in Lake Canuck. The sampling program involved seven species of fish obtained from various habitats within the lake. A total of 360 fish of various sizes and sexes were analyzed. It was discovered that 30% of the fish had residue levels greater than 0.5 ppm of mercury, the upper level of contamination recommended by Health Canada for fish eaten by humans. The scientist reported these results to a governmental regulator, who was alarmed by the high mercury residues because of Lake Canuck’s popularity as a place where people fish for food. The regulator asked the scientist to recommend whether it was safe to eat any fish from the lake or whether to avoid only certain sizes, sexes, species, or habitats. What sorts of data analyses should the scientist perform to develop useful recommendations? What other scientific and non-scientific aspects should be considered?

References Cited and Further Reading

American Association for the Advancement of Science (AAAS). 1990. Science for All Americans. AAAS, Washington, DC.

Barnes, B. 1985. About Science. Blackwell Ltd ,London, UK.

Giere, R.N. 2005. Understanding Scientific Reasoning. 5th ed. Wadsworth Publishing, New York, NY.

Kuhn, T.S. 1996. The Structure of Scientific Revolutions. 3rd ed. University of Chicago Press, Chicago, IL.

McCain, G. and E.M. Siegal. 1982. The Game of Science. Holbrook Press Inc., Boston, MA.

Moore, J.A. 1999. Science as a Way of Knowing. Harvard University Press, Boston, MA.

Popper, K. 1979. Objective Knowledge: An Evolutionary Approach. Clarendon Press, Oxford, UK.

Raven, P.H., G.B. Johnson, K.A. Mason, and J. Losos. 2013. Biology. 10th ed. McGraw-Hill, Columbus, OH.

Silver, B.L. 2000. The Ascent of Science. Oxford University Press, Oxford, UK.

Environmental Science Copyright © 2018 by Dalhousie University is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

Share This Book

What Is a Hypothesis? (Science)

If...,Then...

Angela Lumsden/Getty Images

• Scientific Method
• Chemical Laws
• Periodic Table
• Projects & Experiments
• Biochemistry
• Physical Chemistry
• Medical Chemistry
• Chemistry In Everyday Life
• Famous Chemists
• Activities for Kids
• Abbreviations & Acronyms
• Weather & Climate
• Ph.D., Biomedical Sciences, University of Tennessee at Knoxville
• B.A., Physics and Mathematics, Hastings College

A hypothesis (plural hypotheses) is a proposed explanation for an observation. The definition depends on the subject.

In science, a hypothesis is part of the scientific method. It is a prediction or explanation that is tested by an experiment. Observations and experiments may disprove a scientific hypothesis, but can never entirely prove one.

In the study of logic, a hypothesis is an if-then proposition, typically written in the form, "If X , then Y ."

In common usage, a hypothesis is simply a proposed explanation or prediction, which may or may not be tested.

Writing a Hypothesis

Most scientific hypotheses are proposed in the if-then format because it's easy to design an experiment to see whether or not a cause and effect relationship exists between the independent variable and the dependent variable . The hypothesis is written as a prediction of the outcome of the experiment.

Null Hypothesis and Alternative Hypothesis

Statistically, it's easier to show there is no relationship between two variables than to support their connection. So, scientists often propose the null hypothesis . The null hypothesis assumes changing the independent variable will have no effect on the dependent variable.

In contrast, the alternative hypothesis suggests changing the independent variable will have an effect on the dependent variable. Designing an experiment to test this hypothesis can be trickier because there are many ways to state an alternative hypothesis.

For example, consider a possible relationship between getting a good night's sleep and getting good grades. The null hypothesis might be stated: "The number of hours of sleep students get is unrelated to their grades" or "There is no correlation between hours of sleep and grades."

An experiment to test this hypothesis might involve collecting data, recording average hours of sleep for each student and grades. If a student who gets eight hours of sleep generally does better than students who get four hours of sleep or 10 hours of sleep, the hypothesis might be rejected.

But the alternative hypothesis is harder to propose and test. The most general statement would be: "The amount of sleep students get affects their grades." The hypothesis might also be stated as "If you get more sleep, your grades will improve" or "Students who get nine hours of sleep have better grades than those who get more or less sleep."

In an experiment, you can collect the same data, but the statistical analysis is less likely to give you a high confidence limit.

Usually, a scientist starts out with the null hypothesis. From there, it may be possible to propose and test an alternative hypothesis, to narrow down the relationship between the variables.

Example of a Hypothesis

Examples of a hypothesis include:

• If you drop a rock and a feather, (then) they will fall at the same rate.
• Plants need sunlight in order to live. (if sunlight, then life)
• Eating sugar gives you energy. (if sugar, then energy)
• White, Jay D.  Research in Public Administration . Conn., 1998.
• Schick, Theodore, and Lewis Vaughn.  How to Think about Weird Things: Critical Thinking for a New Age . McGraw-Hill Higher Education, 2002.
• Scientific Method Flow Chart
• Six Steps of the Scientific Method
• What Are the Elements of a Good Hypothesis?
• What Are Examples of a Hypothesis?
• What Is a Testable Hypothesis?
• Null Hypothesis Examples
• Scientific Hypothesis Examples
• Scientific Variable
• Scientific Method Vocabulary Terms
• Understanding Simple vs Controlled Experiments
• What Is a Controlled Experiment?
• What Is an Experimental Constant?
• What Is the Difference Between a Control Variable and Control Group?
• DRY MIX Experiment Variables Acronym
• Random Error vs. Systematic Error
• The Role of a Controlled Variable in an Experiment

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection .

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

What is a hypothesis, developing a hypothesis (with example), hypothesis examples, other interesting articles, frequently asked questions about writing hypotheses.

A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.

A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

• An independent variable is something the researcher changes or controls.
• A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

Step 1. Ask a question

Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.

Step 2. Do some preliminary research

Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.

4. Refine your hypothesis

You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:

• The relevant variables
• The specific group being studied
• The predicted outcome of the experiment or analysis

5. Phrase your hypothesis in three ways

To identify the variables, you can write a simple prediction in  if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable.

In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.

If you are comparing two groups, the hypothesis can state what difference you expect to find between them.

6. Write a null hypothesis

If your research involves statistical hypothesis testing , you will also have to write a null hypothesis . The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .

• H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
• H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, November 20). How to Write a Strong Hypothesis | Steps & Examples. Scribbr. Retrieved August 28, 2024, from https://www.scribbr.com/methodology/hypothesis/

Is this article helpful?

Shona McCombes

Other students also liked, construct validity | definition, types, & examples, what is a conceptual framework | tips & examples, operationalization | a guide with examples, pros & cons, "i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

The scientific method and climate change: How scientists know

By Holly Shaftel, NASA's Jet Propulsion Laboratory

The scientific method is the gold standard for exploring our natural world. You might have learned about it in grade school, but here’s a quick reminder: It’s the process that scientists use to understand everything from animal behavior to the forces that shape our planet—including climate change.

“The way science works is that I go out and study something, and maybe I collect data or write equations, or I run a big computer program,” said Josh Willis, principal investigator of NASA’s Oceans Melting Greenland (OMG) mission and oceanographer at NASA’s Jet Propulsion Laboratory. “And I use it to learn something about how the world works.”

Using the scientific method, scientists have shown that humans are extremely likely the dominant cause of today’s climate change. The story goes back to the late 1800s, but in 1958, for example, Charles Keeling of the Mauna Loa Observatory in Waimea, Hawaii, started taking meticulous measurements of carbon dioxide (CO 2 ) in the atmosphere, showing the first significant evidence of rapidly rising CO 2 levels and producing the Keeling Curve climate scientists know today.

“The way science works is that I go out and study something, and maybe I collect data or write equations, or I run a big computer program, and I use it to learn something about how the world works.”- Josh Willis, NASA oceanographer and Oceans Melting Greenland principal investigator

Since then, thousands of peer-reviewed scientific papers have come to the same conclusion about climate change, telling us that human activities emit greenhouse gases into the atmosphere, raising Earth’s average temperature and bringing a range of consequences to our ecosystems.

“The weight of all of this information taken together points to the single consistent fact that humans and our activity are warming the planet,” Willis said.

The scientific method’s steps

The exact steps of the scientific method can vary by discipline, but since we have only one Earth (and no “test” Earth), climate scientists follow a few general guidelines to better understand carbon dioxide levels, sea level rise, global temperature and more.

• Form a hypothesis (a statement that an experiment can test)
• Make observations (conduct experiments and gather data)
• Analyze and interpret the data
• Draw conclusions
• Publish results that can be validated with further experiments (rinse and repeat)

As you can see, the scientific method is iterative (repetitive), meaning that climate scientists are constantly making new discoveries about the world based on the building blocks of scientific knowledge.

“The weight of all of this information taken together points to the single consistent fact that humans and our activity are warming the planet." - Josh Willis, NASA oceanographer and Oceans Melting Greenland principal investigator

The scientific method at work.

How does the scientific method work in the real world of climate science? Let’s take NASA’s Oceans Melting Greenland (OMG) campaign, a multi-year survey of Greenland’s ice melt that’s paving the way for improved sea level rise estimates, as an example.

• Form a hypothesis OMG hypothesizes that the oceans are playing a major role in Greenland ice loss.
• Make observations Over a five-year period, OMG will survey Greenland by air and ship to collect ocean temperature and salinity (saltiness) data and take ice thinning measurements to help climate scientists better understand how the ice and warming ocean interact with each other. OMG will also collect data on the sea floor’s shape and depth, which determines how much warm water can reach any given glacier.
• Analyze and interpret data As the OMG crew and scientists collect data around 27,000 miles (over 43,000 kilometers) of Greenland coastline over that five-year period, each year scientists will analyze the data to see how much the oceans warmed or cooled and how the ice changed in response.
• Draw conclusions In one OMG study , scientists discovered that many Greenland glaciers extend deeper (some around 1,000 feet, or about 300 meters) beneath the ocean’s surface than once thought, making them quite vulnerable to the warming ocean. They also discovered that Greenland’s west coast is generally more vulnerable than its east coast.
• Publish results Scientists like Willis write up the results, send in the paper for peer review (a process in which other experts in the field anonymously critique the submission), and then those peers determine whether the information is correct and valuable enough to be published in an academic journal, such as Nature or Earth and Planetary Science Letters . Then it becomes another contribution to the well-substantiated body of climate change knowledge, which evolves and grows stronger as scientists gather and confirm more evidence. Other scientists can take that information further by conducting their own studies to better understand sea level rise.

All in all, the scientific method is “a way of going from observations to answers,” NASA terrestrial ecosystem scientist Erika Podest, based at JPL, said. It adds clarity to our way of thinking and shows that scientific knowledge is always evolving.

Related Terms

• Climate Change
• Climate Science
• Earth Science

Explore More

NASA JPL Developing Underwater Robots to Venture Deep Below Polar Ice

Called IceNode, the project envisions a fleet of autonomous robots that would help determine the melt rate of ice shelves. On a remote patch of the windy, frozen Beaufort Sea north of Alaska, engineers from NASA’s Jet Propulsion Laboratory in Southern California huddled together, peering down a narrow hole in a thick layer of sea […]

NASA Project in Puerto Rico Trains Students in Marine Biology

Tainaliz Marie Rodríguez Lugo took a deep breath, adjusted her snorkel mask, and plunged into the ocean, fins first. Three weeks earlier, Rodríguez Lugo couldn’t swim. Now the college student was gathering data on water quality and coral reefs for a NASA-led marine biology project in Puerto Rico, where she lives.   “There is so much […]

NASA Discovers a Long-Sought Global Electric Field on Earth

An international team of scientists has successfully measured a planet-wide electric field thought to be as fundamental to Earth as its gravity and magnetic fields. Known as the ambipolar electric field, scientists first hypothesized over 60 years ago that it drove atmospheric escape above Earth’s North and South Poles. Measurements from a suborbital rocket have confirmed the existence of the ambipolar field and quantified its strength, revealing its role in driving atmospheric escape and shaping our ionosphere — a layer of the upper atmosphere — more broadly. The paper was published today in the journal Nature.

Discover More Topics From NASA

Explore Earth Science

Earth Science in Action

Earth Science Data

Facts About Earth

What is a scientific hypothesis?

It's the initial building block in the scientific method.

Hypothesis basics

What makes a hypothesis testable.

• Types of hypotheses
• Hypothesis versus theory

Additional resources

Bibliography.

A scientific hypothesis is a tentative, testable explanation for a phenomenon in the natural world. It's the initial building block in the scientific method . Many describe it as an "educated guess" based on prior knowledge and observation. While this is true, a hypothesis is more informed than a guess. While an "educated guess" suggests a random prediction based on a person's expertise, developing a hypothesis requires active observation and background research. 

The basic idea of a hypothesis is that there is no predetermined outcome. For a solution to be termed a scientific hypothesis, it has to be an idea that can be supported or refuted through carefully crafted experimentation or observation. This concept, called falsifiability and testability, was advanced in the mid-20th century by Austrian-British philosopher Karl Popper in his famous book "The Logic of Scientific Discovery" (Routledge, 1959).

A key function of a hypothesis is to derive predictions about the results of future experiments and then perform those experiments to see whether they support the predictions.

A hypothesis is usually written in the form of an if-then statement, which gives a possibility (if) and explains what may happen because of the possibility (then). The statement could also include "may," according to California State University, Bakersfield .

Here are some examples of hypothesis statements:

• If garlic repels fleas, then a dog that is given garlic every day will not get fleas.
• If sugar causes cavities, then people who eat a lot of candy may be more prone to cavities.
• If ultraviolet light can damage the eyes, then maybe this light can cause blindness.

A useful hypothesis should be testable and falsifiable. That means that it should be possible to prove it wrong. A theory that can't be proved wrong is nonscientific, according to Karl Popper's 1963 book " Conjectures and Refutations ."

An example of an untestable statement is, "Dogs are better than cats." That's because the definition of "better" is vague and subjective. However, an untestable statement can be reworded to make it testable. For example, the previous statement could be changed to this: "Owning a dog is associated with higher levels of physical fitness than owning a cat." With this statement, the researcher can take measures of physical fitness from dog and cat owners and compare the two.

Types of scientific hypotheses

In an experiment, researchers generally state their hypotheses in two ways. The null hypothesis predicts that there will be no relationship between the variables tested, or no difference between the experimental groups. The alternative hypothesis predicts the opposite: that there will be a difference between the experimental groups. This is usually the hypothesis scientists are most interested in, according to the University of Miami .

For example, a null hypothesis might state, "There will be no difference in the rate of muscle growth between people who take a protein supplement and people who don't." The alternative hypothesis would state, "There will be a difference in the rate of muscle growth between people who take a protein supplement and people who don't."

If the results of the experiment show a relationship between the variables, then the null hypothesis has been rejected in favor of the alternative hypothesis, according to the book " Research Methods in Psychology " (​​BCcampus, 2015). 

There are other ways to describe an alternative hypothesis. The alternative hypothesis above does not specify a direction of the effect, only that there will be a difference between the two groups. That type of prediction is called a two-tailed hypothesis. If a hypothesis specifies a certain direction — for example, that people who take a protein supplement will gain more muscle than people who don't — it is called a one-tailed hypothesis, according to William M. K. Trochim , a professor of Policy Analysis and Management at Cornell University.

Sometimes, errors take place during an experiment. These errors can happen in one of two ways. A type I error is when the null hypothesis is rejected when it is true. This is also known as a false positive. A type II error occurs when the null hypothesis is not rejected when it is false. This is also known as a false negative, according to the University of California, Berkeley . 

A hypothesis can be rejected or modified, but it can never be proved correct 100% of the time. For example, a scientist can form a hypothesis stating that if a certain type of tomato has a gene for red pigment, that type of tomato will be red. During research, the scientist then finds that each tomato of this type is red. Though the findings confirm the hypothesis, there may be a tomato of that type somewhere in the world that isn't red. Thus, the hypothesis is true, but it may not be true 100% of the time.

Scientific theory vs. scientific hypothesis

The best hypotheses are simple. They deal with a relatively narrow set of phenomena. But theories are broader; they generally combine multiple hypotheses into a general explanation for a wide range of phenomena, according to the University of California, Berkeley . For example, a hypothesis might state, "If animals adapt to suit their environments, then birds that live on islands with lots of seeds to eat will have differently shaped beaks than birds that live on islands with lots of insects to eat." After testing many hypotheses like these, Charles Darwin formulated an overarching theory: the theory of evolution by natural selection.

"Theories are the ways that we make sense of what we observe in the natural world," Tanner said. "Theories are structures of ideas that explain and interpret facts." 

• Read more about writing a hypothesis, from the American Medical Writers Association.
• Find out why a hypothesis isn't always necessary in science, from The American Biology Teacher.
• Learn about null and alternative hypotheses, from Prof. Essa on YouTube .

Encyclopedia Britannica. Scientific Hypothesis. Jan. 13, 2022. https://www.britannica.com/science/scientific-hypothesis

Karl Popper, "The Logic of Scientific Discovery," Routledge, 1959.

California State University, Bakersfield, "Formatting a testable hypothesis." https://www.csub.edu/~ddodenhoff/Bio100/Bio100sp04/formattingahypothesis.htm  

Karl Popper, "Conjectures and Refutations," Routledge, 1963.

Price, P., Jhangiani, R., & Chiang, I., "Research Methods of Psychology — 2nd Canadian Edition," BCcampus, 2015.‌

University of Miami, "The Scientific Method" http://www.bio.miami.edu/dana/161/evolution/161app1_scimethod.pdf  

William M.K. Trochim, "Research Methods Knowledge Base," https://conjointly.com/kb/hypotheses-explained/  

University of California, Berkeley, "Multiple Hypothesis Testing and False Discovery Rate" https://www.stat.berkeley.edu/~hhuang/STAT141/Lecture-FDR.pdf  

University of California, Berkeley, "Science at multiple levels" https://undsci.berkeley.edu/article/0_0_0/howscienceworks_19

Sign up for the Live Science daily newsletter now

Get the world’s most fascinating discoveries delivered straight to your inbox.

Large patch of the Atlantic Ocean near the equator has been cooling at record speeds — and scientists can't figure out why

Earth from space: Massive landslide dams Canadian river, trapping endangered fish on the wrong side

Ancient sea cow was killed by prehistoric croc then torn apart by a tiger shark

Most Popular

• 2 For C. diff, antibiotic resistance comes at a cost
• 3 Large patch of the Atlantic Ocean near the equator has been cooling at record speeds — and scientists can't figure out why
• 4 Gravitational waves hint at a 'supercool' secret about the Big Bang
• 5 New reactor could more than triple the yield of one of the world's most valuable chemicals

Module 1: Introduction to Biology

Experiments and hypotheses, learning outcomes.

• Form a hypothesis and use it to design a scientific experiment

Now we’ll focus on the methods of scientific inquiry. Science often involves making observations and developing hypotheses. Experiments and further observations are often used to test the hypotheses.

A scientific experiment is a carefully organized procedure in which the scientist intervenes in a system to change something, then observes the result of the change. Scientific inquiry often involves doing experiments, though not always. For example, a scientist studying the mating behaviors of ladybugs might begin with detailed observations of ladybugs mating in their natural habitats. While this research may not be experimental, it is scientific: it involves careful and verifiable observation of the natural world. The same scientist might then treat some of the ladybugs with a hormone hypothesized to trigger mating and observe whether these ladybugs mated sooner or more often than untreated ones. This would qualify as an experiment because the scientist is now making a change in the system and observing the effects.

Forming a Hypothesis

When conducting scientific experiments, researchers develop hypotheses to guide experimental design. A hypothesis is a suggested explanation that is both testable and falsifiable. You must be able to test your hypothesis, and it must be possible to prove your hypothesis true or false.

For example, Michael observes that maple trees lose their leaves in the fall. He might then propose a possible explanation for this observation: “cold weather causes maple trees to lose their leaves in the fall.” This statement is testable. He could grow maple trees in a warm enclosed environment such as a greenhouse and see if their leaves still dropped in the fall. The hypothesis is also falsifiable. If the leaves still dropped in the warm environment, then clearly temperature was not the main factor in causing maple leaves to drop in autumn.

In the Try It below, you can practice recognizing scientific hypotheses. As you consider each statement, try to think as a scientist would: can I test this hypothesis with observations or experiments? Is the statement falsifiable? If the answer to either of these questions is “no,” the statement is not a valid scientific hypothesis.

Practice Questions

Determine whether each following statement is a scientific hypothesis.

Air pollution from automobile exhaust can trigger symptoms in people with asthma.

• No. This statement is not testable or falsifiable.
• No. This statement is not testable.
• No. This statement is not falsifiable.
• Yes. This statement is testable and falsifiable.

Natural disasters, such as tornadoes, are punishments for bad thoughts and behaviors.

a: No. This statement is not testable or falsifiable. “Bad thoughts and behaviors” are excessively vague and subjective variables that would be impossible to measure or agree upon in a reliable way. The statement might be “falsifiable” if you came up with a counterexample: a “wicked” place that was not punished by a natural disaster. But some would question whether the people in that place were really wicked, and others would continue to predict that a natural disaster was bound to strike that place at some point. There is no reason to suspect that people’s immoral behavior affects the weather unless you bring up the intervention of a supernatural being, making this idea even harder to test.

Testing a Vaccine

Let’s examine the scientific process by discussing an actual scientific experiment conducted by researchers at the University of Washington. These researchers investigated whether a vaccine may reduce the incidence of the human papillomavirus (HPV). The experimental process and results were published in an article titled, “ A controlled trial of a human papillomavirus type 16 vaccine .”

Preliminary observations made by the researchers who conducted the HPV experiment are listed below:

• Human papillomavirus (HPV) is the most common sexually transmitted virus in the United States.
• There are about 40 different types of HPV. A significant number of people that have HPV are unaware of it because many of these viruses cause no symptoms.
• Some types of HPV can cause cervical cancer.
• About 4,000 women a year die of cervical cancer in the United States.

Practice Question

Researchers have developed a potential vaccine against HPV and want to test it. What is the first testable hypothesis that the researchers should study?

• HPV causes cervical cancer.
• People should not have unprotected sex with many partners.
• People who get the vaccine will not get HPV.
• The HPV vaccine will protect people against cancer.

Experimental Design

You’ve successfully identified a hypothesis for the University of Washington’s study on HPV: People who get the HPV vaccine will not get HPV.

The next step is to design an experiment that will test this hypothesis. There are several important factors to consider when designing a scientific experiment. First, scientific experiments must have an experimental group. This is the group that receives the experimental treatment necessary to address the hypothesis.

The experimental group receives the vaccine, but how can we know if the vaccine made a difference? Many things may change HPV infection rates in a group of people over time. To clearly show that the vaccine was effective in helping the experimental group, we need to include in our study an otherwise similar control group that does not get the treatment. We can then compare the two groups and determine if the vaccine made a difference. The control group shows us what happens in the absence of the factor under study.

However, the control group cannot get “nothing.” Instead, the control group often receives a placebo. A placebo is a procedure that has no expected therapeutic effect—such as giving a person a sugar pill or a shot containing only plain saline solution with no drug. Scientific studies have shown that the “placebo effect” can alter experimental results because when individuals are told that they are or are not being treated, this knowledge can alter their actions or their emotions, which can then alter the results of the experiment.

Moreover, if the doctor knows which group a patient is in, this can also influence the results of the experiment. Without saying so directly, the doctor may show—through body language or other subtle cues—their views about whether the patient is likely to get well. These errors can then alter the patient’s experience and change the results of the experiment. Therefore, many clinical studies are “double blind.” In these studies, neither the doctor nor the patient knows which group the patient is in until all experimental results have been collected.

Both placebo treatments and double-blind procedures are designed to prevent bias. Bias is any systematic error that makes a particular experimental outcome more or less likely. Errors can happen in any experiment: people make mistakes in measurement, instruments fail, computer glitches can alter data. But most such errors are random and don’t favor one outcome over another. Patients’ belief in a treatment can make it more likely to appear to “work.” Placebos and double-blind procedures are used to level the playing field so that both groups of study subjects are treated equally and share similar beliefs about their treatment.

The scientists who are researching the effectiveness of the HPV vaccine will test their hypothesis by separating 2,392 young women into two groups: the control group and the experimental group. Answer the following questions about these two groups.

• This group is given a placebo.
• This group is deliberately infected with HPV.
• This group is given nothing.
• This group is given the HPV vaccine.
• a: This group is given a placebo. A placebo will be a shot, just like the HPV vaccine, but it will have no active ingredient. It may change peoples’ thinking or behavior to have such a shot given to them, but it will not stimulate the immune systems of the subjects in the same way as predicted for the vaccine itself.
• d: This group is given the HPV vaccine. The experimental group will receive the HPV vaccine and researchers will then be able to see if it works, when compared to the control group.

Experimental Variables

A variable is a characteristic of a subject (in this case, of a person in the study) that can vary over time or among individuals. Sometimes a variable takes the form of a category, such as male or female; often a variable can be measured precisely, such as body height. Ideally, only one variable is different between the control group and the experimental group in a scientific experiment. Otherwise, the researchers will not be able to determine which variable caused any differences seen in the results. For example, imagine that the people in the control group were, on average, much more sexually active than the people in the experimental group. If, at the end of the experiment, the control group had a higher rate of HPV infection, could you confidently determine why? Maybe the experimental subjects were protected by the vaccine, but maybe they were protected by their low level of sexual contact.

To avoid this situation, experimenters make sure that their subject groups are as similar as possible in all variables except for the variable that is being tested in the experiment. This variable, or factor, will be deliberately changed in the experimental group. The one variable that is different between the two groups is called the independent variable. An independent variable is known or hypothesized to cause some outcome. Imagine an educational researcher investigating the effectiveness of a new teaching strategy in a classroom. The experimental group receives the new teaching strategy, while the control group receives the traditional strategy. It is the teaching strategy that is the independent variable in this scenario. In an experiment, the independent variable is the variable that the scientist deliberately changes or imposes on the subjects.

Dependent variables are known or hypothesized consequences; they are the effects that result from changes or differences in an independent variable. In an experiment, the dependent variables are those that the scientist measures before, during, and particularly at the end of the experiment to see if they have changed as expected. The dependent variable must be stated so that it is clear how it will be observed or measured. Rather than comparing “learning” among students (which is a vague and difficult to measure concept), an educational researcher might choose to compare test scores, which are very specific and easy to measure.

In any real-world example, many, many variables MIGHT affect the outcome of an experiment, yet only one or a few independent variables can be tested. Other variables must be kept as similar as possible between the study groups and are called control variables . For our educational research example, if the control group consisted only of people between the ages of 18 and 20 and the experimental group contained people between the ages of 30 and 35, we would not know if it was the teaching strategy or the students’ ages that played a larger role in the results. To avoid this problem, a good study will be set up so that each group contains students with a similar age profile. In a well-designed educational research study, student age will be a controlled variable, along with other possibly important factors like gender, past educational achievement, and pre-existing knowledge of the subject area.

What is the independent variable in this experiment?

• Sex (all of the subjects will be female)
• Presence or absence of the HPV vaccine
• Presence or absence of HPV (the virus)

List three control variables other than age.

What is the dependent variable in this experiment?

• Sex (male or female)
• Rates of HPV infection
• Age (years)
• Revision and adaptation. Authored by : Shelli Carter and Lumen Learning. Provided by : Lumen Learning. License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike
• Scientific Inquiry. Provided by : Open Learning Initiative. Located at : https://oli.cmu.edu/jcourse/workbook/activity/page?context=434a5c2680020ca6017c03488572e0f8 . Project : Introduction to Biology (Open + Free). License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike

Theories, Hypotheses, and Laws: Definitions, examples, and their roles in science

by Anthony Carpi, Ph.D., Anne E. Egger, Ph.D.

Listen to this reading

Did you know that the idea of evolution had been part of Western thought for more than 2,000 years before Charles Darwin was born? Like many theories, the theory of evolution was the result of the work of many different scientists working in different disciplines over a period of time.

A scientific theory is an explanation inferred from multiple lines of evidence for some broad aspect of the natural world and is logical, testable, and predictive.

As new evidence comes to light, or new interpretations of existing data are proposed, theories may be revised and even change; however, they are not tenuous or speculative.

A scientific hypothesis is an inferred explanation of an observation or research finding; while more exploratory in nature than a theory, it is based on existing scientific knowledge.

A scientific law is an expression of a mathematical or descriptive relationship observed in nature.

Imagine yourself shopping in a grocery store with a good friend who happens to be a chemist. Struggling to choose between the many different types of tomatoes in front of you, you pick one up, turn to your friend, and ask her if she thinks the tomato is organic . Your friend simply chuckles and replies, "Of course it's organic!" without even looking at how the fruit was grown. Why the amused reaction? Your friend is highlighting a simple difference in vocabulary. To a chemist, the term organic refers to any compound in which hydrogen is bonded to carbon. Tomatoes (like all plants) are abundant in organic compounds – thus your friend's laughter. In modern agriculture, however, organic has come to mean food items grown or raised without the use of chemical fertilizers, pesticides, or other additives.

So who is correct? You both are. Both uses of the word are correct, though they mean different things in different contexts. There are, of course, lots of words that have more than one meaning (like bat , for example), but multiple meanings can be especially confusing when two meanings convey very different ideas and are specific to one field of study.

• Scientific theories

The term theory also has two meanings, and this double meaning often leads to confusion. In common language, the term theory generally refers to speculation or a hunch or guess. You might have a theory about why your favorite sports team isn't playing well, or who ate the last cookie from the cookie jar. But these theories do not fit the scientific use of the term. In science, a theory is a well-substantiated and comprehensive set of ideas that explains a phenomenon in nature. A scientific theory is based on large amounts of data and observations that have been collected over time. Scientific theories can be tested and refined by additional research , and they allow scientists to make predictions. Though you may be correct in your hunch, your cookie jar conjecture doesn't fit this more rigorous definition.

All scientific disciplines have well-established, fundamental theories . For example, atomic theory describes the nature of matter and is supported by multiple lines of evidence from the way substances behave and react in the world around us (see our series on Atomic Theory ). Plate tectonic theory describes the large scale movement of the outer layer of the Earth and is supported by evidence from studies about earthquakes , magnetic properties of the rocks that make up the seafloor , and the distribution of volcanoes on Earth (see our series on Plate Tectonic Theory ). The theory of evolution by natural selection , which describes the mechanism by which inherited traits that affect survivability or reproductive success can cause changes in living organisms over generations , is supported by extensive studies of DNA , fossils , and other types of scientific evidence (see our Charles Darwin series for more information). Each of these major theories guides and informs modern research in those fields, integrating a broad, comprehensive set of ideas.

So how are these fundamental theories developed, and why are they considered so well supported? Let's take a closer look at some of the data and research supporting the theory of natural selection to better see how a theory develops.

Comprehension Checkpoint

• The development of a scientific theory: Evolution and natural selection

The theory of evolution by natural selection is sometimes maligned as Charles Darwin 's speculation on the origin of modern life forms. However, evolutionary theory is not speculation. While Darwin is rightly credited with first articulating the theory of natural selection, his ideas built on more than a century of scientific research that came before him, and are supported by over a century and a half of research since.

• The Fixity Notion: Linnaeus

Figure 1: Cover of the 1760 edition of Systema Naturae .

Research about the origins and diversity of life proliferated in the 18th and 19th centuries. Carolus Linnaeus , a Swedish botanist and the father of modern taxonomy (see our module Taxonomy I for more information), was a devout Christian who believed in the concept of Fixity of Species , an idea based on the biblical story of creation. The Fixity of Species concept said that each species is based on an ideal form that has not changed over time. In the early stages of his career, Linnaeus traveled extensively and collected data on the structural similarities and differences between different species of plants. Noting that some very different plants had similar structures, he began to piece together his landmark work, Systema Naturae, in 1735 (Figure 1). In Systema , Linnaeus classified organisms into related groups based on similarities in their physical features. He developed a hierarchical classification system , even drawing relationships between seemingly disparate species (for example, humans, orangutans, and chimpanzees) based on the physical similarities that he observed between these organisms. Linnaeus did not explicitly discuss change in organisms or propose a reason for his hierarchy, but by grouping organisms based on physical characteristics, he suggested that species are related, unintentionally challenging the Fixity notion that each species is created in a unique, ideal form.

• The age of Earth: Leclerc and Hutton

Also in the early 1700s, Georges-Louis Leclerc, a French naturalist, and James Hutton , a Scottish geologist, began to develop new ideas about the age of the Earth. At the time, many people thought of the Earth as 6,000 years old, based on a strict interpretation of the events detailed in the Christian Old Testament by the influential Scottish Archbishop Ussher. By observing other planets and comets in the solar system , Leclerc hypothesized that Earth began as a hot, fiery ball of molten rock, mostly consisting of iron. Using the cooling rate of iron, Leclerc calculated that Earth must therefore be at least 70,000 years old in order to have reached its present temperature.

Hutton approached the same topic from a different perspective, gathering observations of the relationships between different rock formations and the rates of modern geological processes near his home in Scotland. He recognized that the relatively slow processes of erosion and sedimentation could not create all of the exposed rock layers in only a few thousand years (see our module The Rock Cycle ). Based on his extensive collection of data (just one of his many publications ran to 2,138 pages), Hutton suggested that the Earth was far older than human history – hundreds of millions of years old.

While we now know that both Leclerc and Hutton significantly underestimated the age of the Earth (by about 4 billion years), their work shattered long-held beliefs and opened a window into research on how life can change over these very long timescales.

• Fossil studies lead to the development of a theory of evolution: Cuvier

Figure 2: Illustration of an Indian elephant jaw and a mammoth jaw from Cuvier's 1796 paper.

With the age of Earth now extended by Leclerc and Hutton, more researchers began to turn their attention to studying past life. Fossils are the main way to study past life forms, and several key studies on fossils helped in the development of a theory of evolution . In 1795, Georges Cuvier began to work at the National Museum in Paris as a naturalist and anatomist. Through his work, Cuvier became interested in fossils found near Paris, which some claimed were the remains of the elephants that Hannibal rode over the Alps when he invaded Rome in 218 BCE . In studying both the fossils and living species , Cuvier documented different patterns in the dental structure and number of teeth between the fossils and modern elephants (Figure 2) (Horner, 1843). Based on these data , Cuvier hypothesized that the fossil remains were not left by Hannibal, but were from a distinct species of animal that once roamed through Europe and had gone extinct thousands of years earlier: the mammoth. The concept of species extinction had been discussed by a few individuals before Cuvier, but it was in direct opposition to the Fixity of Species concept – if every organism were based on a perfectly adapted, ideal form, how could any cease to exist? That would suggest it was no longer ideal.

While his work provided critical evidence of extinction , a key component of evolution , Cuvier was highly critical of the idea that species could change over time. As a result of his extensive studies of animal anatomy, Cuvier had developed a holistic view of organisms , stating that the

number, direction, and shape of the bones that compose each part of an animal's body are always in a necessary relation to all the other parts, in such a way that ... one can infer the whole from any one of them ...

In other words, Cuvier viewed each part of an organism as a unique, essential component of the whole organism. If one part were to change, he believed, the organism could not survive. His skepticism about the ability of organisms to change led him to criticize the whole idea of evolution , and his prominence in France as a scientist played a large role in discouraging the acceptance of the idea in the scientific community.

• Studies of invertebrates support a theory of change in species: Lamarck

Jean Baptiste Lamarck, a contemporary of Cuvier's at the National Museum in Paris, studied invertebrates like insects and worms. As Lamarck worked through the museum's large collection of invertebrates, he was impressed by the number and variety of organisms . He became convinced that organisms could, in fact, change through time, stating that

... time and favorable conditions are the two principal means which nature has employed in giving existence to all her productions. We know that for her time has no limit, and that consequently she always has it at her disposal.

This was a radical departure from both the fixity concept and Cuvier's ideas, and it built on the long timescale that geologists had recently established. Lamarck proposed that changes that occurred during an organism 's lifetime could be passed on to their offspring, suggesting, for example, that a body builder's muscles would be inherited by their children.

As it turned out, the mechanism by which Lamarck proposed that organisms change over time was wrong, and he is now often referred to disparagingly for his "inheritance of acquired characteristics" idea. Yet despite the fact that some of his ideas were discredited, Lamarck established a support for evolutionary theory that others would build on and improve.

• Rock layers as evidence for evolution: Smith

In the early 1800s, a British geologist and canal surveyor named William Smith added another component to the accumulating evidence for evolution . Smith observed that rock layers exposed in different parts of England bore similarities to one another: These layers (or strata) were arranged in a predictable order, and each layer contained distinct groups of fossils . From this series of observations , he developed a hypothesis that specific groups of animals followed one another in a definite sequence through Earth's history, and this sequence could be seen in the rock layers. Smith's hypothesis was based on his knowledge of geological principles , including the Law of Superposition.

The Law of Superposition states that sediments are deposited in a time sequence, with the oldest sediments deposited first, or at the bottom, and newer layers deposited on top. The concept was first expressed by the Persian scientist Avicenna in the 11th century, but was popularized by the Danish scientist Nicolas Steno in the 17th century. Note that the law does not state how sediments are deposited; it simply describes the relationship between the ages of deposited sediments.

Figure 3: Engraving from William Smith's 1815 monograph on identifying strata by fossils.

Smith backed up his hypothesis with extensive drawings of fossils uncovered during his research (Figure 3), thus allowing other scientists to confirm or dispute his findings. His hypothesis has, in fact, been confirmed by many other scientists and has come to be referred to as the Law of Faunal Succession. His work was critical to the formation of evolutionary theory as it not only confirmed Cuvier's work that organisms have gone extinct , but it also showed that the appearance of life does not date to the birth of the planet. Instead, the fossil record preserves a timeline of the appearance and disappearance of different organisms in the past, and in doing so offers evidence for change in organisms over time.

• The theory of evolution by natural selection: Darwin and Wallace

It was into this world that Charles Darwin entered: Linnaeus had developed a taxonomy of organisms based on their physical relationships, Leclerc and Hutton demonstrated that there was sufficient time in Earth's history for organisms to change, Cuvier showed that species of organisms have gone extinct , Lamarck proposed that organisms change over time, and Smith established a timeline of the appearance and disappearance of different organisms in the geological record .

Figure 4: Title page of the 1859 Murray edition of the Origin of Species by Charles Darwin.

Charles Darwin collected data during his work as a naturalist on the HMS Beagle starting in 1831. He took extensive notes on the geology of the places he visited; he made a major find of fossils of extinct animals in Patagonia and identified an extinct giant ground sloth named Megatherium . He experienced an earthquake in Chile that stranded beds of living mussels above water, where they would be preserved for years to come.

Perhaps most famously, he conducted extensive studies of animals on the Galápagos Islands, noting subtle differences in species of mockingbird, tortoise, and finch that were isolated on different islands with different environmental conditions. These subtle differences made the animals highly adapted to their environments .

This broad spectrum of data led Darwin to propose an idea about how organisms change "by means of natural selection" (Figure 4). But this idea was not based only on his work, it was also based on the accumulation of evidence and ideas of many others before him. Because his proposal encompassed and explained many different lines of evidence and previous work, they formed the basis of a new and robust scientific theory regarding change in organisms – the theory of evolution by natural selection .

Darwin's ideas were grounded in evidence and data so compelling that if he had not conceived them, someone else would have. In fact, someone else did. Between 1858 and 1859, Alfred Russel Wallace , a British naturalist, wrote a series of letters to Darwin that independently proposed natural selection as the means for evolutionary change. The letters were presented to the Linnean Society of London, a prominent scientific society at the time (see our module on Scientific Institutions and Societies ). This long chain of research highlights that theories are not just the work of one individual. At the same time, however, it often takes the insight and creativity of individuals to put together all of the pieces and propose a new theory . Both Darwin and Wallace were experienced naturalists who were familiar with the work of others. While all of the work leading up to 1830 contributed to the theory of evolution , Darwin's and Wallace's theory changed the way that future research was focused by presenting a comprehensive, well-substantiated set of ideas, thus becoming a fundamental theory of biological research.

• Expanding, testing, and refining scientific theories
• Genetics and evolution: Mendel and Dobzhansky

Since Darwin and Wallace first published their ideas, extensive research has tested and expanded the theory of evolution by natural selection . Darwin had no concept of genes or DNA or the mechanism by which characteristics were inherited within a species . A contemporary of Darwin's, the Austrian monk Gregor Mendel , first presented his own landmark study, Experiments in Plant Hybridization, in 1865 in which he provided the basic patterns of genetic inheritance , describing which characteristics (and evolutionary changes) can be passed on in organisms (see our Genetics I module for more information). Still, it wasn't until much later that a "gene" was defined as the heritable unit.

In 1937, the Ukrainian born geneticist Theodosius Dobzhansky published Genetics and the Origin of Species , a seminal work in which he described genes themselves and demonstrated that it is through mutations in genes that change occurs. The work defined evolution as "a change in the frequency of an allele within a gene pool" ( Dobzhansky, 1982 ). These studies and others in the field of genetics have added to Darwin's work, expanding the scope of the theory .

• Evolution under a microscope: Lenski

More recently, Dr. Richard Lenski, a scientist at Michigan State University, isolated a single Escherichia coli bacterium in 1989 as the first step of the longest running experimental test of evolutionary theory to date – a true test meant to replicate evolution and natural selection in the lab.

After the single microbe had multiplied, Lenski isolated the offspring into 12 different strains , each in their own glucose-supplied culture, predicting that the genetic make-up of each strain would change over time to become more adapted to their specific culture as predicted by evolutionary theory . These 12 lines have been nurtured for over 40,000 bacterial generations (luckily bacterial generations are much shorter than human generations) and exposed to different selective pressures such as heat , cold, antibiotics, and infection with other microorganisms. Lenski and colleagues have studied dozens of aspects of evolutionary theory with these genetically isolated populations . In 1999, they published a paper that demonstrated that random genetic mutations were common within the populations and highly diverse across different individual bacteria . However, "pivotal" mutations that are associated with beneficial changes in the group are shared by all descendants in a population and are much rarer than random mutations, as predicted by the theory of evolution by natural selection (Papadopoulos et al., 1999).

• Punctuated equilibrium: Gould and Eldredge

While established scientific theories like evolution have a wealth of research and evidence supporting them, this does not mean that they cannot be refined as new information or new perspectives on existing data become available. For example, in 1972, biologist Stephen Jay Gould and paleontologist Niles Eldredge took a fresh look at the existing data regarding the timing by which evolutionary change takes place. Gould and Eldredge did not set out to challenge the theory of evolution; rather they used it as a guiding principle and asked more specific questions to add detail and nuance to the theory. This is true of all theories in science: they provide a framework for additional research. At the time, many biologists viewed evolution as occurring gradually, causing small incremental changes in organisms at a relatively steady rate. The idea is referred to as phyletic gradualism , and is rooted in the geological concept of uniformitarianism . After reexamining the available data, Gould and Eldredge came to a different explanation, suggesting that evolution consists of long periods of stability that are punctuated by occasional instances of dramatic change – a process they called punctuated equilibrium .

Like Darwin before them, their proposal is rooted in evidence and research on evolutionary change, and has been supported by multiple lines of evidence. In fact, punctuated equilibrium is now considered its own theory in evolutionary biology. Punctuated equilibrium is not as broad of a theory as natural selection . In science, some theories are broad and overarching of many concepts, such as the theory of evolution by natural selection; others focus on concepts at a smaller, or more targeted, scale such as punctuated equilibrium. And punctuated equilibrium does not challenge or weaken the concept of natural selection; rather, it represents a change in our understanding of the timing by which change occurs in organisms , and a theory within a theory. The theory of evolution by natural selection now includes both gradualism and punctuated equilibrium to describe the rate at which change proceeds.

• Hypotheses and laws: Other scientific concepts

One of the challenges in understanding scientific terms like theory is that there is not a precise definition even within the scientific community. Some scientists debate over whether certain proposals merit designation as a hypothesis or theory , and others mistakenly use the terms interchangeably. But there are differences in these terms. A hypothesis is a proposed explanation for an observable phenomenon. Hypotheses , just like theories , are based on observations from research . For example, LeClerc did not hypothesize that Earth had cooled from a molten ball of iron as a random guess; rather, he developed this hypothesis based on his observations of information from meteorites.

A scientist often proposes a hypothesis before research confirms it as a way of predicting the outcome of study to help better define the parameters of the research. LeClerc's hypothesis allowed him to use known parameters (the cooling rate of iron) to do additional work. A key component of a formal scientific hypothesis is that it is testable and falsifiable. For example, when Richard Lenski first isolated his 12 strains of bacteria , he likely hypothesized that random mutations would cause differences to appear within a period of time in the different strains of bacteria. But when a hypothesis is generated in science, a scientist will also make an alternative hypothesis , an explanation that explains a study if the data do not support the original hypothesis. If the different strains of bacteria in Lenski's work did not diverge over the indicated period of time, perhaps the rate of mutation was slower than first thought.

So you might ask, if theories are so well supported, do they eventually become laws? The answer is no – not because they aren't well-supported, but because theories and laws are two very different things. Laws describe phenomena, often mathematically. Theories, however, explain phenomena. For example, in 1687 Isaac Newton proposed a Theory of Gravitation, describing gravity as a force of attraction between two objects. As part of this theory, Newton developed a Law of Universal Gravitation that explains how this force operates. This law states that the force of gravity between two objects is inversely proportional to the square of the distance between those objects. Newton 's Law does not explain why this is true, but it describes how gravity functions (see our Gravity: Newtonian Relationships module for more detail). In 1916, Albert Einstein developed his theory of general relativity to explain the mechanism by which gravity has its effect. Einstein's work challenges Newton's theory, and has been found after extensive testing and research to more accurately describe the phenomenon of gravity. While Einstein's work has replaced Newton's as the dominant explanation of gravity in modern science, Newton's Law of Universal Gravitation is still used as it reasonably (and more simply) describes the force of gravity under many conditions. Similarly, the Law of Faunal Succession developed by William Smith does not explain why organisms follow each other in distinct, predictable ways in the rock layers, but it accurately describes the phenomenon.

Theories, hypotheses , and laws drive scientific progress

Theories, hypotheses , and laws are not simply important components of science, they drive scientific progress. For example, evolutionary biology now stands as a distinct field of science that focuses on the origins and descent of species . Geologists now rely on plate tectonics as a conceptual model and guiding theory when they are studying processes at work in Earth's crust . And physicists refer to atomic theory when they are predicting the existence of subatomic particles yet to be discovered. This does not mean that science is "finished," or that all of the important theories have been discovered already. Like evolution , progress in science happens both gradually and in short, dramatic bursts. Both types of progress are critical for creating a robust knowledge base with data as the foundation and scientific theories giving structure to that knowledge.

Table of Contents

• Theories, hypotheses, and laws drive scientific progress

Activate glossary term highlighting to easily identify key terms within the module. Once highlighted, you can click on these terms to view their definitions.

Activate NGSS annotations to easily identify NGSS standards within the module. Once highlighted, you can click on them to view these standards.