hypothesis in natural world

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

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Hypothesis n., plural: hypotheses [/haɪˈpɑːθəsɪs/] Definition: Testable scientific prediction

Table of Contents

What Is Hypothesis?

A scientific hypothesis is a foundational element of the scientific method . It’s a testable statement proposing a potential explanation for natural phenomena. The term hypothesis means “little theory” . A hypothesis is a short statement that can be tested and gives a possible reason for a phenomenon or a possible link between two variables . In the setting of scientific research, a hypothesis is a tentative explanation or statement that can be proven wrong and is used to guide experiments and empirical research.

It is an important part of the scientific method because it gives a basis for planning tests, gathering data, and judging evidence to see if it is true and could help us understand how natural things work. Several hypotheses can be tested in the real world, and the results of careful and systematic observation and analysis can be used to support, reject, or improve them.

Researchers and scientists often use the word hypothesis to refer to this educated guess . These hypotheses are firmly established based on scientific principles and the rigorous testing of new technology and experiments .

For example, in astrophysics, the Big Bang Theory is a working hypothesis that explains the origins of the universe and considers it as a natural phenomenon. It is among the most prominent scientific hypotheses in the field.

“The scientific method: steps, terms, and examples” by Scishow:

Biology definition: A hypothesis  is a supposition or tentative explanation for (a group of) phenomena, (a set of) facts, or a scientific inquiry that may be tested, verified or answered by further investigation or methodological experiment. It is like a scientific guess . It’s an idea or prediction that scientists make before they do experiments. They use it to guess what might happen and then test it to see if they were right. It’s like a smart guess that helps them learn new things. A scientific hypothesis that has been verified through scientific experiment and research may well be considered a scientific theory .

Etymology: The word “hypothesis” comes from the Greek word “hupothesis,” which means “a basis” or “a supposition.” It combines “hupo” (under) and “thesis” (placing). Synonym:   proposition; assumption; conjecture; postulate Compare:   theory See also: null hypothesis

Characteristics Of Hypothesis

A useful hypothesis must have the following qualities:

• It should never be written as a question.
• You should be able to test it in the real world to see if it’s right or wrong.
• It needs to be clear and exact.
• It should list the factors that will be used to figure out the relationship.
• It should only talk about one thing. You can make a theory in either a descriptive or form of relationship.
• It shouldn’t go against any natural rule that everyone knows is true. Verification will be done well with the tools and methods that are available.
• It should be written in as simple a way as possible so that everyone can understand it.
• It must explain what happened to make an answer necessary.
• It should be testable in a fair amount of time.
• It shouldn’t say different things.

Sources Of Hypothesis

Sources of hypothesis are:

• Patterns of similarity between the phenomenon under investigation and existing hypotheses.
• Insights derived from prior research, concurrent observations, and insights from opposing perspectives.
• The formulations are derived from accepted scientific theories and proposed by researchers.
• In research, it’s essential to consider hypothesis as different subject areas may require various hypotheses (plural form of hypothesis). Researchers also establish a significance level to determine the strength of evidence supporting a hypothesis.
• Individual cognitive processes also contribute to the formation of hypotheses.

One hypothesis is a tentative explanation for an observation or phenomenon. It is based on prior knowledge and understanding of the world, and it can be tested by gathering and analyzing data. Observed facts are the data that are collected to test a hypothesis. They can support or refute the hypothesis.

For example, the hypothesis that “eating more fruits and vegetables will improve your health” can be tested by gathering data on the health of people who eat different amounts of fruits and vegetables. If the people who eat more fruits and vegetables are healthier than those who eat less fruits and vegetables, then the hypothesis is supported.

Hypotheses are essential for scientific inquiry. They help scientists to focus their research, to design experiments, and to interpret their results. They are also essential for the development of scientific theories.

Types Of Hypothesis

In research, you typically encounter two types of hypothesis: the alternative hypothesis (which proposes a relationship between variables) and the null hypothesis (which suggests no relationship).

Simple Hypothesis

It illustrates the association between one dependent variable and one independent variable. For instance, if you consume more vegetables, you will lose weight more quickly. Here, increasing vegetable consumption is the independent variable, while weight loss is the dependent variable.

Complex Hypothesis

It exhibits the relationship between at least two dependent variables and at least two independent variables. Eating more vegetables and fruits results in weight loss, radiant skin, and a decreased risk of numerous diseases, including heart disease.

Directional Hypothesis

It shows that a researcher wants to reach a certain goal. The way the factors are related can also tell us about their nature. For example, four-year-old children who eat well over a time of five years have a higher IQ than children who don’t eat well. This shows what happened and how it happened.

Non-directional Hypothesis

When there is no theory involved, it is used. It is a statement that there is a connection between two variables, but it doesn’t say what that relationship is or which way it goes.

Null Hypothesis

It says something that goes against the theory. It’s a statement that says something is not true, and there is no link between the independent and dependent factors. “H 0 ” represents the null hypothesis.

Associative and Causal Hypothesis

When a change in one variable causes a change in the other variable, this is called the associative hypothesis . The causal hypothesis, on the other hand, says that there is a cause-and-effect relationship between two or more factors.

Examples Of Hypothesis

Examples of simple hypotheses:

• Students who consume breakfast before taking a math test will have a better overall performance than students who do not consume breakfast.
• Students who experience test anxiety before an English examination will get lower scores than students who do not experience test anxiety.
• Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone, is a statement that suggests that drivers who talk on the phone while driving are more likely to make mistakes.

Examples of a complex hypothesis:

• Individuals who consume a lot of sugar and don’t get much exercise are at an increased risk of developing depression.
• Younger people who are routinely exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces, according to a new study.
• Increased levels of air pollution led to higher rates of respiratory illnesses, which in turn resulted in increased costs for healthcare for the affected communities.

Examples of Directional Hypothesis:

• The crop yield will go up a lot if the amount of fertilizer is increased.
• Patients who have surgery and are exposed to more stress will need more time to get better.
• Increasing the frequency of brand advertising on social media will lead to a significant increase in brand awareness among the target audience.

Examples of Non-Directional Hypothesis (or Two-Tailed Hypothesis):

• The test scores of two groups of students are very different from each other.
• There is a link between gender and being happy at work.
• There is a correlation between the amount of caffeine an individual consumes and the speed with which they react.

Examples of a null hypothesis:

• Children who receive a new reading intervention will have scores that are different than students who do not receive the intervention.
• The results of a memory recall test will not reveal any significant gap in performance between children and adults.
• There is not a significant relationship between the number of hours spent playing video games and academic performance.

Examples of Associative Hypothesis:

• There is a link between how many hours you spend studying and how well you do in school.
• Drinking sugary drinks is bad for your health as a whole.
• There is an association between socioeconomic status and access to quality healthcare services in urban neighborhoods.

Functions Of Hypothesis

The research issue can be understood better with the help of a hypothesis, which is why developing one is crucial. The following are some of the specific roles that a hypothesis plays: (Rashid, Apr 20, 2022)

• A hypothesis gives a study a point of concentration. It enlightens us as to the specific characteristics of a study subject we need to look into.
• It instructs us on what data to acquire as well as what data we should not collect, giving the study a focal point .
• The development of a hypothesis improves objectivity since it enables the establishment of a focal point.
• A hypothesis makes it possible for us to contribute to the development of the theory. Because of this, we are in a position to definitively determine what is true and what is untrue .

How will Hypothesis help in the Scientific Method?

• The scientific method begins with observation and inquiry about the natural world when formulating research questions. Researchers can refine their observations and queries into specific, testable research questions with the aid of hypothesis. They provide an investigation with a focused starting point.
• Hypothesis generate specific predictions regarding the expected outcomes of experiments or observations. These forecasts are founded on the researcher’s current knowledge of the subject. They elucidate what researchers anticipate observing if the hypothesis is true.
• Hypothesis direct the design of experiments and data collection techniques. Researchers can use them to determine which variables to measure or manipulate, which data to obtain, and how to conduct systematic and controlled research.
• Following the formulation of a hypothesis and the design of an experiment, researchers collect data through observation, measurement, or experimentation. The collected data is used to verify the hypothesis’s predictions.
• Hypothesis establish the criteria for evaluating experiment results. The observed data are compared to the predictions generated by the hypothesis. This analysis helps determine whether empirical evidence supports or refutes the hypothesis.
• The results of experiments or observations are used to derive conclusions regarding the hypothesis. If the data support the predictions, then the hypothesis is supported. If this is not the case, the hypothesis may be revised or rejected, leading to the formulation of new queries and hypothesis.
• The scientific approach is iterative, resulting in new hypothesis and research issues from previous trials. This cycle of hypothesis generation, testing, and refining drives scientific progress.

Importance Of Hypothesis

• Hypothesis are testable statements that enable scientists to determine if their predictions are accurate. This assessment is essential to the scientific method, which is based on empirical evidence.
• Hypothesis serve as the foundation for designing experiments or data collection techniques. They can be used by researchers to develop protocols and procedures that will produce meaningful results.
• Hypothesis hold scientists accountable for their assertions. They establish expectations for what the research should reveal and enable others to assess the validity of the findings.
• Hypothesis aid in identifying the most important variables of a study. The variables can then be measured, manipulated, or analyzed to determine their relationships.
• Hypothesis assist researchers in allocating their resources efficiently. They ensure that time, money, and effort are spent investigating specific concerns, as opposed to exploring random concepts.
• Testing hypothesis contribute to the scientific body of knowledge. Whether or not a hypothesis is supported, the results contribute to our understanding of a phenomenon.
• Hypothesis can result in the creation of theories. When supported by substantive evidence, hypothesis can serve as the foundation for larger theoretical frameworks that explain complex phenomena.
• Beyond scientific research, hypothesis play a role in the solution of problems in a variety of domains. They enable professionals to make educated assumptions about the causes of problems and to devise solutions.

Research Hypotheses: Did you know that a hypothesis refers to an educated guess or prediction about the outcome of a research study?

It’s like a roadmap guiding researchers towards their destination of knowledge. Just like a compass points north, a well-crafted hypothesis points the way to valuable discoveries in the world of science and inquiry.

Choose the best answer. 

Send Your Results (Optional)

Further reading.

• RNA-DNA World Hypothesis
• BYJU’S. (2023). Hypothesis. Retrieved 01 Septermber 2023, from https://byjus.com/physics/hypothesis/#sources-of-hypothesis
• Collegedunia. (2023). Hypothesis. Retrieved 1 September 2023, from https://collegedunia.com/exams/hypothesis-science-articleid-7026#d
• Hussain, D. J. (2022). Hypothesis. Retrieved 01 September 2023, from https://mmhapu.ac.in/doc/eContent/Management/JamesHusain/Research%20Hypothesis%20-Meaning,%20Nature%20&%20Importance-Characteristics%20of%20Good%20%20Hypothesis%20Sem2.pdf
• Media, D. (2023). Hypothesis in the Scientific Method. Retrieved 01 September 2023, from https://www.verywellmind.com/what-is-a-hypothesis-2795239#toc-hypotheses-examples
• Rashid, M. H. A. (Apr 20, 2022). Research Methodology. Retrieved 01 September 2023, from https://limbd.org/hypothesis-definitions-functions-characteristics-types-errors-the-process-of-testing-a-hypothesis-hypotheses-in-qualitative-research/#:~:text=Functions%20of%20a%20Hypothesis%3A&text=Specifically%2C%20a%20hypothesis%20serves%20the,providing%20focus%20to%20the%20study.

©BiologyOnline.com. Content provided and moderated by Biology Online Editors.

Last updated on September 8th, 2023

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1.2 The Scientific Methods

Section learning objectives.

By the end of this section, you will be able to do the following:

• Explain how the methods of science are used to make scientific discoveries
• Define a scientific model and describe examples of physical and mathematical models used in physics
• Compare and contrast hypothesis, theory, and law

Teacher Support

The learning objectives in this section will help your students master the following standards:

• (A) know the definition of science and understand that it has limitations, as specified in subsection (b)(2) of this section;
• (B) know that scientific hypotheses are tentative and testable statements that must be capable of being supported or not supported by observational evidence. Hypotheses of durable explanatory power which have been tested over a wide variety of conditions are incorporated into theories;
• (C) know that scientific theories are based on natural and physical phenomena and are capable of being tested by multiple independent researchers. Unlike hypotheses, scientific theories are well-established and highly-reliable explanations, but may be subject to change as new areas of science and new technologies are developed;
• (D) distinguish between scientific hypotheses and scientific theories.

Section Key Terms

[OL] Pre-assessment for this section could involve students sharing or writing down an anecdote about when they used the methods of science. Then, students could label their thought processes in their anecdote with the appropriate scientific methods. The class could also discuss their definitions of theory and law, both outside and within the context of science.

[OL] It should be noted and possibly mentioned that a scientist , as mentioned in this section, does not necessarily mean a trained scientist. It could be anyone using methods of science.

Scientific Methods

Scientists often plan and carry out investigations to answer questions about the universe around us. These investigations may lead to natural laws. Such laws are intrinsic to the universe, meaning that humans did not create them and cannot change them. We can only discover and understand them. Their discovery is a very human endeavor, with all the elements of mystery, imagination, struggle, triumph, and disappointment inherent in any creative effort. The cornerstone of discovering natural laws is observation. Science must describe the universe as it is, not as we imagine or wish it to be.

We all are curious to some extent. We look around, make generalizations, and try to understand what we see. For example, we look up and wonder whether one type of cloud signals an oncoming storm. As we become serious about exploring nature, we become more organized and formal in collecting and analyzing data. We attempt greater precision, perform controlled experiments (if we can), and write down ideas about how data may be organized. We then formulate models, theories, and laws based on the data we have collected, and communicate those results with others. This, in a nutshell, describes the scientific method that scientists employ to decide scientific issues on the basis of evidence from observation and experiment.

An investigation often begins with a scientist making an observation . The scientist observes a pattern or trend within the natural world. Observation may generate questions that the scientist wishes to answer. Next, the scientist may perform some research about the topic and devise a hypothesis . A hypothesis is a testable statement that describes how something in the natural world works. In essence, a hypothesis is an educated guess that explains something about an observation.

[OL] An educated guess is used throughout this section in describing a hypothesis to combat the tendency to think of a theory as an educated guess.

Scientists may test the hypothesis by performing an experiment . During an experiment, the scientist collects data that will help them learn about the phenomenon they are studying. Then the scientists analyze the results of the experiment (that is, the data), often using statistical, mathematical, and/or graphical methods. From the data analysis, they draw conclusions. They may conclude that their experiment either supports or rejects their hypothesis. If the hypothesis is supported, the scientist usually goes on to test another hypothesis related to the first. If their hypothesis is rejected, they will often then test a new and different hypothesis in their effort to learn more about whatever they are studying.

Scientific processes can be applied to many situations. Let’s say that you try to turn on your car, but it will not start. You have just made an observation! You ask yourself, "Why won’t my car start?" You can now use scientific processes to answer this question. First, you generate a hypothesis such as, "The car won’t start because it has no gasoline in the gas tank." To test this hypothesis, you put gasoline in the car and try to start it again. If the car starts, then your hypothesis is supported by the experiment. If the car does not start, then your hypothesis is rejected. You will then need to think up a new hypothesis to test such as, "My car won’t start because the fuel pump is broken." Hopefully, your investigations lead you to discover why the car won’t start and enable you to fix it.

A model is a representation of something that is often too difficult (or impossible) to study directly. Models can take the form of physical models, equations, computer programs, or simulations—computer graphics/animations. Models are tools that are especially useful in modern physics because they let us visualize phenomena that we normally cannot observe with our senses, such as very small objects or objects that move at high speeds. For example, we can understand the structure of an atom using models, without seeing an atom with our own eyes. Although images of single atoms are now possible, these images are extremely difficult to achieve and are only possible due to the success of our models. The existence of these images is a consequence rather than a source of our understanding of atoms. Models are always approximate, so they are simpler to consider than the real situation; the more complete a model is, the more complicated it must be. Models put the intangible or the extremely complex into human terms that we can visualize, discuss, and hypothesize about.

Scientific models are constructed based on the results of previous experiments. Even still, models often only describe a phenomenon partially or in a few limited situations. Some phenomena are so complex that they may be impossible to model them in their entirety, even using computers. An example is the electron cloud model of the atom in which electrons are moving around the atom’s center in distinct clouds ( Figure 1.12 ), that represent the likelihood of finding an electron in different places. This model helps us to visualize the structure of an atom. However, it does not show us exactly where an electron will be within its cloud at any one particular time.

As mentioned previously, physicists use a variety of models including equations, physical models, computer simulations, etc. For example, three-dimensional models are often commonly used in chemistry and physics to model molecules. Properties other than appearance or location are usually modelled using mathematics, where functions are used to show how these properties relate to one another. Processes such as the formation of a star or the planets, can also be modelled using computer simulations. Once a simulation is correctly programmed based on actual experimental data, the simulation can allow us to view processes that happened in the past or happen too quickly or slowly for us to observe directly. In addition, scientists can also run virtual experiments using computer-based models. In a model of planet formation, for example, the scientist could alter the amount or type of rocks present in space and see how it affects planet formation.

Scientists use models and experimental results to construct explanations of observations or design solutions to problems. For example, one way to make a car more fuel efficient is to reduce the friction or drag caused by air flowing around the moving car. This can be done by designing the body shape of the car to be more aerodynamic, such as by using rounded corners instead of sharp ones. Engineers can then construct physical models of the car body, place them in a wind tunnel, and examine the flow of air around the model. This can also be done mathematically in a computer simulation. The air flow pattern can be analyzed for regions smooth air flow and for eddies that indicate drag. The model of the car body may have to be altered slightly to produce the smoothest pattern of air flow (i.e., the least drag). The pattern with the least drag may be the solution to increasing fuel efficiency of the car. This solution might then be incorporated into the car design.

Using Models and the Scientific Processes

Be sure to secure loose items before opening the window or door.

In this activity, you will learn about scientific models by making a model of how air flows through your classroom or a room in your house.

• One room with at least one window or door that can be opened
• Work with a group of four, as directed by your teacher. Close all of the windows and doors in the room you are working in. Your teacher may assign you a specific window or door to study.
• Before opening any windows or doors, draw a to-scale diagram of your room. First, measure the length and width of your room using the tape measure. Then, transform the measurement using a scale that could fit on your paper, such as 5 centimeters = 1 meter.
• Your teacher will assign you a specific window or door to study air flow. On your diagram, add arrows showing your hypothesis (before opening any windows or doors) of how air will flow through the room when your assigned window or door is opened. Use pencil so that you can easily make changes to your diagram.
• On your diagram, mark four locations where you would like to test air flow in your room. To test for airflow, hold a strip of single ply tissue paper between the thumb and index finger. Note the direction that the paper moves when exposed to the airflow. Then, for each location, predict which way the paper will move if your air flow diagram is correct.
• Now, each member of your group will stand in one of the four selected areas. Each member will test the airflow Agree upon an approximate height at which everyone will hold their papers.
• When you teacher tells you to, open your assigned window and/or door. Each person should note the direction that their paper points immediately after the window or door was opened. Record your results on your diagram.
• Did the airflow test data support or refute the hypothetical model of air flow shown in your diagram? Why or why not? Correct your model based on your experimental evidence.
• With your group, discuss how accurate your model is. What limitations did it have? Write down the limitations that your group agreed upon.
• Yes, you could use your model to predict air flow through a new window. The earlier experiment of air flow would help you model the system more accurately.
• Yes, you could use your model to predict air flow through a new window. The earlier experiment of air flow is not useful for modeling the new system.
• No, you cannot model a system to predict the air flow through a new window. The earlier experiment of air flow would help you model the system more accurately.
• No, you cannot model a system to predict the air flow through a new window. The earlier experiment of air flow is not useful for modeling the new system.

This Snap Lab! has students construct a model of how air flows in their classroom. Each group of four students will create a model of air flow in their classroom using a scale drawing of the room. Then, the groups will test the validity of their model by placing weathervanes that they have constructed around the room and opening a window or door. By observing the weather vanes, students will see how air actually flows through the room from a specific window or door. Students will then correct their model based on their experimental evidence. The following material list is given per group:

• One room with at least one window or door that can be opened (An optimal configuration would be one window or door per group.)
• Several pieces of construction paper (at least four per group)
• Strips of single ply tissue paper
• One tape measure (long enough to measure the dimensions of the room)
• Group size can vary depending on the number of windows/doors available and the number of students in the class.
• The room dimensions could be provided by the teacher. Also, students may need a brief introduction in how to make a drawing to scale.
• This is another opportunity to discuss controlled experiments in terms of why the students should hold the strips of tissue paper at the same height and in the same way. One student could also serve as a control and stand far away from the window/door or in another area that will not receive air flow from the window/door.
• You will probably need to coordinate this when multiple windows or doors are used. Only one window or door should be opened at a time for best results. Between openings, allow a short period (5 minutes) when all windows and doors are closed, if possible.

Answers to the Grasp Check will vary, but the air flow in the new window or door should be based on what the students observed in their experiment.

Scientific Laws and Theories

A scientific law is a description of a pattern in nature that is true in all circumstances that have been studied. That is, physical laws are meant to be universal , meaning that they apply throughout the known universe. Laws are often also concise, whereas theories are more complicated. A law can be expressed in the form of a single sentence or mathematical equation. For example, Newton’s second law of motion , which relates the motion of an object to the force applied ( F ), the mass of the object ( m ), and the object’s acceleration ( a ), is simply stated using the equation

Scientific ideas and explanations that are true in many, but not all situations in the universe are usually called principles . An example is Pascal’s principle , which explains properties of liquids, but not solids or gases. However, the distinction between laws and principles is sometimes not carefully made in science.

A theory is an explanation for patterns in nature that is supported by much scientific evidence and verified multiple times by multiple researchers. While many people confuse theories with educated guesses or hypotheses, theories have withstood more rigorous testing and verification than hypotheses.

[OL] Explain to students that in informal, everyday English the word theory can be used to describe an idea that is possibly true but that has not been proven to be true. This use of the word theory often leads people to think that scientific theories are nothing more than educated guesses. This is not just a misconception among students, but among the general public as well.

As a closing idea about scientific processes, we want to point out that scientific laws and theories, even those that have been supported by experiments for centuries, can still be changed by new discoveries. This is especially true when new technologies emerge that allow us to observe things that were formerly unobservable. Imagine how viewing previously invisible objects with a microscope or viewing Earth for the first time from space may have instantly changed our scientific theories and laws! What discoveries still await us in the future? The constant retesting and perfecting of our scientific laws and theories allows our knowledge of nature to progress. For this reason, many scientists are reluctant to say that their studies prove anything. By saying support instead of prove , it keeps the door open for future discoveries, even if they won’t occur for centuries or even millennia.

[OL] With regard to scientists avoiding using the word prove , the general public knows that science has proven certain things such as that the heart pumps blood and the Earth is round. However, scientists should shy away from using prove because it is impossible to test every single instance and every set of conditions in a system to absolutely prove anything. Using support or similar terminology leaves the door open for further discovery.

Check Your Understanding

• Models are simpler to analyze.
• Models give more accurate results.
• Models provide more reliable predictions.
• Models do not require any computer calculations.
• They are the same.
• A hypothesis has been thoroughly tested and found to be true.
• A hypothesis is a tentative assumption based on what is already known.
• A hypothesis is a broad explanation firmly supported by evidence.
• A scientific model is a representation of something that can be easily studied directly. It is useful for studying things that can be easily analyzed by humans.
• A scientific model is a representation of something that is often too difficult to study directly. It is useful for studying a complex system or systems that humans cannot observe directly.
• A scientific model is a representation of scientific equipment. It is useful for studying working principles of scientific equipment.
• A scientific model is a representation of a laboratory where experiments are performed. It is useful for studying requirements needed inside the laboratory.
• The hypothesis must be validated by scientific experiments.
• The hypothesis must not include any physical quantity.
• The hypothesis must be a short and concise statement.
• The hypothesis must apply to all the situations in the universe.
• A scientific theory is an explanation of natural phenomena that is supported by evidence.
• A scientific theory is an explanation of natural phenomena without the support of evidence.
• A scientific theory is an educated guess about the natural phenomena occurring in nature.
• A scientific theory is an uneducated guess about natural phenomena occurring in nature.
• A hypothesis is an explanation of the natural world with experimental support, while a scientific theory is an educated guess about a natural phenomenon.
• A hypothesis is an educated guess about natural phenomenon, while a scientific theory is an explanation of natural world with experimental support.
• A hypothesis is experimental evidence of a natural phenomenon, while a scientific theory is an explanation of the natural world with experimental support.
• A hypothesis is an explanation of the natural world with experimental support, while a scientific theory is experimental evidence of a natural phenomenon.

Use the Check Your Understanding questions to assess students’ achievement of the section’s learning objectives. If students are struggling with a specific objective, the Check Your Understanding will help identify which objective and direct students to the relevant content.

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Want to cite, share, or modify this book? This book uses the Creative Commons Attribution License and you must attribute Texas Education Agency (TEA). The original material is available at: https://www.texasgateway.org/book/tea-physics . Changes were made to the original material, including updates to art, structure, and other content updates.

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Science and the scientific method: Definitions and examples

Here's a look at the foundation of doing science — the scientific method.

The scientific method

Hypothesis, theory and law, a brief history of science, additional resources, bibliography.

Science is a systematic and logical approach to discovering how things in the universe work. It is also the body of knowledge accumulated through the discoveries about all the things in the universe. 

The word "science" is derived from the Latin word "scientia," which means knowledge based on demonstrable and reproducible data, according to the Merriam-Webster dictionary . True to this definition, science aims for measurable results through testing and analysis, a process known as the scientific method. Science is based on fact, not opinion or preferences. The process of science is designed to challenge ideas through research. One important aspect of the scientific process is that it focuses only on the natural world, according to the University of California, Berkeley . Anything that is considered supernatural, or beyond physical reality, does not fit into the definition of science.

When conducting research, scientists use the scientific method to collect measurable, empirical evidence in an experiment related to a hypothesis (often in the form of an if/then statement) that is designed to support or contradict a scientific theory .

"As a field biologist, my favorite part of the scientific method is being in the field collecting the data," Jaime Tanner, a professor of biology at Marlboro College, told Live Science. "But what really makes that fun is knowing that you are trying to answer an interesting question. So the first step in identifying questions and generating possible answers (hypotheses) is also very important and is a creative process. Then once you collect the data you analyze it to see if your hypothesis is supported or not."

The steps of the scientific method go something like this, according to Highline College :

• Make an observation or observations.
• Form a hypothesis — a tentative description of what's been observed, and make predictions based on that hypothesis.
• Test the hypothesis and predictions in an experiment that can be reproduced.
• Analyze the data and draw conclusions; accept or reject the hypothesis or modify the hypothesis if necessary.
• Reproduce the experiment until there are no discrepancies between observations and theory. "Replication of methods and results is my favorite step in the scientific method," Moshe Pritsker, a former post-doctoral researcher at Harvard Medical School and CEO of JoVE, told Live Science. "The reproducibility of published experiments is the foundation of science. No reproducibility — no science."

Some key underpinnings to the scientific method:

• The hypothesis must be testable and falsifiable, according to North Carolina State University . Falsifiable means that there must be a possible negative answer to the hypothesis.
• Research must involve deductive reasoning and inductive reasoning . Deductive reasoning is the process of using true premises to reach a logical true conclusion while inductive reasoning uses observations to infer an explanation for those observations.
• An experiment should include a dependent variable (which does not change) and an independent variable (which does change), according to the University of California, Santa Barbara .
• An experiment should include an experimental group and a control group. The control group is what the experimental group is compared against, according to Britannica .

The process of generating and testing a hypothesis forms the backbone of the scientific method. When an idea has been confirmed over many experiments, it can be called a scientific theory. While a theory provides an explanation for a phenomenon, a scientific law provides a description of a phenomenon, according to The University of Waikato . One example would be the law of conservation of energy, which is the first law of thermodynamics that says that energy can neither be created nor destroyed. 

A law describes an observed phenomenon, but it doesn't explain why the phenomenon exists or what causes it. "In science, laws are a starting place," said Peter Coppinger, an associate professor of biology and biomedical engineering at the Rose-Hulman Institute of Technology. "From there, scientists can then ask the questions, 'Why and how?'"

Laws are generally considered to be without exception, though some laws have been modified over time after further testing found discrepancies. For instance, Newton's laws of motion describe everything we've observed in the macroscopic world, but they break down at the subatomic level.

This does not mean theories are not meaningful. For a hypothesis to become a theory, scientists must conduct rigorous testing, typically across multiple disciplines by separate groups of scientists. Saying something is "just a theory" confuses the scientific definition of "theory" with the layperson's definition. To most people a theory is a hunch. In science, a theory is the framework for observations and facts, Tanner told Live Science.

The earliest evidence of science can be found as far back as records exist. Early tablets contain numerals and information about the solar system , which were derived by using careful observation, prediction and testing of those predictions. Science became decidedly more "scientific" over time, however.

1200s: Robert Grosseteste developed the framework for the proper methods of modern scientific experimentation, according to the Stanford Encyclopedia of Philosophy. His works included the principle that an inquiry must be based on measurable evidence that is confirmed through testing.

1400s: Leonardo da Vinci began his notebooks in pursuit of evidence that the human body is microcosmic. The artist, scientist and mathematician also gathered information about optics and hydrodynamics.

1500s: Nicolaus Copernicus advanced the understanding of the solar system with his discovery of heliocentrism. This is a model in which Earth and the other planets revolve around the sun, which is the center of the solar system.

1600s: Johannes Kepler built upon those observations with his laws of planetary motion. Galileo Galilei improved on a new invention, the telescope, and used it to study the sun and planets. The 1600s also saw advancements in the study of physics as Isaac Newton developed his laws of motion.

1700s: Benjamin Franklin discovered that lightning is electrical. He also contributed to the study of oceanography and meteorology. The understanding of chemistry also evolved during this century as Antoine Lavoisier, dubbed the father of modern chemistry , developed the law of conservation of mass.

1800s: Milestones included Alessandro Volta's discoveries regarding electrochemical series, which led to the invention of the battery. John Dalton also introduced atomic theory, which stated that all matter is composed of atoms that combine to form molecules. The basis of modern study of genetics advanced as Gregor Mendel unveiled his laws of inheritance. Later in the century, Wilhelm Conrad Röntgen discovered X-rays , while George Ohm's law provided the basis for understanding how to harness electrical charges.

1900s: The discoveries of Albert Einstein , who is best known for his theory of relativity, dominated the beginning of the 20th century. Einstein's theory of relativity is actually two separate theories. His special theory of relativity, which he outlined in a 1905 paper, " The Electrodynamics of Moving Bodies ," concluded that time must change according to the speed of a moving object relative to the frame of reference of an observer. His second theory of general relativity, which he published as " The Foundation of the General Theory of Relativity ," advanced the idea that matter causes space to curve.

In 1952, Jonas Salk developed the polio vaccine , which reduced the incidence of polio in the United States by nearly 90%, according to Britannica . The following year, James D. Watson and Francis Crick discovered the structure of DNA , which is a double helix formed by base pairs attached to a sugar-phosphate backbone, according to the National Human Genome Research Institute .

2000s: The 21st century saw the first draft of the human genome completed, leading to a greater understanding of DNA. This advanced the study of genetics, its role in human biology and its use as a predictor of diseases and other disorders, according to the National Human Genome Research Institute .

• This video from City University of New York delves into the basics of what defines science.
• Learn about what makes science science in this book excerpt from Washington State University .
• This resource from the University of Michigan — Flint explains how to design your own scientific study.

Merriam-Webster Dictionary, Scientia. 2022. https://www.merriam-webster.com/dictionary/scientia

University of California, Berkeley, "Understanding Science: An Overview." 2022. ​​ https://undsci.berkeley.edu/article/0_0_0/intro_01  

Highline College, "Scientific method." July 12, 2015. https://people.highline.edu/iglozman/classes/astronotes/scimeth.htm  

North Carolina State University, "Science Scripts." https://projects.ncsu.edu/project/bio183de/Black/science/science_scripts.html  

University of California, Santa Barbara. "What is an Independent variable?" October 31,2017. http://scienceline.ucsb.edu/getkey.php?key=6045  

Encyclopedia Britannica, "Control group." May 14, 2020. https://www.britannica.com/science/control-group  

The University of Waikato, "Scientific Hypothesis, Theories and Laws." https://sci.waikato.ac.nz/evolution/Theories.shtml  

Stanford Encyclopedia of Philosophy, Robert Grosseteste. May 3, 2019. https://plato.stanford.edu/entries/grosseteste/  

Encyclopedia Britannica, "Jonas Salk." October 21, 2021. https://www.britannica.com/ biography /Jonas-Salk

National Human Genome Research Institute, "​Phosphate Backbone." https://www.genome.gov/genetics-glossary/Phosphate-Backbone  

National Human Genome Research Institute, "What is the Human Genome Project?" https://www.genome.gov/human-genome-project/What  

‌ Live Science contributor Ashley Hamer updated this article on Jan. 16, 2022.

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What Is a Hypothesis? (Science)

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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
• What Are the Elements of a Good Hypothesis?
• Six Steps of the Scientific Method
• What Are Examples of a Hypothesis?
• What Is a Testable Hypothesis?
• Null Hypothesis Examples
• Scientific Hypothesis Examples
• Scientific Variable
• Scientific Method Vocabulary Terms
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• What Is a Controlled Experiment?
• What Is an Experimental Constant?
• What Is the Difference Between a Control Variable and Control Group?
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• Random Error vs. Systematic Error
• The Role of a Controlled Variable in an Experiment

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

Verywell / Alex Dos Diaz

• The Scientific Method

Hypothesis Format

Falsifiability of a hypothesis.

• Operationalization

Hypothesis Types

Hypotheses examples.

• Collecting Data

A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process.

Consider a study designed to examine the relationship between sleep deprivation and test performance. The hypothesis might be: "This study is designed to assess the hypothesis that sleep-deprived people will perform worse on a test than individuals who are not sleep-deprived."

At a Glance

A hypothesis is crucial to scientific research because it offers a clear direction for what the researchers are looking to find. This allows them to design experiments to test their predictions and add to our scientific knowledge about the world. This article explores how a hypothesis is used in psychology research, how to write a good hypothesis, and the different types of hypotheses you might use.

The Hypothesis in the Scientific Method

In the scientific method , whether it involves research in psychology, biology, or some other area, a hypothesis represents what the researchers think will happen in an experiment. The scientific method involves the following steps:

• Forming a question
• Performing background research
• Creating a hypothesis
• Designing an experiment
• Collecting data
• Analyzing the results
• Drawing conclusions
• Communicating the results

The hypothesis is a prediction, but it involves more than a guess. Most of the time, the hypothesis begins with a question which is then explored through background research. At this point, researchers then begin to develop a testable hypothesis.

Unless you are creating an exploratory study, your hypothesis should always explain what you  expect  to happen.

In a study exploring the effects of a particular drug, the hypothesis might be that researchers expect the drug to have some type of effect on the symptoms of a specific illness. In psychology, the hypothesis might focus on how a certain aspect of the environment might influence a particular behavior.

Remember, a hypothesis does not have to be correct. While the hypothesis predicts what the researchers expect to see, the goal of the research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore numerous factors to determine which ones might contribute to the ultimate outcome.

In many cases, researchers may find that the results of an experiment  do not  support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.

In many cases, researchers might draw a hypothesis from a specific theory or build on previous research. For example, prior research has shown that stress can impact the immune system. So a researcher might hypothesize: "People with high-stress levels will be more likely to contract a common cold after being exposed to the virus than people who have low-stress levels."

In other instances, researchers might look at commonly held beliefs or folk wisdom. "Birds of a feather flock together" is one example of folk adage that a psychologist might try to investigate. The researcher might pose a specific hypothesis that "People tend to select romantic partners who are similar to them in interests and educational level."

Elements of a Good Hypothesis

So how do you write a good hypothesis? When trying to come up with a hypothesis for your research or experiments, ask yourself the following questions:

• Is your hypothesis based on your research on a topic?
• Can your hypothesis be tested?
• Does your hypothesis include independent and dependent variables?

Before you come up with a specific hypothesis, spend some time doing background research. Once you have completed a literature review, start thinking about potential questions you still have. Pay attention to the discussion section in the  journal articles you read . Many authors will suggest questions that still need to be explored.

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

• Collect as many observations about a topic or problem as you can.
• Evaluate these observations and look for possible causes of the problem.
• Create a list of possible explanations that you might want to explore.
• After you have developed some possible hypotheses, think of ways that you could confirm or disprove each hypothesis through experimentation. This is known as falsifiability.

In the scientific method ,  falsifiability is an important part of any valid hypothesis. In order to test a claim scientifically, it must be possible that the claim could be proven false.

Students sometimes confuse the idea of falsifiability with the idea that it means that something is false, which is not the case. What falsifiability means is that  if  something was false, then it is possible to demonstrate that it is false.

One of the hallmarks of pseudoscience is that it makes claims that cannot be refuted or proven false.

The Importance of Operational Definitions

A variable is a factor or element that can be changed and manipulated in ways that are observable and measurable. However, the researcher must also define how the variable will be manipulated and measured in the study.

Operational definitions are specific definitions for all relevant factors in a study. This process helps make vague or ambiguous concepts detailed and measurable.

For example, a researcher might operationally define the variable " test anxiety " as the results of a self-report measure of anxiety experienced during an exam. A "study habits" variable might be defined by the amount of studying that actually occurs as measured by time.

These precise descriptions are important because many things can be measured in various ways. Clearly defining these variables and how they are measured helps ensure that other researchers can replicate your results.

Replicability

One of the basic principles of any type of scientific research is that the results must be replicable.

Replication means repeating an experiment in the same way to produce the same results. By clearly detailing the specifics of how the variables were measured and manipulated, other researchers can better understand the results and repeat the study if needed.

Some variables are more difficult than others to define. For example, how would you operationally define a variable such as aggression ? For obvious ethical reasons, researchers cannot create a situation in which a person behaves aggressively toward others.

To measure this variable, the researcher must devise a measurement that assesses aggressive behavior without harming others. The researcher might utilize a simulated task to measure aggressiveness in this situation.

Hypothesis Checklist

• Does your hypothesis focus on something that you can actually test?
• Does your hypothesis include both an independent and dependent variable?
• Can you manipulate the variables?
• Can your hypothesis be tested without violating ethical standards?

The hypothesis you use will depend on what you are investigating and hoping to find. Some of the main types of hypotheses that you might use include:

• Simple hypothesis : This type of hypothesis suggests there is a relationship between one independent variable and one dependent variable.
• Complex hypothesis : This type suggests a relationship between three or more variables, such as two independent and dependent variables.
• Null hypothesis : This hypothesis suggests no relationship exists between two or more variables.
• Alternative hypothesis : This hypothesis states the opposite of the null hypothesis.
• Statistical hypothesis : This hypothesis uses statistical analysis to evaluate a representative population sample and then generalizes the findings to the larger group.
• Logical hypothesis : This hypothesis assumes a relationship between variables without collecting data or evidence.

A hypothesis often follows a basic format of "If {this happens} then {this will happen}." One way to structure your hypothesis is to describe what will happen to the  dependent variable  if you change the  independent variable .

The basic format might be: "If {these changes are made to a certain independent variable}, then we will observe {a change in a specific dependent variable}."

A few examples of simple hypotheses:

• "Students who eat breakfast will perform better on a math exam than students who do not eat breakfast."
• "Students who experience test anxiety before an English exam will get lower scores than students who do not experience test anxiety."​
• "Motorists who talk on the phone while driving will be more likely to make errors on a driving course than those who do not talk on the phone."
• "Children who receive a new reading intervention will have higher reading scores than students who do not receive the intervention."

Examples of a complex hypothesis include:

• "People with high-sugar diets and sedentary activity levels are more likely to develop depression."
• "Younger people who are regularly exposed to green, outdoor areas have better subjective well-being than older adults who have limited exposure to green spaces."

Examples of a null hypothesis include:

• "There is no difference in anxiety levels between people who take St. John's wort supplements and those who do not."
• "There is no difference in scores on a memory recall task between children and adults."
• "There is no difference in aggression levels between children who play first-person shooter games and those who do not."

Examples of an alternative hypothesis:

• "People who take St. John's wort supplements will have less anxiety than those who do not."
• "Adults will perform better on a memory task than children."
• "Children who play first-person shooter games will show higher levels of aggression than children who do not." 

Collecting Data on Your Hypothesis

Once a researcher has formed a testable hypothesis, the next step is to select a research design and start collecting data. The research method depends largely on exactly what they are studying. There are two basic types of research methods: descriptive research and experimental research.

Descriptive Research Methods

Descriptive research such as  case studies ,  naturalistic observations , and surveys are often used when  conducting an experiment is difficult or impossible. These methods are best used to describe different aspects of a behavior or psychological phenomenon.

Once a researcher has collected data using descriptive methods, a  correlational study  can examine how the variables are related. This research method might be used to investigate a hypothesis that is difficult to test experimentally.

Experimental Research Methods

Experimental methods  are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable).

Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship—whether changes in one variable actually  cause  another to change.

The hypothesis is a critical part of any scientific exploration. It represents what researchers expect to find in a study or experiment. In situations where the hypothesis is unsupported by the research, the research still has value. Such research helps us better understand how different aspects of the natural world relate to one another. It also helps us develop new hypotheses that can then be tested in the future.

Thompson WH, Skau S. On the scope of scientific hypotheses .  R Soc Open Sci . 2023;10(8):230607. doi:10.1098/rsos.230607

Taran S, Adhikari NKJ, Fan E. Falsifiability in medicine: what clinicians can learn from Karl Popper [published correction appears in Intensive Care Med. 2021 Jun 17;:].  Intensive Care Med . 2021;47(9):1054-1056. doi:10.1007/s00134-021-06432-z

Eyler AA. Research Methods for Public Health . 1st ed. Springer Publishing Company; 2020. doi:10.1891/9780826182067.0004

Nosek BA, Errington TM. What is replication ?  PLoS Biol . 2020;18(3):e3000691. doi:10.1371/journal.pbio.3000691

Aggarwal R, Ranganathan P. Study designs: Part 2 - Descriptive studies .  Perspect Clin Res . 2019;10(1):34-36. doi:10.4103/picr.PICR_154_18

Nevid J. Psychology: Concepts and Applications. Wadworth, 2013.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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Understanding Science

How science REALLY works...

• Understanding Science 101
• Misconceptions
• Science aims to build knowledge about the natural world.
• This knowledge is open to question and revision as we come up with new ideas and discover new evidence.
• Because it has been tested, scientific knowledge is reliable.

Misconception:  Scientific ideas are absolute and unchanging.

Misconception:  Because scientific ideas are tentative and subject to change, they can’t be trusted.

Correction:  Accepted scientific ideas are well-supported and reliable, but could be revised if warranted by the evidence.  Read more about it.

Science aims to explain and understand

The knowledge that is built by science is always open to question and revision. No scientific idea is ever once-and-for-all “proven” Why not? Well, science is constantly seeking new  evidence , which could reveal problems with our current understandings. Ideas that we fully  accept  today may be rejected or modified in light of new evidence discovered tomorrow. For example, up until 1938, paleontologists accepted the idea that coelacanths (an ancient fish) went extinct at the time that they last appear in the fossil record — about 80 million years ago. But that year, a live coelacanth was discovered off the coast of South Africa, causing scientists to revise their ideas and to investigate how this animal survives in the deep sea.

Despite the fact that they are subject to change, scientific ideas are reliable. The ideas that have gained scientific acceptance have done so because they are supported by many lines of evidence and have generated many expectations that hold true. Such scientific ideas allow us to figure out how entities in the natural world are likely to behave (e.g., how likely it is that a child will inherit a particular genetic disease) and how we can harness that understanding to solve problems (e.g., how electricity, wire, glass, and various compounds can be fashioned into a working light bulb). For example, scientific understandings of motion and gases allow us to build airplanes that reliably get us from one airport to the next. Though the knowledge used to design airplanes could be modified and built upon, it is also reliable. Time and time again, that knowledge has allowed us to produce airplanes that fly. We have good reason to trust accepted scientific ideas: they work!

A SCIENCE PROTOTYPE: RUTHERFORD AND THE ATOM

Ernest Rutherford’s investigations were aimed at understanding a small, but illuminating, corner of the natural world: the atom. He investigated this world using alpha particles, which are helium atoms stripped of their electrons. Rutherford had found that when a beam of these tiny, positively-charged alpha particles is fired through gold foil, the particles don’t stay on their beeline course, but change direction when passing through the foil. Rutherford wanted to figure out what this might tell him about the layout of an atom.

Rutherford’s story continues as we examine each item on the Science Checklist. To find out how this investigation measures up against the rest of the checklist, read on.

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Science builds reliable knowledge, but does it seek the truth? Learn more in our side trip on  The many meanings of truth .

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Part I: Humans and the Ecological Environment

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 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 (Figure 2.1). Consider the following example of inductive logic, applied to an environmental topic:

• Observation 1: Marine mammals off the Atlantic coast of North America have large residues of DDT and other chlorinated hydrocarbons in their fat and other body tissues.
• Observation 2: So do marine mammals off the Pacific coast.
• 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 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 example 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 19). 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.

Figure 2.1 Deductive and Inductive Reasoning in Science. Making sense of the natural world begins with observations. Left) As we collect observations of the world, we can begin to make general predictions (or perceptions) regarding phenomena. This process is known as inductive reasoning, making general predictions from specific phenomena. From these generalized perceptions of reality, specific predictions can be deduced using logic, generating hypotheses. Middle) Experimentation allows researchers to test the predictions of the hypotheses. If a hypothesis is falsified, that is another observation which adds to our general perception of reality. Right) As more and more similar but different experiments reinforce a specific prediction, growing support emerges for the development of a scientific theory, another example of inductive reasoning. In turn, a theory can assist in the development of additional, untested hypotheses using deductive reasoning. S ource: used with permission from Jason Walker, The Biolog y Primer .  

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 3 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 2.2). 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 2.2. 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 used 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. B. Freeman.

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 3), 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 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 North America 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 variety 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 26). 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.

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 centimeter. 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. 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 the United States in 2020 was 330 million people, which uses three significant figures. However, the population should not be reported as 330,000,000, which implies an unrealistic accuracy and precision of nine significant figures.

A Need for Skepticism

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.

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). 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.

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 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 Erie. 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 0.15 ppm of mercury, the upper level of contamination recommended by the United States Environmental Protection Agency 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 Erie’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. Authored by : Bill Freedman. Provided by : Dalhousie University. Located at : https://digitaleditions.library.dal.ca/environmentalscience/ . License : CC BY-NC: Attribution-NonCommercial

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AP®︎/College Biology

Course: ap®︎/college biology   >   unit 7.

• Earth formation
• Beginnings of life
• Origins of life
• Hypotheses about the origins of life
• The RNA origin of life
• Origins of life on Earth

What is a Hypothesis – Types, Examples and Writing Guide

Table of Contents

Definition:

Hypothesis is an educated guess or proposed explanation for a phenomenon, based on some initial observations or data. It is a tentative statement that can be tested and potentially proven or disproven through further investigation and experimentation.

Hypothesis is often used in scientific research to guide the design of experiments and the collection and analysis of data. It is an essential element of the scientific method, as it allows researchers to make predictions about the outcome of their experiments and to test those predictions to determine their accuracy.

Types of Hypothesis

Types of Hypothesis are as follows:

Research Hypothesis

A research hypothesis is a statement that predicts a relationship between variables. It is usually formulated as a specific statement that can be tested through research, and it is often used in scientific research to guide the design of experiments.

Null Hypothesis

The null hypothesis is a statement that assumes there is no significant difference or relationship between variables. It is often used as a starting point for testing the research hypothesis, and if the results of the study reject the null hypothesis, it suggests that there is a significant difference or relationship between variables.

Alternative Hypothesis

An alternative hypothesis is a statement that assumes there is a significant difference or relationship between variables. It is often used as an alternative to the null hypothesis and is tested against the null hypothesis to determine which statement is more accurate.

Directional Hypothesis

A directional hypothesis is a statement that predicts the direction of the relationship between variables. For example, a researcher might predict that increasing the amount of exercise will result in a decrease in body weight.

Non-directional Hypothesis

A non-directional hypothesis is a statement that predicts the relationship between variables but does not specify the direction. For example, a researcher might predict that there is a relationship between the amount of exercise and body weight, but they do not specify whether increasing or decreasing exercise will affect body weight.

Statistical Hypothesis

A statistical hypothesis is a statement that assumes a particular statistical model or distribution for the data. It is often used in statistical analysis to test the significance of a particular result.

Composite Hypothesis

A composite hypothesis is a statement that assumes more than one condition or outcome. It can be divided into several sub-hypotheses, each of which represents a different possible outcome.

Empirical Hypothesis

An empirical hypothesis is a statement that is based on observed phenomena or data. It is often used in scientific research to develop theories or models that explain the observed phenomena.

Simple Hypothesis

A simple hypothesis is a statement that assumes only one outcome or condition. It is often used in scientific research to test a single variable or factor.

Complex Hypothesis

A complex hypothesis is a statement that assumes multiple outcomes or conditions. It is often used in scientific research to test the effects of multiple variables or factors on a particular outcome.

Applications of Hypothesis

Hypotheses are used in various fields to guide research and make predictions about the outcomes of experiments or observations. Here are some examples of how hypotheses are applied in different fields:

• Science : In scientific research, hypotheses are used to test the validity of theories and models that explain natural phenomena. For example, a hypothesis might be formulated to test the effects of a particular variable on a natural system, such as the effects of climate change on an ecosystem.
• Medicine : In medical research, hypotheses are used to test the effectiveness of treatments and therapies for specific conditions. For example, a hypothesis might be formulated to test the effects of a new drug on a particular disease.
• Psychology : In psychology, hypotheses are used to test theories and models of human behavior and cognition. For example, a hypothesis might be formulated to test the effects of a particular stimulus on the brain or behavior.
• Sociology : In sociology, hypotheses are used to test theories and models of social phenomena, such as the effects of social structures or institutions on human behavior. For example, a hypothesis might be formulated to test the effects of income inequality on crime rates.
• Business : In business research, hypotheses are used to test the validity of theories and models that explain business phenomena, such as consumer behavior or market trends. For example, a hypothesis might be formulated to test the effects of a new marketing campaign on consumer buying behavior.
• Engineering : In engineering, hypotheses are used to test the effectiveness of new technologies or designs. For example, a hypothesis might be formulated to test the efficiency of a new solar panel design.

How to write a Hypothesis

Here are the steps to follow when writing a hypothesis:

Identify the Research Question

The first step is to identify the research question that you want to answer through your study. This question should be clear, specific, and focused. It should be something that can be investigated empirically and that has some relevance or significance in the field.

Conduct a Literature Review

Before writing your hypothesis, it’s essential to conduct a thorough literature review to understand what is already known about the topic. This will help you to identify the research gap and formulate a hypothesis that builds on existing knowledge.

Determine the Variables

The next step is to identify the variables involved in the research question. A variable is any characteristic or factor that can vary or change. There are two types of variables: independent and dependent. The independent variable is the one that is manipulated or changed by the researcher, while the dependent variable is the one that is measured or observed as a result of the independent variable.

Formulate the Hypothesis

Based on the research question and the variables involved, you can now formulate your hypothesis. A hypothesis should be a clear and concise statement that predicts the relationship between the variables. It should be testable through empirical research and based on existing theory or evidence.

Write the Null Hypothesis

The null hypothesis is the opposite of the alternative hypothesis, which is the hypothesis that you are testing. The null hypothesis states that there is no significant difference or relationship between the variables. It is important to write the null hypothesis because it allows you to compare your results with what would be expected by chance.

Refine the Hypothesis

After formulating the hypothesis, it’s important to refine it and make it more precise. This may involve clarifying the variables, specifying the direction of the relationship, or making the hypothesis more testable.

Examples of Hypothesis

Here are a few examples of hypotheses in different fields:

• Psychology : “Increased exposure to violent video games leads to increased aggressive behavior in adolescents.”
• Biology : “Higher levels of carbon dioxide in the atmosphere will lead to increased plant growth.”
• Sociology : “Individuals who grow up in households with higher socioeconomic status will have higher levels of education and income as adults.”
• Education : “Implementing a new teaching method will result in higher student achievement scores.”
• Marketing : “Customers who receive a personalized email will be more likely to make a purchase than those who receive a generic email.”
• Physics : “An increase in temperature will cause an increase in the volume of a gas, assuming all other variables remain constant.”
• Medicine : “Consuming a diet high in saturated fats will increase the risk of developing heart disease.”

Purpose of Hypothesis

The purpose of a hypothesis is to provide a testable explanation for an observed phenomenon or a prediction of a future outcome based on existing knowledge or theories. A hypothesis is an essential part of the scientific method and helps to guide the research process by providing a clear focus for investigation. It enables scientists to design experiments or studies to gather evidence and data that can support or refute the proposed explanation or prediction.

The formulation of a hypothesis is based on existing knowledge, observations, and theories, and it should be specific, testable, and falsifiable. A specific hypothesis helps to define the research question, which is important in the research process as it guides the selection of an appropriate research design and methodology. Testability of the hypothesis means that it can be proven or disproven through empirical data collection and analysis. Falsifiability means that the hypothesis should be formulated in such a way that it can be proven wrong if it is incorrect.

In addition to guiding the research process, the testing of hypotheses can lead to new discoveries and advancements in scientific knowledge. When a hypothesis is supported by the data, it can be used to develop new theories or models to explain the observed phenomenon. When a hypothesis is not supported by the data, it can help to refine existing theories or prompt the development of new hypotheses to explain the phenomenon.

When to use Hypothesis

Here are some common situations in which hypotheses are used:

• In scientific research , hypotheses are used to guide the design of experiments and to help researchers make predictions about the outcomes of those experiments.
• In social science research , hypotheses are used to test theories about human behavior, social relationships, and other phenomena.
• I n business , hypotheses can be used to guide decisions about marketing, product development, and other areas. For example, a hypothesis might be that a new product will sell well in a particular market, and this hypothesis can be tested through market research.

Characteristics of Hypothesis

Here are some common characteristics of a hypothesis:

• Testable : A hypothesis must be able to be tested through observation or experimentation. This means that it must be possible to collect data that will either support or refute the hypothesis.
• Falsifiable : A hypothesis must be able to be proven false if it is not supported by the data. If a hypothesis cannot be falsified, then it is not a scientific hypothesis.
• Clear and concise : A hypothesis should be stated in a clear and concise manner so that it can be easily understood and tested.
• Based on existing knowledge : A hypothesis should be based on existing knowledge and research in the field. It should not be based on personal beliefs or opinions.
• Specific : A hypothesis should be specific in terms of the variables being tested and the predicted outcome. This will help to ensure that the research is focused and well-designed.
• Tentative: A hypothesis is a tentative statement or assumption that requires further testing and evidence to be confirmed or refuted. It is not a final conclusion or assertion.
• Relevant : A hypothesis should be relevant to the research question or problem being studied. It should address a gap in knowledge or provide a new perspective on the issue.

Advantages of Hypothesis

Hypotheses have several advantages in scientific research and experimentation:

• Guides research: A hypothesis provides a clear and specific direction for research. It helps to focus the research question, select appropriate methods and variables, and interpret the results.
• Predictive powe r: A hypothesis makes predictions about the outcome of research, which can be tested through experimentation. This allows researchers to evaluate the validity of the hypothesis and make new discoveries.
• Facilitates communication: A hypothesis provides a common language and framework for scientists to communicate with one another about their research. This helps to facilitate the exchange of ideas and promotes collaboration.
• Efficient use of resources: A hypothesis helps researchers to use their time, resources, and funding efficiently by directing them towards specific research questions and methods that are most likely to yield results.
• Provides a basis for further research: A hypothesis that is supported by data provides a basis for further research and exploration. It can lead to new hypotheses, theories, and discoveries.
• Increases objectivity: A hypothesis can help to increase objectivity in research by providing a clear and specific framework for testing and interpreting results. This can reduce bias and increase the reliability of research findings.

Limitations of Hypothesis

Some Limitations of the Hypothesis are as follows:

• Limited to observable phenomena: Hypotheses are limited to observable phenomena and cannot account for unobservable or intangible factors. This means that some research questions may not be amenable to hypothesis testing.
• May be inaccurate or incomplete: Hypotheses are based on existing knowledge and research, which may be incomplete or inaccurate. This can lead to flawed hypotheses and erroneous conclusions.
• May be biased: Hypotheses may be biased by the researcher’s own beliefs, values, or assumptions. This can lead to selective interpretation of data and a lack of objectivity in research.
• Cannot prove causation: A hypothesis can only show a correlation between variables, but it cannot prove causation. This requires further experimentation and analysis.
• Limited to specific contexts: Hypotheses are limited to specific contexts and may not be generalizable to other situations or populations. This means that results may not be applicable in other contexts or may require further testing.
• May be affected by chance : Hypotheses may be affected by chance or random variation, which can obscure or distort the true relationship between variables.

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• Introduction

The human relationship with nature

Biophilia and conservation, biophilia and technology, nature and health.

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• University of Washington - Developmental Psychology and the Biophilia Hypothesis: Children’s Affiliation with Nature
• International Society of Biourbanism - Biophilia and Gaia: Two Hypotheses for an Affective Ecology
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biophilia hypothesis , idea that humans possess an innate tendency to seek connections with nature and other forms of life . The term biophilia was used by German-born American psychoanalyst Erich Fromm in The Anatomy of Human Destructiveness (1973), which described biophilia as “the passionate love of life and of all that is alive.” The term was later used by American biologist Edward O. Wilson in his work Biophilia (1984), which proposed that the tendency of humans to focus on and to affiliate with nature and other life-forms has, in part, a genetic basis.

Anecdotal and qualitative evidence suggests that humans are innately attracted to nature. For example, the appearance of the natural world, with its rich diversity of shapes, colours, and life, is universally appreciated. This appreciation is often invoked as evidence of biophilia. The symbolic use of nature in human language, in idioms such as “blind as a bat” and “eager beaver,” and the pervasiveness of spiritual reverence for animals and nature in human cultures worldwide are other sources of evidence for biophilia. Such spiritual experience and widespread affiliations with natural metaphors appear to be rooted in the evolutionary history of the human species, originating in eras when people lived in much closer contact with nature than most do today. Human divergence from the natural world appears to have occurred in parallel with technological developments, with advances in the 19th and 20th centuries having the most significant impact, fundamentally changing human interactions with nature. In its most literal sense, this separation was made possible by the construction of enclosed and relatively sterile spaces, from homes to workplaces to cars, in which modern humans were sheltered from the elements of nature and in which many, particularly people living in more-developed countries, now spend the majority of their time.

Some of the most powerful evidence for an innate connection between humans and nature comes from studies of biophobia (the fear of nature), in which measurable physiological responses are produced upon exposure to an object that is the source of fear, such as a snake or a spider . These responses are the result of evolution in a world in which humans were constantly vulnerable to predators, poisonous plants and animals, and natural phenomena such as thunder and lightning. Fear was a fundamental connection with nature that enabled survival, and, as a result, humans needed to maintain a close relationship with their environment , using sights and sounds as vital cues, particularly for fight-or-flight responses .

Genes that influence biophilia have not been identified, and it is suspected that the increased dependence of the human species on technology has led to an attenuation in the human drive to connect with nature. Wilson and others have argued that such declines in biophilic behaviour could remove meaning from nature, translating into a loss of human respect for the natural world. In fact, the loss of desire to interact with the natural world, resulting in a decreased appreciation for the diversity of life-forms that support human survival, has been cited as a potential factor contributing to environmental destruction and the rapid rate of species extinction . Thus, reestablishing the human connection with nature has become an important theme in conservation .

In Biophilia , Wilson introduced a conservation ethic based on multiple dimensions of the innate relationship humans share with nature. His notion of environmental stewardship drew on various concepts, including the practical dependence of humans on nature, which centres on the ecological services (e.g., clean water and soil) nature provides; the satisfaction derived from direct interaction with nature, such as through exploration and development of outdoor skills; the physical appeal of nature, evident in its role as a source of inspiration and peace; and the human attachment to nature in the form of emotional connections to landscapes and animals.

Biophilia has been explored by researchers in a wide range of fields, and, as a result, its meaning and significance have been variously interpreted. Juxtaposed to the notion that biophilia competes with the human technological drive is the notion that technology is in itself an extension of human evolution and biophilia. Both perspectives were offered in The Biophilia Hypothesis (1993), a work coedited by Wilson and American social ecologist Stephen R. Kellert. Among the collection of views the work presented were those of American biologists Lynn Margulis and Dorion Sagan and Indian ecologist Madhav Gadgil, who considered the possibility that the human attraction to other life-forms is reflected in the diversity of technological developments that exist in the world today. Some of these technologies, including those employed in molecular biology and genetic engineering , have enabled scientists to develop entirely new forms of life, with which humans are wholly fascinated. The idea that technology feeds the human biophilic drive also finds support in the search for life on other planets ( see extraterrestrial life ).

Regardless of the extent to which individuals feel or perceive biophilia, research has indicated that simply spending time in nature is beneficial for human health . In one study, persons who spent even just two hours per week interacting with nature reported greater satisfaction and better health than persons who spent less time in natural environments . Among adults, spending time in natural environments is associated with improved mental and spiritual health; for children , being out in nature has been found to encourage physical activity and play.

August 9, 2024

Experiments Prepare to Test Whether Consciousness Arises from Quantum Weirdness

Researchers wish to probe whether consciousness has a basis in quantum mechanical phenomena

By Hartmut Neven & Christof Koch

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The brain is a mere piece of furniture in the vastness of the cosmos, subject to the same physical laws as asteroids, electrons or photons. On the surface, its three pounds of neural tissue seem to have little to do with quantum mechanics , the textbook theory that underlies all physical systems, since quantum effects are most pronounced on microscopic scales. Newly proposed experiments, however, promise to bridge this gap between microscopic and macroscopic systems, like the brain, and offer answers to the mystery of consciousness.

Quantum mechanics explains a range of phenomena that cannot be understood using the intuitions formed by everyday experience. Recall the Schrödinger’s cat thought experiment , in which a cat exists in a superposition of states, both dead and alive. In our daily lives there seems to be no such uncertainty—a cat is either dead or alive. But the equations of quantum mechanics tell us that at any moment the world is composed of many such coexisting states, a tension that has long troubled physicists.

Taking the bull by its horns, the cosmologist Roger Penrose in 1989 made the radical suggestion that a conscious moment occurs whenever a superimposed quantum state collapses. The idea that two fundamental scientific mysteries—the origin of consciousness and the collapse of what is called the wave function in quantum mechanics—are related, triggered enormous excitement.

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Penrose’s theory can be grounded in the intricacies of quantum computation . Consider a quantum bit, a qubit, the unit of information in quantum information theory that exists in a superposition of a logical 0 with a logical 1. According to Penrose, when this system collapses into either 0 or 1, a flicker of conscious experience is created, described by a single classical bit.

Penrose, together with anesthesiologist Stuart Hameroff, suggested that such collapse takes place in microtubules , tubelike, elongated structural proteins that form part of the cytoskeleton of cells, such as those making up the central nervous system.

These ideas have never been taken up by the scientific community as brains are wet and warm, inimical to the formation of superpositions, at least compared to existing quantum computers that operate at temperatures 10,000 times colder than room temperature to avoid destroying superposition states.

Penrose’s proposal suffers from a flaw when applied to two or more entangled qubits. Measuring one of these entangled qubits instantaneously reveals the state of the other one, no matter how far away. Their states are correlated, but correlation is not causation, and, according to standard quantum mechanics, entanglement cannot be employed to achieve faster-than-light communication. However, per Penrose’s proposal, qubits participating in an entangled state share a conscious experience. When one of them assumes a definite state, we could use this to establish a communication channel capable of transmitting information faster than the speed of light, a violation of special relativity.

In our view, the entanglement of hundreds of qubits, if not thousands or more, is essential to adequately describe the phenomenal richness of any one subjective experience: the colors, motions, textures, smells, sounds, bodily sensations, emotions, thoughts, shards of memories and so on that constitute the feeling of life itself.

In an article published in the open-access journal Entropy , we and our colleagues turned the Penrose hypothesis on its head, suggesting that an experience is created whenever a system goes into a quantum superposition rather than when it collapses. According to our proposal, any system entering a state with one or more entangled superimposed qubits will experience a moment of consciousness.

You, the astute reader, must by now be saying to yourself: But wait a minute here—I don’t ever consciously experience a superposition of states. Any one experience has a definitive quality; it is one thing and not the other. I see a particular shade of red, feel a toothache. I don’t simultaneously experience red and not-red, pain and not-pain.

The definitiveness of any conscious experience naturally arises within the many-worlds interpretation of quantum mechanics . A metaphysical position first put forward by physicist Hugh Everett in 1957, the many-worlds view, posits time’s evolution as an enormously branched tree, with every possible outcome of a quantum event splitting off its own universe. A single qubit entering a superposition gives birth to two universes, in one of which the qubit’s state is 0 while in a twin universe everything is identical except that the qubit’s state is 1.

Entanglement potentially offers something else for brain scientists by providing a natural solution to what is called the binding problem, the subjective unity of every experience that has long posed a key challenge to the study of consciousness. Consider seeing the Statue of Liberty: her face, the crown on her head, the torch in her raised right hand, and so on. All these distinctions and relationships are bound together into a single perception whose substrate might be numerous qubits, all entangled with each other.

To make these esoteric ideas concrete, we propose three experiments that would increasingly shape our thinking on these matters. The first experiment, progressing right now thanks to funding from the Santa Monica–based Tiny Blue Dot Foundation, seeks to provide evidence of the relevance of quantum mechanics to neuroscience in two very accessible test beds: tiny fruit flies and cerebral organoids, the latter lentil-sized assemblies of thousands of neurons grown from human-induced pluripotent stem cells. It is known that the inert noble gas xenon can act as anesthetic in animals and people. Remarkably, an earlier experiment claimed that its anesthetic potency, measured as the concentration of the gas that induces immobility, depends on the specific isotopes of xenon. Two isotopes of an element contain the same number of positively charged protons but different numbers of noncharged neutrons in their nuclei. The chemical properties of isotopes—that is, what they interact with—are similar, by and large, even though their masses and magnetic properties differ slightly.

If fruit flies and organoids can be used to detect different xenon isotopes, the hunt will be on for the exact mechanisms by which a gas that is inert and that remains aloof from binding to proteins or other molecules achieves this. Is it the tiny difference in the mass of these isotopes (131 versus 132 nucleons) that makes the difference? Or is it their nuclear spin, a quantum mechanical property of the nucleus? These xenon isotopes differ substantially in their nuclear spin; some have zero spin and others 1 / 2 or 3 / 2 .

These xenon experiments will inform a second follow-on experiment in which we will attempt to couple qubits to brain organoids in a way that allows entanglement to spread between biological and technical qubits. The final experiment, which at this stage is still a purely conceptual one, aims to enhance consciousness by coupling engineered quantum states to a human brain in an entangled manner. The person may then experience an expanded state of consciousness like those accessed under the influence of ayahuasca or psilocybin.

Both quantum engineering and the design of brain-machine interfaces are progressing rapidly. It may not be beyond human ingenuity to directly probe and expand our conscious mind by making use of quantum science and technology.

This is an opinion and analysis article, and the views expressed by the author or authors are not necessarily those of Scientific American.

Natural resources in an urbanizing world

The College of Natural Resources and Environment is leveraging research, innovation, and sustainable solutions to shape the cities of tomorrow.

• David Fleming
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Leave the cooling shade and birdsong of Virginia’s forests behind. Forget about the lifecycles of our interior rivers and streams, the arteries of our landscapes. Skip the coastal marshlands teeming with life, the shorelines of migrating birds forging for nesting materials.

Instead, imagine the commonwealth's cities.

Picture the century-old brick factories being converted to a new purpose in Roanoke. See new spikes rising on skylines of Alexandria or Arlington, new foundations for construction projects in Richmond. Imagine a different kind of flow altogether: one of materials and resources, of energy and waste. Imagine life in that place: people moving toward urban centers and the ecosystems of plants and animals that call our cities home.

“When we talk about cities, we often get caught up in talking about built forms or social questions or the politics of land use or economics,” said Virginia Tech Associate Professor Robert Oliver of the College of Natural Resources and Environment , who researches urban mega events such as this summer’s Olympics in Paris. “What our college is good at is reminding people that all of those questions are ultimately questions about resources.”

As world populations move toward urban spaces, understanding the interplay between the flow and utilization of natural and human resources is critical to ensuring a sustainable future for our cities. Researchers in the college are participating in critical work that will lay the foundation for our urban landscapes of tomorrow.

Where wildlife meets the streetlight

Assistant Professor Willandia Chaves ’ research on the human dimensions that impact wildlife conservation takes place in two urban spheres. In the U.S., she is exploring questions of access and connection to natural spaces for people living in urban communities. In her home country of Brazil, Chaves researches how the movement of people between rural and urban areas impacts the harvest, trade, and consumption of wildlife.

“There is a big difference in terms of the demand for wildlife in places that are still developing toward urbanization,” said Chaves, who teaches in the Department of Fish and Wildlife Conservation . “In some places in Brazil, the process of urbanization is still recent and that affects how people access and use wildlife. The rural-urban transition also has implications in terms of the cultural loss of traditions and identities as well as some more immediate concerns about food security.

“We know that wildlife is important for many rural residents, but it is not yet clear how much wildlife contributes to the food security of urban residents. Given how fast the world is urbanizing, understanding wildlife use by urban residents will be important for the sustainable and equitable use of these resources.”

Assistant Professor Willandia Chaves (at left) researches the human dimensions impacting wildlife conservation in the urban environments of Brazil and the U.S. Photo courtesy of Cristiano Pires.

Chaves also is conducting and fostering research on outdoor participation and the social assets that marginalized communities utilize to access natural spaces.

“I study various social resources that people and communities use to maneuver through spaces and structures that were not created with them in mind,” said Chaves, who is an affiliate faculty member of the Global Change Center and the Center for Emerging Zoonotic, and Arthropod-borne Pathogens . “I am looking into what types of assets or cultural capital people can bring and rely on to create community and engage with spaces that they might otherwise feel left out of.”

Chaves is not the only professor exploring the global dimensions of human and wildlife interactions in urban environments. Elizabeth Nyboer is exploring the importance of inland fisheries to global nutrition in cities , while Luis Escobar researches the biogeography of disease transmission from animals to humans. Kathleen Alexander is utilizing a One Health vision to better understand the dynamics of an increasingly urbanizing Botswana, while  Mark Ford is coordinating with federal agencies to understand and protect North American bat populations around Washington, D.C.

“Understanding the intersection of urbanization and natural resource use is critical to conservation,” said Chaves. “Urbanization fundamentally changes our relationship with the natural resources we utilize and consume, and that, in turn, impacts human well-bring.”

Seeing the tree in a concrete forest

Associate Professor Eric Wiseman  belives that if you want to understand life in the city, you could start by considering the trees.

“In urban ecosystems, a lot of the critical ecosystem functions are mediated through trees,” said Wiseman, who teaches urban and community forestry in the Department of Forest Resources and Environmental Conservation . “Trees have a significant influence on the cycles of water, carbon, and nitrogen. They mitigate extreme heat by casting shade and capturing solar energy, and they are the scaffolding habitat for the flora and fauna that occupy the natural spaces in cities.”

Wiseman’s research and teaching focuses on understanding the ecology and physiology of urban forests, and how they can be managed to enhance ecosystem processes while also improving life for city dwellers.

“There’s a great deal of systems thinking in urban forestry,” Wiseman said. “The field has its roots in traditional forestry, where we’re aiming to understand the basic geographic and ecological characteristics of a landscape to better cultivate trees for a desired outcome, often production of lumber and pulp.

“In urban spaces, instead of a stand of trees in a forest, we might be managing numerous individual trees in a park or a neighborhood or even an entire city. Our management interventions are aimed not at maximizing the product value of a tree, but instead on trying to sustain trees for their non-commodity ecosystem services.”

Associate Professor Eric Wiseman (at left) leads the urban forestry program in the Department of Forest Resources and Environmental Conservation. Photo by Krista Timney for Virginia Tech.

Wiseman said the subject of urban green and forest space is increasingly merging with social science conversations about inclusivity and access for underserved communities.

“Increasingly, we’re having students enter our program with interests in addressing questions of environmental justice and tree canopy equity for underserved and vulnerable populations,” said Wiseman. “For people in a city, urban forests are one of the primary mechanisms toward addressing environmental justice issues.”

Wiseman is not the only researcher looking to trees to enhance life in the city. In the Department of Sustainable Biomaterials , Daniel Hindman , Joe Loferski , and Brian Bond , the college’s associate dean for Extension, outreach, and engagement, are researching the potentials and capacities of low-carbon wood composites such as cross-laminated timber for sustainable solutions to building construction.

Virginia Water Resources Research Center Associate Director Daniel McLaughlin is conducting research with The Nature Conservancy and the City of Virginia Beach to explore how the urban forests of Virginia Beach can mitigate stormwater challenges while also providing ecosystem services to the booming coastal region.

“Urban forests serve a disproportionate role in services like water removal from storm events,” said McLaughlin. “It’s really important to realize that even small patches of forests within an otherwise urban landscape can provide important services to a community, and the response from citizens has been overwhelmingly supportive.”

A wide-scale vision of urban challenges

As Paris braces for the Summer Olympics, Robert Oliver is preparing to add one more data point to his research exploring large-scale events and how cities manage rapid urban change.

“My work has long been about mega events,” said Oliver, who teaches in the Department of Geography . "I look at things like the Olympics and say, ‘All right, what was the visioning plan for a city, how did it unfold, and what sticks around after the event is over.'”

Oliver, who teaches urban sustainability and human geography dynamics in cities, said large-scale events provide geographers the chance to explore an intersection of concepts, from political motivations to public space uses to resource allocation and land utilization.

“One of the central themes of my introduction to human geography course is human-environment interaction,” said Oliver, who said students taking that course come from a range of majors around Virginia Tech. “That interaction requires us to think about how humans adapt, modify, and depend on their environment, and how we can think about smarter solutions for the challenges of urban living.”

Associate Professor Robert Oliver – who teaches on the subjects of urban sustainability and human geography dynamics in cities – is an expert on urban megaevents like this year’s Summer Olympics in Paris. Photo courtesy of Robert Oliver.

Oliver, who utilizes both quantitative and qualitative approaches to develop broad-scale perspectives on urban outcomes, said an event such as the Summer Olympics presents an opportunity for cities to make improvements that align with multiple ambitions.

“A number of years ago, the International Olympic Committee tried to realign the priorities of the Olympic movement to be more in line with the urban goals of host cities,” said Oliver. “The 2024 Olympics in Paris will be the first benchmark of the new agenda. The city is being very deliberate about the adaptive reuse of existing facilities while striving to locate new infrastructure in previously underserved areas.”

The broad perspective view that Oliver applies to cities echoes throughout the college. Fellow geographer Anamaria Bukvic is merging community surveys with geospatial data to better understand how coastal communities can react and adapt to the challenges of rising seas , while Junghwan Kim is exploring human mobility questions while leading the Smart Cities for Good research group that explores how to leverage technology to solve environmental and social challenges in urban locations.

Finally, Jennifer Russell and Kiara Winans , both in the Department of Sustainable Biomaterials, are blazing a new trail to transition industry and society toward economic models that prioritize sustainability and a circular economy .

“Humans have the ability to make or break the possibility of sustainable cities,” said Russell. “We have the technology, the data, and the ability to adapt and innovate, and what we’re working toward now is collective alignment and understanding.”

Understanding the flow and utilization of resources such as trees and water, the interplay between humans and animals, and the broad-scale dynamics of life in urban spaces are critical drivers for the coming century, a priority of the United Nations Sustainable Cities and Communities goal to make cities inclusive, safe, and resilient.

Researchers in the College of Natural Resources and Environment will be at the forefront of understanding, building, and adapting today’s natural resources for the demands of tomorrow’s urbanizing world.

Zeke Barlow

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Mothers Are Told That Natural Childbirth Is Best.

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A few weeks after I had my first child, I ran into a woman from my prenatal yoga class who was still pregnant. She told me, sadly, that her baby was breech, and because her desperate efforts to get him turned around had failed, she was going to need a C-section. I told her that I’d had one too and it had gone great; coming home from the hospital with a healthy baby and no rips or tears except the incision on my abdomen made me feel like I’d gotten away with something. My response surprised her, because others she spoke to treated missing out on a vaginal birth as a great tragedy. “You were the only person who made me feel comfortable about it,” she told me recently.

Even if you believe, as many experts do, that America’s C-section rate is too high, such surgeries will always be necessary in a significant minority of pregnancies. (The World Health Organization considers a C-section rate of about 10 percent to be ideal.) Yet the natural-parenting movement, which purports to recreate the practices of a romanticized premodern past, often makes women who’ve delivered via C-section, or with epidurals and other medical interventions, feel like failures. And for some, the movement’s dictates blight the early years of parenthood with rigid expectations for exclusive breastfeeding, constant baby-wearing and co-sleeping. My liberal, upper-middle-class urban milieu likes to pride itself on trusting the science, but many of us are in thrall to a toxic ideology whose legitimate insights are braided with myth and pseudoscience.

Don’t Tell My Friends, But… New York Times Opinion columnists burst bubbles, overturn conventional wisdom and question the assumptions — both big and small — of the people they usually agree with. New York Times Opinion columnists burst bubbles, overturn conventional wisdom and question the assumptions — both big and small — of the people they usually agree with.

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The natural-parenting movement, like the anti-vaccine movement, relies on our forgetfulness about what life was like before the innovations that it denounces. Having a baby without medical help may be natural, but so is obstetric fistula and hemorrhaging to death. It’s a miracle of modern medicine that over the course of the 20th century, America’s maternal mortality rate declined by almost 99 percent. There has never been a time when all mothers could breastfeed, and before the advent of baby formula, as the scholar Carla Cevasco wrote , “many families had to endure the agony of losing a baby to starvation, malnutrition, or related disease.” When it comes to human reproduction, nature is neither kind nor efficient.

Also like the anti-vaccine movement, the natural-parenting movement is a reaction to very real failures in our medical system, which has more than earned people’s distrust. Many women have had experiences with OB-GYNs that leave them feeling disrespected and abused. Some doctors don’t take women’s pain — especially Black women’s pain — seriously. Some force unwanted and unnecessary interventions on women against their will. Midwives and doulas may give women the sustained, individualized attention that they should be getting from their doctors but too often aren’t.

But while natural parenting sells itself as a vehicle for women’s liberation from a patriarchal medical establishment, it is shaped by its reactionary roots. Amy Tuteur, a retired OB-GYN, former Harvard Medical School instructor and longtime foe of the natural-parenting movement, points out that Grantly Dick-Read, the British obstetrician who coined the term “ natural childbirth ,” was a eugenicist who believed that “primitive” women didn’t experience pain in childbirth, unlike “ over-civilized ” white women. He regarded women’s fear of labor as hysterical and wanted upper-middle-class white women to get over it so that they’d have more babies. Ina May Gaskin, the grandmother of modern midwifery, called Dick-Read her “hero.”

La Leche League, which started popularizing breastfeeding in the 1950s, was founded by a group of Catholic homemakers with extremely traditional ideas about gender roles; as late as the 1980s, the group frowned on mothers of young children having jobs. When Dr. William Sears developed his influential theories on attachment parenting — a philosophy that promotes near-constant baby-wearing and co-sleeping — he was an evangelical Christian who believed that God had ordained women’s submission to their husbands. Natural parenting has since been thoroughly secularized, but it still preaches something akin to spiritual transcendence through female sacrifice.

Though natural parenting makes substantial demands on mothers, in almost every case, the evidence supporting its tenets is either lacking or exaggerated. Take, for example, nursing. In developed countries, where access to clean drinking water to mix formula isn’t an issue, breastfeeding has some modest effects. “The evidence suggests that breastfeeding may slightly decrease your infant’s chance of diarrhea and eczema,” wrote the economist Emily Oster, author of several books on pregnancy and parenting. But the other happy outcomes touted by breastfeeding evangelists, including increased intelligence, lower rates of obesity, and fewer allergies and behavioral problems, shrink or disappear when studies adjust for maternal class and I.Q.

Even if you distrust the natural-parenting movement, its pressures are hard to escape. Long before I was pregnant, my reporting on maternal health in countries without adequate obstetric care made me very suspicious of essentialist ideas about what Gaskin called “the ancient wisdom” of women’s bodies. During my first pregnancy, my sudden obsessive interest in birth led me to a harrowing phenomenon that I wrote about for the Daily Beast : traumatized women who’d lost babies during home births with unqualified midwives.

Nevertheless, when my first child needed more milk than I could produce, I was ashamed and hired a lactation consultant who had been profiled by this newspaper. She spoke to me with saccharine condescension and prescribed a regimen of round-the-clock feeding and pumping that would have sent me spiraling into postpartum depression if I’d stuck to it. The encounter was one of my lowest moments of early parenthood. Ironically, while there’s little evidence showing that breastfeeding is particularly important for babies, there’s quite a bit showing that maternal happiness is. If the natural-parenting movement really cared about children, it would do some introspection about how often it makes their parents miserable.

Michelle Goldberg has been an Opinion columnist since 2017. She is the author of several books about politics, religion and women’s rights, and was part of a team that won a Pulitzer Prize for public service in 2018 for reporting on workplace sexual harassment.

Illustration by Rozalina Burkova. Produced by Shannon Lin, Jessia Ma and Shoshana Schultz.

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Figure’s new humanoid robot leverages OpenAI for natural speech conversations

Figure has unveiled its latest humanoid robot, the Figure 02. The system is — as its name helpfully suggests — the successor to the Figure 01 robot unveiled in 2023 . An initial teaser video is similar to those we’ve seen from other humanoids, echoing consumer electronics product videos, rather than a raw demo of the robot in action.

Another video released Tuesday showcases the robot’s slow, bent-leg gait across the floor of what looks to be the demo area constructed in the middle of Figure’s offices. Another two robots appear in the background, carting totes — the biggest out-of-the-box application for most of these humanoids.

The most notable addition this time out arrives by way of a longstanding partnership with OpenAI, which helped Figure raise a $675 million Series B back in February , valuing the South Bay firm at $2.6 billion.

The mainstream explosion of neural networks has been enticing for the robotics industry at large, but humanoid developers have taken a particular interest in the technology. One of the form factor’s key selling points is its ability to effectively slot alongside human co-workers on a factory floor — once the proper safety measures are in place, of course. Figure 02 is outfitted with speakers and microphones to speak and listen to people at work.

Models like ChatGPT and Google Gemini have been prized for their natural language capabilities, ushering in a new area of smart assistants and chatbots. Outfitting these systems with such capabilities is a no-brainer: Doing so helps humans instruct the robots, while at the same time adding a level of transparency to what the robot is doing at any given time.

Communication like this is doubly important when dealing with humanoid robots, as the systems are designed to wander freely without a safety cage. Despite their human-like design, it’s important not to lose sight of the fact that they’re still big, heavy and potentially dangerous pieces of moving metal. Combined with vision and proximity sensors, speech can be an important safety tool.

Figure certainly isn’t alone in this work. Late last year, Agility showcased the work it’s been doing to leverage generative AI for improved human-robot communication. The use of neural networks was a key focus for Google’s Everyday Robots team before it was shuttered. Elon Musk, meanwhile, is ostensibly in charge of both Grok AI and Optimus — two projects that will no doubt dovetail sooner rather than later.

For its part, OpenAI has hedged its bets a bit in the category. Prior to its Figure investment, the firm backed Norwegian firm 1X. Over the past year, however, Figure has become far buzzier in the industry. Its aforementioned Series B also included other top tech names like Microsoft, Amazon, Nvidia and Intel Capital.

Figure recently began pilots with BMW . In June, the company debuted a video showcasing an earlier, tethered version of the robot autonomously performing tasks on the floor, with the help of neural networks.

The company notes that the 02 robot has already paid a visit to the automaker’s Spartanburg, South Carolina, facility for training and data collection purposes. We’re still very much in the early stages of these partnerships. Agility, Apptronik and Sanctuary AI have announced similar pilots with carmakers. Working on Teslas has been a key focus for Optimus since before it was Optimus, and Boston Dynamics-owner Hyundai has its sights set on humanoids in its own factories.

Communication is one piece of what Figure is referring to as a “ground-up hardware and software redesign” between 01 and 02. The list also includes six RGB cameras, coupled with an onboard visual language model, improved CPU/GPU computing and improved hands, with 16 degrees of freedom.

Hands have been their own hot-button topic in the humanoid robot world. There are differing opinions regarding how closely designers should hew to their human counterparts.

There’s a lot to be said for the nimbleness and dexterity of our appendages, though human-inspired hands have been criticized for their delicacy and a perceived over engineering. Figure, for its part, has been dedicated to using humanlike hands as its system’s end effectors.

We don’t have a timeline for a wider Figure 02 rollout, though the company is hinting at a broader future outside the warehouse/factory floor. “Figure’s robot combines the dexterity of the human form with advanced AI to perform a wide range of tasks across commercial applications and, in the near future, the home,” the company writes.

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