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How research works: understanding the process of science.

Have you ever wondered how research works? How scientists make discoveries about our health and the world around us? Whether they’re studying plants, animals, humans, or something else in our world, they follow the scientific method. But this method isn’t always—or even usually—a straight line, and often the answers are unexpected and lead to more questions. Let’s dive in to see how it all works.

The Question Scientists start with a question about something they observe in the world. They develop a hypothesis, which is a testable prediction of what the answer to their question will be. Often their predictions turn out to be correct, but sometimes searching for the answer leads to unexpected outcomes.

The Techniques To test their hypotheses, scientists conduct experiments. They use many different tools and techniques, and sometimes they need to invent a new tool to fully answer their question. They may also work with one or more scientists with different areas of expertise to approach the question from other angles and get a more complete answer to their question.

The Evidence Throughout their experiments, scientists collect and analyze their data. They reach conclusions based on those analyses and determine whether their results match the predictions from their hypothesis. Often these conclusions trigger new questions and new hypotheses to test.

Researchers share their findings with one another by publishing papers in scientific journals and giving presentations at meetings. Data sharing is very important for the scientific field, and although some results may seem insignificant, each finding is often a small piece of a larger puzzle. That small piece may spark a new question and ultimately lead to new findings.

Sometimes research results seem to contradict each other, but this doesn’t necessarily mean that the results are wrong. Instead, it often means that the researchers used different tools, methods, or timeframes to obtain their results. The results of a single study are usually unable to fully explain the complex systems in the world around us. We must consider how results from many research studies fit together. This perspective gives us a more complete picture of what’s really happening.

Even if the scientific process doesn’t answer the original question, the knowledge gained may help provide other answers that lead to new hypotheses and discoveries.

Learn more about the importance of communicating how this process works in the NIH News in Health article, “ Explaining How Research Works .”

This post is a great supplement to Pathways: The Basic Science Careers Issue.

Pathways introduces the important role that scientists play in understanding the world around us, and all scientists use the scientific method as they make discoveries—which is explained in this post.

Learn more in our Educator’s Corner .

2 Replies to “How Research Works: Understanding the Process of Science”

Nice basic explanation. I believe informing the lay public on how science works, how parts of the body interact, etc. is a worthwhile endeavor. You all Rock! Now, we need to spread the word ‼️❗️‼️ Maybe eith a unique app. And one day, with VR and incentives to read & answer a couple questions.

As you know, the importance of an informed population is what will keep democracy alive. Plus it will improve peoples overall wellness & life outcomes.

Thanks for this clear explanation for the person who does not know science. Without getting too technical or advanced, it might be helpful to follow your explanation of replication with a reference to meta-analysis. You might say something as simple as, “Meta-analysis is a method for doing research on all the best research; meta-analytic research confirms the overall trend in results, even when the best studies show different results.”

Comments are closed.

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

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

Listen to this reading

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

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

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

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

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

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

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

• Scientific theories

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

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

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

Comprehension Checkpoint

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

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

• The Fixity Notion: Linnaeus

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

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

• The age of Earth: Leclerc and Hutton

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

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

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

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

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

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

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

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

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

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

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

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

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

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

• Rock layers as evidence for evolution: Smith

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

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

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

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

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

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

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

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

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

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

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

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

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

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

• Evolution under a microscope: Lenski

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

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

• Punctuated equilibrium: Gould and Eldredge

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

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

• Hypotheses and laws: Other scientific concepts

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

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

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

Theories, hypotheses , and laws drive scientific progress

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

Table of Contents

• Theories, hypotheses, and laws drive scientific progress

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Science Connected Magazine

Science Literacy, Education, Communication

Failed Experiments Move Science Forward

Experiments that don’t go as expected and trials that yield negative results are critical for moving science forward. Research scientists discuss the value of failed experiments.

A new series for ResearchGate by Katherine Lindemann Article contributed by Michele Heisler

Researchers don’t dream of negative studies, but experiments that don’t go as expected and trials that yield negative results are critical for moving science forward. To highlight this important part of the research process, we asked research scientists to speak about their own experiences with “failure.” Our first contributor is Michele Heisler,  a health services researcher who develops and tests health system-based interventions.

There is a certain moment that every researcher who develops and evaluates health care interventions both eagerly anticipates and dreads. It is the moment that comes after years of planning and honing the intervention to be tested, after securing funding, recruiting study participants, conducting the intervention, ensuring it is being conducted as envisioned, meticulously gathering and recording data, and desperately trying to keep people involved in the study. After years of hard work, you and maybe some members of your research team gather in a room around a single computer. The code is written. All the relevant data is entered. You push the run button, the computer whirs, and output appears on the screen. You take a deep breath and lean over to review it.

And there it is. The results are often there in a single regression. You peer down and sigh. It didn’t work. The patients who received your amazingly crafted, brilliant, sure-to-succeed intervention did not do any better on your primary outcome than patients who got the usual care or some other approach that you didn’t think would work as well. All those years of work and in a single minute you realize you have what is called a “negative study.” Your hypothesis was wrong. You can’t blame it on poor fidelity to your imagined intervention: you carefully assessed fidelity, and it was delivered as intended. You can’t blame it on lack of engagement: most of the participants engaged in the intervention as well as you could have hoped. It just didn’t work any better than the alternative.

The Pain of Failed Experiments

The first time this happened to me, I felt crushed by a sense of failure. How could it not have worked? A similar intervention had worked beautifully with patients with a different health condition. I had felt so sure this would be an effective intervention. And all those years spent on this just to find it didn’t work? I gave myself a bracing self-lecture on why randomized controlled trials and equipoise, the principles used to assign patients to different treatments in trials, were so very important. I reminded myself that I was an objective researcher seeking truth and not an advocate for certain approaches until they were rigorously tested—and even then continuing to question and challenge.

RELATED: SCIENTISTS, PLEASE DESCRIBE YOUR FAILURES

But, it was only after I sulked for a while and then buckled down to try to make sense of the results and write them up that I began to see the importance of these “negative” findings. Happily, we did gather qualitative data from participants about their views and experiences that we were able to scour. As I delved into the data, the reasons the intervention didn’t work as we had hoped began to become clear. The reasons were fascinating and unexpected, but made so much sense in retrospect. My team and I became excited about the lessons learned from the failure of the intervention, the reasons it failed, and how we needed to change and adapt our approach to incorporate these lessons.

We want things to work. We believe in our ideas. Until they are rigorously tested, we just know our brilliant interventions will work as we imagined. I still dread the moment when truth about success or failure is irrevocably flashed on the computer screen. I now firmly believe, though, that the lessons from the failures may be as crucial as the “successes” to inform interventions that will improve health.

A version of this article entitled “Failure moves research forward” was originally published by ResearchGate .

Research and Hypothesis Testing: Moving from Theory to Experiment

• First Online: 14 November 2019

Cite this chapter

• Mark W. Scerbo 6 ,
• Aaron W. Calhoun 7 &
• Joshua Hui 8  

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In this chapter, we discuss the theoretical foundation for research and why theory is important for conducting experiments. We begin with a brief discussion of theory and its role in research. Next, we address the relationship between theory and hypotheses and distinguish between research questions and hypotheses. We then discuss theoretical constructs and how operational definitions make the constructs measurable. Next, we address the experiment and its role in establishing a plan to test the hypothesis. Finally, we offer an example from the literature of an experiment grounded in theory, the hypothesis that was tested, and the conclusions the authors were able to draw based on the hypothesis. We conclude by emphasizing that theory development and refinement does not result from a single experiment, but instead requires a process of research that takes time and commitment.

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Research Questions and Research Design

An Introduction to Experimental Design Research

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Scerbo, M.W., Calhoun, A.W., Hui, J. (2019). Research and Hypothesis Testing: Moving from Theory to Experiment. In: Nestel, D., Hui, J., Kunkler, K., Scerbo, M., Calhoun, A. (eds) Healthcare Simulation Research. Springer, Cham. https://doi.org/10.1007/978-3-030-26837-4_22

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• How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

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

Table of contents

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

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

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

Variables in hypotheses

Hypotheses propose a relationship between two or more variables . An independent variable is something the researcher changes or controls. A dependent variable is something the researcher observes and measures.

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

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Step 1: ask a question.

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

Step 2: Do some preliminary research

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

At this stage, you might construct a conceptual framework to identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalise more complex constructs.

Step 3: Formulate your hypothesis

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

Step 4: Refine your hypothesis

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

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

Step 5: Phrase your hypothesis in three ways

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

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

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

Step 6. Write a null hypothesis

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

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

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

A research hypothesis is your proposed answer to your research question. The research hypothesis usually includes an explanation (‘ x affects y because …’).

A statistical hypothesis, on the other hand, is a mathematical statement about a population parameter. Statistical hypotheses always come in pairs: the null and alternative hypotheses. In a well-designed study , the statistical hypotheses correspond logically to the research hypothesis.

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What is and How to Write a Good Hypothesis in Research?

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One of the most important aspects of conducting research is constructing a strong hypothesis. But what makes a hypothesis in research effective? In this article, we’ll look at the difference between a hypothesis and a research question, as well as the elements of a good hypothesis in research. We’ll also include some examples of effective hypotheses, and what pitfalls to avoid.

What is a Hypothesis in Research?

Simply put, a hypothesis is a research question that also includes the predicted or expected result of the research. Without a hypothesis, there can be no basis for a scientific or research experiment. As such, it is critical that you carefully construct your hypothesis by being deliberate and thorough, even before you set pen to paper. Unless your hypothesis is clearly and carefully constructed, any flaw can have an adverse, and even grave, effect on the quality of your experiment and its subsequent results.

Research Question vs Hypothesis

It’s easy to confuse research questions with hypotheses, and vice versa. While they’re both critical to the Scientific Method, they have very specific differences. Primarily, a research question, just like a hypothesis, is focused and concise. But a hypothesis includes a prediction based on the proposed research, and is designed to forecast the relationship of and between two (or more) variables. Research questions are open-ended, and invite debate and discussion, while hypotheses are closed, e.g. “The relationship between A and B will be C.”

A hypothesis is generally used if your research topic is fairly well established, and you are relatively certain about the relationship between the variables that will be presented in your research. Since a hypothesis is ideally suited for experimental studies, it will, by its very existence, affect the design of your experiment. The research question is typically used for new topics that have not yet been researched extensively. Here, the relationship between different variables is less known. There is no prediction made, but there may be variables explored. The research question can be casual in nature, simply trying to understand if a relationship even exists, descriptive or comparative.

How to Write Hypothesis in Research

Writing an effective hypothesis starts before you even begin to type. Like any task, preparation is key, so you start first by conducting research yourself, and reading all you can about the topic that you plan to research. From there, you’ll gain the knowledge you need to understand where your focus within the topic will lie.

Remember that a hypothesis is a prediction of the relationship that exists between two or more variables. Your job is to write a hypothesis, and design the research, to “prove” whether or not your prediction is correct. A common pitfall is to use judgments that are subjective and inappropriate for the construction of a hypothesis. It’s important to keep the focus and language of your hypothesis objective.

An effective hypothesis in research is clearly and concisely written, and any terms or definitions clarified and defined. Specific language must also be used to avoid any generalities or assumptions.

Use the following points as a checklist to evaluate the effectiveness of your research hypothesis:

• Predicts the relationship and outcome
• Simple and concise – avoid wordiness
• Clear with no ambiguity or assumptions about the readers’ knowledge
• Observable and testable results
• Relevant and specific to the research question or problem

Research Hypothesis Example

Perhaps the best way to evaluate whether or not your hypothesis is effective is to compare it to those of your colleagues in the field. There is no need to reinvent the wheel when it comes to writing a powerful research hypothesis. As you’re reading and preparing your hypothesis, you’ll also read other hypotheses. These can help guide you on what works, and what doesn’t, when it comes to writing a strong research hypothesis.

Here are a few generic examples to get you started.

Eating an apple each day, after the age of 60, will result in a reduction of frequency of physician visits.

Budget airlines are more likely to receive more customer complaints. A budget airline is defined as an airline that offers lower fares and fewer amenities than a traditional full-service airline. (Note that the term “budget airline” is included in the hypothesis.

Workplaces that offer flexible working hours report higher levels of employee job satisfaction than workplaces with fixed hours.

Each of the above examples are specific, observable and measurable, and the statement of prediction can be verified or shown to be false by utilizing standard experimental practices. It should be noted, however, that often your hypothesis will change as your research progresses.

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What Is a Hypothesis and How Do I Write One?

General Education

Think about something strange and unexplainable in your life. Maybe you get a headache right before it rains, or maybe you think your favorite sports team wins when you wear a certain color. If you wanted to see whether these are just coincidences or scientific fact, you would form a hypothesis, then create an experiment to see whether that hypothesis is true or not.

But what is a hypothesis, anyway? If you’re not sure about what a hypothesis is--or how to test for one!--you’re in the right place. This article will teach you everything you need to know about hypotheses, including: 

• Defining the term “hypothesis” 
• Providing hypothesis examples 
• Giving you tips for how to write your own hypothesis

So let’s get started!

What Is a Hypothesis?

Merriam Webster defines a hypothesis as “an assumption or concession made for the sake of argument.” In other words, a hypothesis is an educated guess . Scientists make a reasonable assumption--or a hypothesis--then design an experiment to test whether it’s true or not. Keep in mind that in science, a hypothesis should be testable. You have to be able to design an experiment that tests your hypothesis in order for it to be valid. 

As you could assume from that statement, it’s easy to make a bad hypothesis. But when you’re holding an experiment, it’s even more important that your guesses be good...after all, you’re spending time (and maybe money!) to figure out more about your observation. That’s why we refer to a hypothesis as an educated guess--good hypotheses are based on existing data and research to make them as sound as possible.

Hypotheses are one part of what’s called the scientific method .  Every (good) experiment or study is based in the scientific method. The scientific method gives order and structure to experiments and ensures that interference from scientists or outside influences does not skew the results. It’s important that you understand the concepts of the scientific method before holding your own experiment. Though it may vary among scientists, the scientific method is generally made up of six steps (in order):

• Observation
• Asking questions
• Forming a hypothesis
• Analyze the data
• Communicate your results

You’ll notice that the hypothesis comes pretty early on when conducting an experiment. That’s because experiments work best when they’re trying to answer one specific question. And you can’t conduct an experiment until you know what you’re trying to prove!

Independent and Dependent Variables 

After doing your research, you’re ready for another important step in forming your hypothesis: identifying variables. Variables are basically any factor that could influence the outcome of your experiment . Variables have to be measurable and related to the topic being studied.

There are two types of variables:  independent variables and dependent variables. I ndependent variables remain constant . For example, age is an independent variable; it will stay the same, and researchers can look at different ages to see if it has an effect on the dependent variable. 

Speaking of dependent variables... dependent variables are subject to the influence of the independent variable , meaning that they are not constant. Let’s say you want to test whether a person’s age affects how much sleep they need. In that case, the independent variable is age (like we mentioned above), and the dependent variable is how much sleep a person gets. 

Variables will be crucial in writing your hypothesis. You need to be able to identify which variable is which, as both the independent and dependent variables will be written into your hypothesis. For instance, in a study about exercise, the independent variable might be the speed at which the respondents walk for thirty minutes, and the dependent variable would be their heart rate. In your study and in your hypothesis, you’re trying to understand the relationship between the two variables.

Elements of a Good Hypothesis

The best hypotheses start by asking the right questions . For instance, if you’ve observed that the grass is greener when it rains twice a week, you could ask what kind of grass it is, what elevation it’s at, and if the grass across the street responds to rain in the same way. Any of these questions could become the backbone of experiments to test why the grass gets greener when it rains fairly frequently.

As you’re asking more questions about your first observation, make sure you’re also making more observations . If it doesn’t rain for two weeks and the grass still looks green, that’s an important observation that could influence your hypothesis. You'll continue observing all throughout your experiment, but until the hypothesis is finalized, every observation should be noted.

Finally, you should consult secondary research before writing your hypothesis . Secondary research is comprised of results found and published by other people. You can usually find this information online or at your library. Additionally, m ake sure the research you find is credible and related to your topic. If you’re studying the correlation between rain and grass growth, it would help you to research rain patterns over the past twenty years for your county, published by a local agricultural association. You should also research the types of grass common in your area, the type of grass in your lawn, and whether anyone else has conducted experiments about your hypothesis. Also be sure you’re checking the quality of your research . Research done by a middle school student about what minerals can be found in rainwater would be less useful than an article published by a local university.

Writing Your Hypothesis

Once you’ve considered all of the factors above, you’re ready to start writing your hypothesis. Hypotheses usually take a certain form when they’re written out in a research report.

When you boil down your hypothesis statement, you are writing down your best guess and not the question at hand . This means that your statement should be written as if it is fact already, even though you are simply testing it.

The reason for this is that, after you have completed your study, you'll either accept or reject your if-then or your null hypothesis. All hypothesis testing examples should be measurable and able to be confirmed or denied. You cannot confirm a question, only a statement! 

In fact, you come up with hypothesis examples all the time! For instance, when you guess on the outcome of a basketball game, you don’t say, “Will the Miami Heat beat the Boston Celtics?” but instead, “I think the Miami Heat will beat the Boston Celtics.” You state it as if it is already true, even if it turns out you’re wrong. You do the same thing when writing your hypothesis.

Additionally, keep in mind that hypotheses can range from very specific to very broad.  These hypotheses can be specific, but if your hypothesis testing examples involve a broad range of causes and effects, your hypothesis can also be broad.  

The Two Types of Hypotheses

Now that you understand what goes into a hypothesis, it’s time to look more closely at the two most common types of hypothesis: the if-then hypothesis and the null hypothesis.

#1: If-Then Hypotheses

First of all, if-then hypotheses typically follow this formula:

If ____ happens, then ____ will happen.

The goal of this type of hypothesis is to test the causal relationship between the independent and dependent variable. It’s fairly simple, and each hypothesis can vary in how detailed it can be. We create if-then hypotheses all the time with our daily predictions. Here are some examples of hypotheses that use an if-then structure from daily life: 

• If I get enough sleep, I’ll be able to get more work done tomorrow.
• If the bus is on time, I can make it to my friend’s birthday party. 
• If I study every night this week, I’ll get a better grade on my exam. 

In each of these situations, you’re making a guess on how an independent variable (sleep, time, or studying) will affect a dependent variable (the amount of work you can do, making it to a party on time, or getting better grades). 

You may still be asking, “What is an example of a hypothesis used in scientific research?” Take one of the hypothesis examples from a real-world study on whether using technology before bed affects children’s sleep patterns. The hypothesis read s:

“We hypothesized that increased hours of tablet- and phone-based screen time at bedtime would be inversely correlated with sleep quality and child attention.”

It might not look like it, but this is an if-then statement. The researchers basically said, “If children have more screen usage at bedtime, then their quality of sleep and attention will be worse.” The sleep quality and attention are the dependent variables and the screen usage is the independent variable. (Usually, the independent variable comes after the “if” and the dependent variable comes after the “then,” as it is the independent variable that affects the dependent variable.) This is an excellent example of how flexible hypothesis statements can be, as long as the general idea of “if-then” and the independent and dependent variables are present.

#2: Null Hypotheses

Your if-then hypothesis is not the only one needed to complete a successful experiment, however. You also need a null hypothesis to test it against. In its most basic form, the null hypothesis is the opposite of your if-then hypothesis . When you write your null hypothesis, you are writing a hypothesis that suggests that your guess is not true, and that the independent and dependent variables have no relationship .

One null hypothesis for the cell phone and sleep study from the last section might say: 

“If children have more screen usage at bedtime, their quality of sleep and attention will not be worse.” 

In this case, this is a null hypothesis because it’s asking the opposite of the original thesis! 

Conversely, if your if-then hypothesis suggests that your two variables have no relationship, then your null hypothesis would suggest that there is one. So, pretend that there is a study that is asking the question, “Does the amount of followers on Instagram influence how long people spend on the app?” The independent variable is the amount of followers, and the dependent variable is the time spent. But if you, as the researcher, don’t think there is a relationship between the number of followers and time spent, you might write an if-then hypothesis that reads:

“If people have many followers on Instagram, they will not spend more time on the app than people who have less.”

In this case, the if-then suggests there isn’t a relationship between the variables. In that case, one of the null hypothesis examples might say:

“If people have many followers on Instagram, they will spend more time on the app than people who have less.”

You then test both the if-then and the null hypothesis to gauge if there is a relationship between the variables, and if so, how much of a relationship. 

4 Tips to Write the Best Hypothesis

If you’re going to take the time to hold an experiment, whether in school or by yourself, you’re also going to want to take the time to make sure your hypothesis is a good one. The best hypotheses have four major elements in common: plausibility, defined concepts, observability, and general explanation.

#1: Plausibility

At first glance, this quality of a hypothesis might seem obvious. When your hypothesis is plausible, that means it’s possible given what we know about science and general common sense. However, improbable hypotheses are more common than you might think. 

Imagine you’re studying weight gain and television watching habits. If you hypothesize that people who watch more than  twenty hours of television a week will gain two hundred pounds or more over the course of a year, this might be improbable (though it’s potentially possible). Consequently, c ommon sense can tell us the results of the study before the study even begins.

Improbable hypotheses generally go against  science, as well. Take this hypothesis example: 

“If a person smokes one cigarette a day, then they will have lungs just as healthy as the average person’s.” 

This hypothesis is obviously untrue, as studies have shown again and again that cigarettes negatively affect lung health. You must be careful that your hypotheses do not reflect your own personal opinion more than they do scientifically-supported findings. This plausibility points to the necessity of research before the hypothesis is written to make sure that your hypothesis has not already been disproven.

#2: Defined Concepts

The more advanced you are in your studies, the more likely that the terms you’re using in your hypothesis are specific to a limited set of knowledge. One of the hypothesis testing examples might include the readability of printed text in newspapers, where you might use words like “kerning” and “x-height.” Unless your readers have a background in graphic design, it’s likely that they won’t know what you mean by these terms. Thus, it’s important to either write what they mean in the hypothesis itself or in the report before the hypothesis.

Here’s what we mean. Which of the following sentences makes more sense to the common person?

If the kerning is greater than average, more words will be read per minute.

If the space between letters is greater than average, more words will be read per minute.

For people reading your report that are not experts in typography, simply adding a few more words will be helpful in clarifying exactly what the experiment is all about. It’s always a good idea to make your research and findings as accessible as possible. 

Good hypotheses ensure that you can observe the results. 

#3: Observability

In order to measure the truth or falsity of your hypothesis, you must be able to see your variables and the way they interact. For instance, if your hypothesis is that the flight patterns of satellites affect the strength of certain television signals, yet you don’t have a telescope to view the satellites or a television to monitor the signal strength, you cannot properly observe your hypothesis and thus cannot continue your study.

Some variables may seem easy to observe, but if you do not have a system of measurement in place, you cannot observe your hypothesis properly. Here’s an example: if you’re experimenting on the effect of healthy food on overall happiness, but you don’t have a way to monitor and measure what “overall happiness” means, your results will not reflect the truth. Monitoring how often someone smiles for a whole day is not reasonably observable, but having the participants state how happy they feel on a scale of one to ten is more observable. 

In writing your hypothesis, always keep in mind how you'll execute the experiment.

#4: Generalizability 

Perhaps you’d like to study what color your best friend wears the most often by observing and documenting the colors she wears each day of the week. This might be fun information for her and you to know, but beyond you two, there aren’t many people who could benefit from this experiment. When you start an experiment, you should note how generalizable your findings may be if they are confirmed. Generalizability is basically how common a particular phenomenon is to other people’s everyday life.

Let’s say you’re asking a question about the health benefits of eating an apple for one day only, you need to realize that the experiment may be too specific to be helpful. It does not help to explain a phenomenon that many people experience. If you find yourself with too specific of a hypothesis, go back to asking the big question: what is it that you want to know, and what do you think will happen between your two variables?

Hypothesis Testing Examples

We know it can be hard to write a good hypothesis unless you’ve seen some good hypothesis examples. We’ve included four hypothesis examples based on some made-up experiments. Use these as templates or launch pads for coming up with your own hypotheses.

Experiment #1: Students Studying Outside (Writing a Hypothesis)

You are a student at PrepScholar University. When you walk around campus, you notice that, when the temperature is above 60 degrees, more students study in the quad. You want to know when your fellow students are more likely to study outside. With this information, how do you make the best hypothesis possible?

You must remember to make additional observations and do secondary research before writing your hypothesis. In doing so, you notice that no one studies outside when it’s 75 degrees and raining, so this should be included in your experiment. Also, studies done on the topic beforehand suggested that students are more likely to study in temperatures less than 85 degrees. With this in mind, you feel confident that you can identify your variables and write your hypotheses:

If-then: “If the temperature in Fahrenheit is less than 60 degrees, significantly fewer students will study outside.”

Null: “If the temperature in Fahrenheit is less than 60 degrees, the same number of students will study outside as when it is more than 60 degrees.”

These hypotheses are plausible, as the temperatures are reasonably within the bounds of what is possible. The number of people in the quad is also easily observable. It is also not a phenomenon specific to only one person or at one time, but instead can explain a phenomenon for a broader group of people.

To complete this experiment, you pick the month of October to observe the quad. Every day (except on the days where it’s raining)from 3 to 4 PM, when most classes have released for the day, you observe how many people are on the quad. You measure how many people come  and how many leave. You also write down the temperature on the hour. 

After writing down all of your observations and putting them on a graph, you find that the most students study on the quad when it is 70 degrees outside, and that the number of students drops a lot once the temperature reaches 60 degrees or below. In this case, your research report would state that you accept or “failed to reject” your first hypothesis with your findings.

Experiment #2: The Cupcake Store (Forming a Simple Experiment)

Let’s say that you work at a bakery. You specialize in cupcakes, and you make only two colors of frosting: yellow and purple. You want to know what kind of customers are more likely to buy what kind of cupcake, so you set up an experiment. Your independent variable is the customer’s gender, and the dependent variable is the color of the frosting. What is an example of a hypothesis that might answer the question of this study?

Here’s what your hypotheses might look like: 

If-then: “If customers’ gender is female, then they will buy more yellow cupcakes than purple cupcakes.”

Null: “If customers’ gender is female, then they will be just as likely to buy purple cupcakes as yellow cupcakes.”

This is a pretty simple experiment! It passes the test of plausibility (there could easily be a difference), defined concepts (there’s nothing complicated about cupcakes!), observability (both color and gender can be easily observed), and general explanation ( this would potentially help you make better business decisions ).

Experiment #3: Backyard Bird Feeders (Integrating Multiple Variables and Rejecting the If-Then Hypothesis)

While watching your backyard bird feeder, you realized that different birds come on the days when you change the types of seeds. You decide that you want to see more cardinals in your backyard, so you decide to see what type of food they like the best and set up an experiment. 

However, one morning, you notice that, while some cardinals are present, blue jays are eating out of your backyard feeder filled with millet. You decide that, of all of the other birds, you would like to see the blue jays the least. This means you'll have more than one variable in your hypothesis. Your new hypotheses might look like this: 

If-then: “If sunflower seeds are placed in the bird feeders, then more cardinals will come than blue jays. If millet is placed in the bird feeders, then more blue jays will come than cardinals.”

Null: “If either sunflower seeds or millet are placed in the bird, equal numbers of cardinals and blue jays will come.”

Through simple observation, you actually find that cardinals come as often as blue jays when sunflower seeds or millet is in the bird feeder. In this case, you would reject your “if-then” hypothesis and “fail to reject” your null hypothesis . You cannot accept your first hypothesis, because it’s clearly not true. Instead you found that there was actually no relation between your different variables. Consequently, you would need to run more experiments with different variables to see if the new variables impact the results.

Experiment #4: In-Class Survey (Including an Alternative Hypothesis)

You’re about to give a speech in one of your classes about the importance of paying attention. You want to take this opportunity to test a hypothesis you’ve had for a while: 

If-then: If students sit in the first two rows of the classroom, then they will listen better than students who do not.

Null: If students sit in the first two rows of the classroom, then they will not listen better or worse than students who do not.

You give your speech and then ask your teacher if you can hand out a short survey to the class. On the survey, you’ve included questions about some of the topics you talked about. When you get back the results, you’re surprised to see that not only do the students in the first two rows not pay better attention, but they also scored worse than students in other parts of the classroom! Here, both your if-then and your null hypotheses are not representative of your findings. What do you do?

This is when you reject both your if-then and null hypotheses and instead create an alternative hypothesis . This type of hypothesis is used in the rare circumstance that neither of your hypotheses is able to capture your findings . Now you can use what you’ve learned to draft new hypotheses and test again! 

Key Takeaways: Hypothesis Writing

The more comfortable you become with writing hypotheses, the better they will become. The structure of hypotheses is flexible and may need to be changed depending on what topic you are studying. The most important thing to remember is the purpose of your hypothesis and the difference between the if-then and the null . From there, in forming your hypothesis, you should constantly be asking questions, making observations, doing secondary research, and considering your variables. After you have written your hypothesis, be sure to edit it so that it is plausible, clearly defined, observable, and helpful in explaining a general phenomenon.

Writing a hypothesis is something that everyone, from elementary school children competing in a science fair to professional scientists in a lab, needs to know how to do. Hypotheses are vital in experiments and in properly executing the scientific method . When done correctly, hypotheses will set up your studies for success and help you to understand the world a little better, one experiment at a time.

What’s Next?

If you’re studying for the science portion of the ACT, there’s definitely a lot you need to know. We’ve got the tools to help, though! Start by checking out our ultimate study guide for the ACT Science subject test. Once you read through that, be sure to download our recommended ACT Science practice tests , since they’re one of the most foolproof ways to improve your score. (And don’t forget to check out our expert guide book , too.)

If you love science and want to major in a scientific field, you should start preparing in high school . Here are the science classes you should take to set yourself up for success.

If you’re trying to think of science experiments you can do for class (or for a science fair!), here’s a list of 37 awesome science experiments you can do at home

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The Hierarchy-of-Hypotheses Approach: A Synthesis Method for Enhancing Theory Development in Ecology and Evolution

Department of Biodiversity Research and Systematic Botany, University of Potsdam, Potsdam, Germany

Department of Restoration Ecology, Technical University of Munich, Freising, Germany

Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany

Carlos A Aguilar-Trigueros

Institute of Biology, Freie Universität, Berlin, Berlin, Germany

Isabelle Bartram

Institute of Sociology, University of Freiburg, Freiburg

Raul Rennó Braga

Universidade Federal do Paraná, Laboratório de Ecologia e Conservação, Curitiba, Brazil

Gregory P Dietl

Paleontological Research Institution and the Department of Earth and Atmospheric Sciences at Cornell University, Ithaca, New York

Martin Enders

Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany

David J Gibson

School of Biological Sciences, Southern Illinois University Carbondale, Carbondale, Illinois

Lorena Gómez-Aparicio

Instituto de Recursos Naturales y Agrobiología de Sevilla, CSIC, LINCGlobal, Sevilla, Spain

Pierre Gras

Department of Ecological Dynamics, Leibniz Institute for Zoo and Wildlife Research (IZW), also in Berlin, Germany

Department of Conservation Biology, Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany

Sophie Lokatis

Christopher j lortie.

Department of Biology, York University, York, Canada, as well as with the National Center for Ecological Analysis and Synthesis, University of California Santa Barbara, Santa Barbara, California

Anne-Christine Mupepele

Chair of Nature Conservation and Landscape Ecology, University of Freiburg, Freiburg, and the Senckenberg Biodiversity and Climate Research Centre, Frankfurt am Main, both in Germany

Stefan Schindler

Environment Agency Austria and University of Vienna's Division of Conservation, Biology, Vegetation, and Landscape Ecology, Vienna, Austria, and his third affiliation is with Community Ecology and Conservation, Czech University of Life Sciences Prague, Prague, Czech Republic, Finally

Jostein Starrfelt

University of Oslo's Centre for Ecological and Evolutionary Synthesis and with the Norwegian Scientific Committee for Food and Environment, Norwegian Institute of Public Health, both in Oslo, Norway

Alexis D Synodinos

Department of Plant Ecology and Nature Conservation, University of Potsdam, Potsdam, Germany

Centre for Biodiversity Theory and Modelling, Theoretical, and Experimental Ecology Station, CNRS, Moulis, France

Jonathan M Jeschke

Associated data.

In the current era of Big Data, existing synthesis tools such as formal meta-analyses are critical means to handle the deluge of information. However, there is a need for complementary tools that help to (a) organize evidence, (b) organize theory, and (c) closely connect evidence to theory. We present the hierarchy-of-hypotheses (HoH) approach to address these issues. In an HoH, hypotheses are conceptually and visually structured in a hierarchically nested way where the lower branches can be directly connected to empirical results. Used for organizing evidence, this tool allows researchers to conceptually connect empirical results derived through diverse approaches and to reveal under which circumstances hypotheses are applicable. Used for organizing theory, it allows researchers to uncover mechanistic components of hypotheses and previously neglected conceptual connections. In the present article, we offer guidance on how to build an HoH, provide examples from population and evolutionary biology and propose terminological clarifications.

In many disciplines, the volume of evidence published in scientific journals is steadily increasing. In principle, this increase should make it possible to describe and explain complex systems in much greater detail than ever before. However, an increase in available information does not necessarily correspond to an increase in knowledge and understanding (Jeschke et al. 2019 ). Publishing results in scientific journals and depositing data in public archives does not guarantee their practical application, reuse, or the advancement of theory. We suggest that this situation can be improved by the development, establishment, and regular application of methods that have the explicit aim of linking evidence and theory.

An important step toward more efficiently exploiting results from case studies is synthesis (for this and other key terms, see box 1 ). There is a wealth of methods available for statistically combining the results of multiple studies (Pullin et al. 2016 , Dicks et al. 2017 ). These methods enable the synthesis of research results stemming from different studies that address a common question (Koricheva et al. 2013 ). In the environmental sciences, evidence synthesis has increased both in frequency and importance (Lortie 2014 ), seeking to make empirical evidence readily available and more suitable as a basis for decision-making (e.g., evidence-based decision making; Sutherland 2006 , Diefenderfer et al. 2016 , Pullin et al. 2016 , Cook et al. 2017 , Dicks et al. 2017 ). Moreover, methodological guidelines have been developed, and web portals implemented to collect and synthesize the results of primary studies. Prime examples are the platforms www.conservationevidence.com and www.environmentalevidence.org , alongside the European Union–funded projects EKLIPSE ( www.eklipse-mechanism.eu ) and BiodiversityKnowledge (Nesshöver et al. 2016 ). These initiatives have promoted significant advances in the organization and assessment of evidence and the implementation of synthesis, thus allowing for a comprehensive representation of applied knowledge in environmental sciences.

Box 1. Glossary.

Evidence. Available body of data and information indicating whether a belief or proposition is true or valid (Howick 2011 , Mupepele et al. 2016 ). These data and information can, for example, stem from an empirical observation, model output, or simulation.

Hypothesis. An assumption that (a) is based on a formalized or nonformalized theoretical model of the real world and (b) can deliver one or more testable predictions (after Giere et al. 2005 ).

Mechanistic hypothesis . Narrowed version of an overarching hypothesis, resulting from specialization or decomposition of the unspecified hypothesis with respect to assumed underlying causes.

Operational hypothesis. Narrowed version of an overarching hypothesis, accounting for a specific study design. Operational hypotheses explicate which method (e.g., which study system or research approach) is used to study the overarching hypothesis.

Overarching hypothesis. Unspecified assumption derived from a general idea, concept or major principle (i.e., from a general ­theoretical model).

Prediction. Statement about how data (i.e., measured outcome of an experiment or observation) should look if the underlying hypothesis is true.

Synthesis. Process of identifying, compiling and combining relevant knowledge from multiple sources.

Theory. A high-level—that is, general—system of conceptual constructs or devices to explain and understand ecological, evolutionary or other phenomena and systems (adapted from Pickett et al. 2007 ). Theory can consist of a worked out, integrated body of mechanistic rules or even natural laws, but it may also consist of a loose collection of conceptual frameworks, ideas and hypotheses.

Fostering evidence-based decision-making is crucial to solving specific applied problems. However, findings resulting from these applied approaches for evidence synthesis are usually not reconnected to a broader body of theory. Therefore, they do not consistently contribute to a structured or targeted advancement of theory—for example, by assessing the usefulness of ideas. It is a missed opportunity to not feed this synthesized evidence back into theory. A similar lack of connection to theory has been observed for studies addressing basic research questions (e.g., Jeltsch et al. 2013 , Scheiner 2013 ). Evidence feeding back into theory, subsequently leading to further theory development, would become a more appealing, simpler and, therefore, more common process if there were well described and widely accepted methods. A positive example in this respect is structural equation modeling, especially if combined with metamodels (Grace et al. 2010 ). With this technique, theoretical knowledge directly feeds into mathematical models, and empirical data are then used to select the model best matching the observations.

In the present article, we provide a detailed description of a relatively new synthesis method—the hierarchy-of-hypotheses (HoH) approach (Jeschke et al. 2012 , Heger et al. 2013 )—that is complementary to existing knowledge synthesis tools. This approach offers the opportunity to organize evidence and ideas, and to create and display links between single study results and theory. We suggest that the representation of broad ideas as nested hierarchies of hypotheses can be powerful and can be used to more efficiently connect single studies to a body of theory. Empirical studies usually formulate very specific hypotheses, derive predictions from these about expected data, and test these predictions in experiments or observations. With an HoH, it can be made explicit which broader ideas these specific hypotheses are linked to. The specific hypotheses can be characterized and visualized as subhypotheses of a broader idea or theory. Therefore, it becomes clear that the single study, although necessarily limited in its scope, is testing an important aspect of a broader idea or theory. Similarly, an HoH can be used to organize a body of literature that is too heterogeneous for statistical meta-analysis. It can be linked with a systematic review of existing studies, so that the studies and their findings are organized and hierarchically structured, thus visualizing which aspects of an overarching question or hypothesis each study is addressing. Alternatively, the HoH approach can be used to refine a broad idea on theoretical grounds and to identify different possibilities of how an idea, concept, or hypothesis can become more specific, less ambiguous, and better structured. Taken together, the approach can help to strengthen the theoretical foundations of a research field.

In this context, it is important to clarify what is meant by hypothesis . In the present article, we apply the terminology offered by the philosopher of science Ronald Giere and colleagues (Giere et al. 2005 , see also Griesemer 2018 ). Accordingly, a hypothesis provides the connection of the (formalized or nonformalized) theoretical model that a researcher has, describing how a specific part of the world works in theory, to the real world by asserting that the model fits that part of the world in some specified aspect. A hypothesis needs to be testable, thus allowing the investigation of whether the theoretical model actually fits the real world. This is done by deriving one or more predictions from the hypothesis that state how data (gathered in an observation or experiment) should look if the hypothesis is true.

The HoH approach has already been introduced as a tool for synthesis in invasion ecology (Jeschke et al. 2012 , Heger et al. 2013 , Heger and Jeschke 2014 , Jeschke and Heger 2018a ). So far, however, explicit and consistent guidance on how to build a hierarchy of hypotheses has not been formally articulated. The primary objective of this publication therefore is to offer a concrete, consistent, and refined description for those who want to use this tool or want to adopt it to their discipline. Furthermore, we want to stimulate methodological discussions about its further development and improvement. In the following, we outline the main ideas behind the HoH approach and the history of its development, present a primer for creating HoHs, provide examples for applications within and outside of invasion ecology, and discuss its strengths and limitations.

The hierarchy-of-hypotheses approach

The basic tenet behind the HoH approach is that complexity can often be handled by hierarchically structuring the topic under study (Heger and Jeschke 2018c ). The approach has been developed to clarify the link between big ideas, and experiments or surveys designed to test them. Usually, experiments and surveys actually test predictions derived from smaller, more specific ideas that represent an aspect or one manifestation of the big idea. Different studies all addressing a joint major hypothesis consequently often each address different versions of it. This diversity makes it hard to reconcile their results. The HoH approach addresses this challenge by dividing the major hypothesis into more specific formulations or subhypotheses. These can be further divided until the level of refinement allows for direct empirical testing. The result is a tree that visually depicts different ways in which a major hypothesis can be formulated. The empirical studies can then be explicitly linked to the branch of the tree they intend to address, thus making a conceptual and visual connection to the major hypothesis. Hierarchical nestedness therefore allows one to structure and display relationships between different versions of an idea, and to conceptually collate empirical tests addressing the same overall question with divergent approaches. A hierarchical arrangement of hypotheses has also been suggested by Pickett and colleagues ( 2007 ) in the context of the method of pairwise alternative hypothesis testing (or strong inference, Platt 1964 ). However, we are not aware of studies that picked up on or further developed this idea.

The HoH approach in its first version (Jeschke et al. 2012 , Heger et al. 2013 , Heger and Jeschke 2014 ) was not a formalized method with a clear set of rules on how to proceed. It emerged and evolved during a literature synthesis project through dealing with the problem of how to merge results of a set of highly diverse studies without losing significant information on what precisely these studies were addressing. In that first iteration of the HoH method, the branches of the hierarchy were selected by the respective author team, on the basis of expert knowledge and assessment of published data. Therefore, pragmatic questions guided the creation of the HoH (e.g., which kind of branching helps group studies in a way that enhances interpretation? ). Through further work on the approach, helpful discussions with colleagues, and critical comments (Farji-Brener and Amador-Vargas 2018 , Griesemer 2018 , Scheiner and Fox 2018 ), suggestions for its refinement were formulated (Heger and Jeschke 2018b , 2018c ). The present article amounts to a further step in the methodological development and refinement of the HoH approach, including terminological clarifications and practical suggestions.

A primer for building a hierarchy of hypotheses

With the methodological guidance provided in the following, we take the initial steps toward formalizing the application of the HoH approach. However, we advocate that its usage should not be confined by rules that are too strict. Although we appreciate the advantages of strict methodological guidelines, such as those provided by The Collaboration for Environmental Evidence ( 2018 ) for synthesizing evidence in systematic reviews, we believe that when it comes to conceptual work and theory development, room is needed for creativity and methodological flexibility.

Applying the HoH approach involves four steps (figure  1 ). We distinguish two basic aims for creating an HoH: organizing evidence and organizing theory. These basic aims reflect the distinction between empirical and theoretical modeling approaches in Griesemer ( 2013 ). Creating and displaying links between evidence and theory can be part of the process in either case. In the first case (i.e., if the aim is to organize evidence), the process starts with a diverse set of empirical results and the question of how these can be grouped to enhance their joint interpretation or further analysis. In the second case (i.e., if the aim is to organize theory), the process of creating the hierarchy starts with decomposing an overarching hypothesis. An HoH allows one to make the meaning of this overarching hypothesis more explicit by formulating its components as separate subhypotheses from which testable, specific predictions can be derived.

Workflow for the creation of a hierarchy of hypotheses. For a detailed explanation, see the main text.

The starting point for an HoH-based analysis in both cases, for organizing evidence as well as for organizing theory, is the identification of a focal hypothesis. This starting point is followed by the compilation of information (step 1 in figure  1 ). Which information needs to be compiled depends on whether the aim is structuring and synthesizing empirical evidence provided by a set of studies (e.g., Jeschke and Heger 2018a and example 1 below) or whether the research interest is more in the theoretical structure and subdivision of the overarching hypothesis (see examples 2 and 3 below). The necessary information needs to be gathered by means of a literature review guided by expert knowledge. Especially if the aim is to organize evidence, we recommend applying a standardized procedure (e.g., PRISMA, Moher et al. 2015 , or ROSES, Haddaway et al. 2018 ) and recording the performed steps.

The next step is to create the hierarchy (step 2 in figure  1 ). If the aim is to organize evidence, step 1 will have led to the compilation of a set of studies empirically addressing the overarching hypothesis or a sufficiently homogeneous overarching theoretical framework. In step 2, these studies will need to be grouped. Depending on the aim of the study, it can be helpful to group the empirical tests of the overarching hypothesis according to study system (e.g., habitat, taxonomic group) or research approach (e.g., measured response variable). For example, in tests of the biotic resistance hypothesis in invasion ecology, which posits that an ecosystem with high biodiversity is more resistant against nonnative species than an ecosystem with lower biodiversity, Jeschke and colleagues ( 2018a ) grouped empirical tests according to how the tests measured biodiversity and resistance against nonnative species. Some tests measured biodiversity as species richness, others as evenness or functional richness. The groups resulting from such considerations can be interpreted as representing operational hypotheses, because they specify the general hypothesis by accounting for diverse research approaches—that is, options for measuring the hypothesized effect (see also Griesemer 2018 , Heger and Jeschke 2018c , as well as figure  2 a and example 1 below). In such cases, we recommend displaying all resulting subhypotheses, if feasible.

Three different types of branching in a hierarchy of hypotheses. The branching example shown in (a) is inspired by example 1 in the main text, (b) by example 2 (see also figure  3 b), and (c) by example 3 (see also figure  4 ).

If the aim is to organize theory, the overarching hypothesis is split into independent components on the basis of conceptual considerations (figure  2 b and  2 c). This splitting of the overarching hypothesis can be done by creating branches according to which factors could have caused the respective process or pattern (see example 2 below, figure  2 b).

Broad, overarching hypotheses often consist of several complementary partial arguments that are necessary elements. Consider the question why species often are well adapted to their biotic environment. A common hypothesis suggests that enduring interaction with enemies drives evolutionary changes, thus leading to adaptations of prey to their enemies (see example 3). This hypothesis presupposes that species face increasing risks from enemies but also that species’ traits evolve in response to the changed risk (figure  2 c and example 3 below). Decomposing overarching hypotheses into their partial arguments by formulating separate mechanistic hypotheses can enhance conceptual clarity and elucidate that sometimes, studies combined under one header are in fact addressing very different things.

For any type of branching, it is critical to identify components or groups (i.e., branches) that are mutually exclusive and not overlapping, so that an unambiguous assignment of single cases or observations into a box (i.e., subhypothesis) can be possible. If this is not feasible, it may be necessary to use conceptual maps, networks or Venn diagrams rather than hierarchical structures (figure  1 , step 2; also see supplemental table S1). Therefore, care should be taken not to impose a hierarchical structure in cases where it is not helpful.

For many applications, the process of building an HoH can stop at this step, and a publication of the results can be considered (step 4). The resulting HoH can, for example, show the connection of a planned study to a body of theory, explicate and visualize the complexity of ideas implicitly included in a major hypothesis, or develop a research program around an overarching idea.

If the aim is to identify research gaps, or to assess the generality or range of applicability of a major hypothesis, however, a further step must be taken (figure  1 , step 3a): The HoH needs to be linked to empirical data. In previous studies (e.g., Jeschke and Heger 2018a ), this step was done by assigning empirical studies to the subhypotheses they addressed and assessing the level of supporting evidence for the predictions derived from each hypothesis or subhypothesis. This assignment of studies to subhypotheses can be done either by using expert judgment or by applying machine learning algorithms (for further details, see Heger and Jeschke 2014 , Jeschke and Heger 2018a , Ryo et al. 2019 ). Depending on the research question, the available resources and the structure of the data, the level of evidence can be assessed for each subhypothesis as well as for the higher-level hypotheses and can then be compared across subhypotheses. Such a comparison can provide information on the generality of an overarching hypothesis (i.e., its unifying power and breadth of applicability) or on the range of conditions under which a mechanism applies (see supplemental table S2 for examples). Before an HoH organizing theory is connected to empirical evidence, it will be necessary in most cases to include operational hypotheses at the lower levels, specifying, for example, different possible experimental approaches.

The hierarchical approach can additionally be used to connect the HoH developed in step 2 to a related body of theory. For example, Heger and colleagues ( 2013 ) suggested that the existing HoH on the enemy release hypothesis (see example 1 below) was conceptually connected to another well-known hypothesis—the novel weapons hypothesis. As a common overarching hypothesis addressing the question why species can successfully establish and spread outside of their native range, they suggested the “lack of eco-evolutionary experience hypothesis”; the enemy release and the novel weapons hypotheses are considered subhypotheses of this overarching hypothesis. This optional step can therefore help to create missing links within a discipline or even across disciplines.

Performing this step requires the study of the related body of theory, looking for conceptual overlaps and overarching topics. It may turn out that hypotheses, concepts, and ideas exist that are conceptually linked to the focal overarching hypothesis but that these links are nonhierarchical. In these cases, it can be useful to build hypothesis networks and apply clustering techniques to identify underlying structures (see, e.g., Enders et al. 2020 ). This step can also be applied in cases in which the HoH has been built to organize evidence.

Once the HoH is finalized, it can be published in order to enter the public domain and facilitate the advancement of the methodology and theory development. For the future, we envision a platform for the publication of HoHs to make the structured representations of research topics available not only via the common path of journal publications. The webpage www.hi-knowledge.org (Jeschke et al. 2018b ) is a first step in this direction and is planned to allow for the upload of results in the future.

Application of the HoH approach: Three examples

We will now exemplify the process of creating an HoH. The first example starts with a diverse set of empirical tests addressing one overarching hypothesis (i.e., with the aim to organize evidence), whereas the second and third examples start with conceptual considerations on how different aspects are linked to one overarching hypothesis (in the present article, the aim is to organize theory).

Example 1: the enemy release hypothesis as a hierarchy

The first published study showing a detailed version of an HoH focused on the enemy release hypothesis (Heger and Jeschke 2014 ). This is a prominent hypothesis in invasion biology (Enders et al. 2018 ). With respect to the research question of why certain species become invasive—that is, why they establish and spread in a new range—it posits, “The absence of enemies is a cause of invasion success” (e.g., Keane and Crawley 2002 ). With a systematic literature review, Heger and Jeschke ( 2014 ) identified studies addressing this hypothesis. This review revealed that the hypothesis has been tested in many different ways. After screening the empirical tests with a specific focus on which research approach had been used, the authors decided to use three branching criteria: the indicator for enemy release (actual damage, infestation with enemies or performance of the invader); the type of comparison (alien versus natives, aliens in native versus invaded range or invasive versus noninvasive aliens); and the type of enemies (specialists or generalists). On the basis of these criteria, Heger and Jeschke created a hierarchically organized representation of the hypothesis's multiple aspects. The order in which the three criteria were applied to create the hierarchy in this case was based on practical considerations. Empirical studies providing evidence were then assigned to the respective branch of the corresponding hierarchy to reveal specific subhypotheses that were more and others that were less supported (Heger and Jeschke 2014 ).

In later publications, Heger and Jeschke suggested some optional refinements of the original approach (Heger and Jeschke 2018b , 2018c ). One of the suggestions was to distinguish between mechanistic hypotheses (originally termed working hypotheses) and operational hypotheses as different forms of subhypotheses when building the hierarchy. Mechanistic hypotheses serve the purpose of refining the broad, overarching idea in a conceptual sense (figure  2 b and  2 c), whereas operational hypotheses refine the hypotheses by accounting for the diversity of study approaches (figure  2 a).

The enemy release hypothesis example indicates that it can be useful to apply different types of branching criteria within one study. Heger and Jeschke ( 2014 ) looked for helpful ways of grouping diverse empirical tests. Some of the branches they decided to create were based on differences in the research methods, such as the distinction between comparisons of aliens versus natives, and comparisons of aliens in their native versus the invaded range (figure  2 a). Other branches explicate complementary partial arguments contained in the major hypothesis: Studies in which the researchers asked whether aliens are confronted with fewer enemies were separated from those in which they asked whether aliens that are released show enhanced performance.

In this example, the HoH approach was used to organize evidence and therefore to expose the variety of manifestations of the enemy release hypothesis and to display the level of evidence for each branch of the HoH (see Heger and Jeschke 2018b and supplemental table S2 for an interpretation of the results).

Example 2: illustrating the potential drivers of the snowshoe hare–canadian lynx population cycles

Understanding and predicting the spatiotemporal dynamics of populations is one of ecology's central goals (Sutherland et al. 2013 ), and population ecology has a long tradition of trying to understand causes for observed patterns in population dynamics. However, research efforts do not always produce clear conclusions, and often lead to competing explanatory hypotheses. A good example, which has been popularized through textbooks, is the 8–11-year synchronized population cycles of the snowshoe hare ( Lepus americanus ) and the Canadian lynx ( Lynx canadensis ; figure  3 a). From eighteenth- to nineteenth-century fur trapping records across the North American boreal and northern temperate forests, it has been known that predator (lynx) and prey (hare) exhibit broadly synchronous population cycles. Research since the late 1930s (MacLulich 1937 , Elton and Nicholson 1942 ) has tried to answer the question how these patterns are produced. A linear food chain of producer (vegetation)—primary consumer or prey (snowshoe hares)—secondary consumer or predator (Canadian lynx) proved too simplistic as an explanation (Stenseth et al. 1997 ). Instead, multiple drivers could have been responsible, resulting in the development of multiple competing explanations (Oli et al. 2020 ).

(a) The population cycle of snowshoe hare and Canadian lynx and (b) a hierarchy of hypotheses illustrating its potential drivers. The hypotheses (blue boxes) branch from the overarching hypothesis into more and more precise mechanistic hypotheses and are confronted with empirical tests (arrows leading to grey boxes) at lower levels of the hierarchy. The broken lines indicate where the hierarchy may be extended. Sources: The figure is based on the summary of snowshoe hare–Canadian lynx research (Krebs et al. 2001 , Krebs et al. 2018 and references therein). Panel (a) is reprinted with permission from OpenStax Biology, Chapter 45.6 Community Ecology, Rice University Publishers, Creative Commons Attribution License (by 4.0).

In the present article, we created an HoH to organize the current suggestions on what drives the snowshoe hare–lynx cycle (figure  3 b). The aim of this exercise is to visualize conceptual connections rooted in current population ecological theory and, therefore, to enhance understanding of the complexity of involved processes.

A major hypothesis in population ecology is that populations are regulated by the interaction between biotic and abiotic factors. This regulation can either happen through processes coupled with the density of the focal organisms (density-dependent processes) or through density-independent processes, such as variability in environmental conditions or disturbances. This conceptual distinction can be used to branch out multiple mechanistic hypotheses that specify particular hypothetical mechanisms inducing the observed cycles. For example, potential drivers of the hare–lynx cycles include density-dependent mechanisms linked to bottom-up resource limitation and top-down predation, and density-independent mechanisms related to 10-year sun spot cycles. Figure  3 b also summarizes the kind of experiments that have been performed and how they relate to the corresponding mechanistic hypotheses. For example, food supplementation and fertilization experiments were used to test the resource limitation hypothesis and predator exclusion experiments to test the hypothesis that hare cycles are induced by predator abundance. Figure  3 b therefore highlights why it can be useful to apply very different types of experiments to test one broad overarching hypothesis.

The experiments that have been performed suggest that the predator–prey cycles result from an interaction between predation and food supplies combined with other modifying factors including social stress, disease and parasitism (Krebs et al., 2001 , 2018 ). Other experiments can be envisioned to test additional hypotheses, such as snow-removal experiments to test whether an increase in winter snow, induced by changed sun spot activity, causes food shortages and high hare mortality (Krebs et al. 2018 ).

In this example, alternate hypotheses are visually contrasted, and the different experiments that have been done are linked to the nested structure of possible drivers. This allows one to intuitively grasp the conceptual contribution of evidence stemming from each experiment to the overall explanation of the pattern. In a next step, quantitative results from these experiments could be summarized and displayed as well—for example, applying formal meta-analyses to summarize and display evidence stemming from each type of experiment. This example highlights how hierarchically structuring hypotheses can help to visually organize ideas about which drivers potentially cause a pattern in a complex system (for a comparison, see figure 11 in Krebs et al. 2018 ).

Example 3: the escalation hypothesis of evolution

The escalation hypothesis is a prominent hypothesis in evolutionary biology. In response to the question why species often seem to be well adapted to their biotic environment, it states that enemies are predominant agents of natural selection, and that enduring interactions with enemies brings about long-term evolutionary trends in the morphology, behavior, and distribution of organisms. Escalation, however, is an intrinsically costly process that can proceed only as long as resources are both available and accessible. Since the publication of Vermeij's book Evolution and Escalation in 1987, which is usually considered the start of the respective modern research program, escalation has represented anything but a fixed theory in its structure or content. The growth of escalation studies has led to the development of an increasing number of specific subhypotheses derived from Vermeij's original formulation and therefore to an expansion of the theoretical domain of the escalation hypothesis. Escalation has been supported by some tests but questioned by others.

Similar as in example 2, an HoH can contribute to conceptual clarity by structuring the diversity of escalation ideas that have been proposed (figure  4 ; Dietl 2015 ). To create the HoH for the escalation hypothesis, instead of assembling empirical studies that have tested it, Dietl ( 2015 ) went through the conceptual exercise of arranging existing escalation ideas on the basis of expert knowledge.

A hierarchy of hypotheses for the escalation hypothesis in evolutionary biology. The broken lines indicate where the hierarchy may be extended.

In its most generalized formulation—that is, “enemies direct evolution”—the escalation hypothesis can be situated at the top of a branch (figure  4 ) along with other hypotheses positing the importance of interaction-related adaptation, such as Van Valen's ( 1973 ) Red Queen hypothesis and hypotheses derived from Thompson's ( 2005 ) geographic mosaic theory of coevolution. Vermeij's original ( 1987 ) formulation of the hypothesis of escalation is actually composed of two separate testable propositions: “Biological hazards due to competitors and predators have become more severe over the course of time in physically comparable habitats” ­(p. 49 in Vermeij 1987 ) and “traits that enhance the competitive and antipredatory capacities of individual organisms have increased in incidence and in degree of expression over the course of time within physically similar habitats” (p. 49 in Vermeij 1987 ). As is the case with other composite hypotheses, these ideas must be singled out before the overarching idea can be unambiguously tested. This requirement creates a natural branching point in the escalation HoH, the risk and response subhypotheses (figure  4 ).

Other lower-level hypotheses and aspects of the risk and response subhypotheses are possible. The risk side of the HoH can be further branched into subhypotheses suggesting either that the enemies evolved enhanced traits through time (e.g., allowing for greater effectiveness in prey capture) or that interaction intensity has increased through time (e.g., because of greater abundance or power of predators; figure  4 ). The response side of the HoH also can be further branched into several subhypotheses (all addressed by Vermeij 1987 ). In particular, species’ responses could take the form of a trend toward more rapid exploitation of resources through time, an increased emphasis on traits that enable individuals to combat or interfere with competitors, a trend toward reduced detectability of prey through time, a trend of increased mobility (that is, active escape defense) through time, or an increase in the development of armor (or passive defense) through time. Arranging these different options of how escalation can manifest in boxes connected to a hierarchical structure helps to gain an overview. The depiction of subhypotheses in separate boxes does not indicate that the authors believe there is no interaction possible among these factors. For example, the evolution of enhanced traits may lead to an increase in interaction intensity. The presented HoH should be viewed only as one way to organize theory. It puts emphasis on the upward connections of subhypotheses to more general hypotheses. If the focus is more on interactions among different factors, other graphical and conceptual approaches may be more helpful (e.g., causal networks; for an example, see Gurevitch et al. 2011 ).

The HoH shown in figure  4 can be used as a conceptual backbone for further work in this field. Also, it can be related to existing evidence. This HoH will allow identification of data gaps and an understanding of which branches of the tree receive support by empirical work and therefore should be considered important components of escalation theory.

Strengths and limits of the HoH approach

The HoH approach can help to organize theory, to organize evidence, and to conceptualize and visualize connections of evidence to theory. Previously published examples of HoHs (e.g., Jeschke and Heger 2018a ) and example 1 given above demonstrate its usefulness for organizing evidence, for pointing out important differences among subhypotheses and for conceptually and graphically connecting empirical results to a broader theoretical idea. Such an HoH can make the rationale underlying a specific study explicit and can elucidate the conceptual connection of the study to a concrete theoretical background.

Applying the HoH approach can also help disclose knowledge gaps and biases (Braga et al. 2018 ) and can help reveal which research approaches have been used to assess an overarching idea (for examples, see Jeschke and Heger 2018a ; other methods can be used to reach these aims too—e.g., systematic maps; Pullin et al. 2016 , Collaboration for Environmental Evidence 2018 ). On the basis of such information, future research can be focused on especially promising areas or methods.

Besides such descriptive applications, the HoH approach can be combined with evidence assessment techniques (step 3a in figure  1 ). It can help to analyze the level of evidence for subhypotheses and therefore deliver the basis for discussing their usefulness and range of applicability (table S2; Jeschke and Heger 2018). Recent studies demonstrate that this kind of application can be useful for research outside of ecology as well—for example, in biomedical research (Bartram and Jeschke 2019 ) or even in a distant field like company management research (Wu et al. 2019 ).

We did not detail in the present article how the confrontation of hypotheses with evidence in an HoH can be done, but in previous work it was shown that this step can deliver the basis for enhancing theory. For example, the HoH-based literature analyses presented in Jeschke and Heger ( 2018a ) showed that several major hypotheses in invasion biology are only weakly supported by evidence. The authors consequently suggested to reformulate them (Jeschke and Heger 2018b ) and to explicitly assess their range of applicability (Heger and Jeschke 2018a ). Because an HoH visually connects data and theory, the approach motivates one to feed empirical results back into theory and, therefore, use them for improving theory. It is our vision that in the future, theory development in ecology and evolution could largely profit from a regular application of the HoH approach. Steps to improve theory can include highlighting strongly supported subhypotheses, pointing out hypotheses with low unification power and breadth of applicability, shedding light on previously unnoticed connections, and revealing gaps in research.

The examples on the hare–lynx cycles and the escalation hypothesis showed that the HoH approach can also guide theory-driven reasoning in both the ecological and evolutionary domains, respectively. That is, the HoH approach can allow the reconsideration and reorganization of conceptual ideas without directly referring to data. Major hypotheses or research questions are usually composed of several elements, and above, we suggest how these elements can be exposed and visualized (figure  2 b and  2 c). In this way, applying the HoH approach can help to enhance conceptual clarity by displaying different meanings and components of broad concepts. Conceptual clarity is not only useful to avoid miscommunication or misinterpretation of empirical results, but we expect that it will also facilitate theory development by enhancing accurate thinking and argumentation.

In addition, the nested, hierarchical structure invites looking for connections upward: Figure  4 shows the escalation hypothesis as one variant of an even broader hypothesis, positing that “Species interactions direct evolution.” This in turn can enhance the future search for patterns and mechanisms across unconnected study fields. A respective example can be found in Schulz and colleagues ( 2019 ). In that article, the authors used the HoH approach to organize twelve hypotheses each addressing the roles that antagonists play during species invasions. By grouping the hypotheses in a hierarchically nested way, Schulz and colleagues showed their conceptual relatedness, which had not been demonstrated before.

In the future, the HoH approach could also be used for creating interdisciplinary links. There are many research questions that are being addressed in several research areas in parallel, using different approaches and addressing different aspects of the overall question. In an HoH, such connections could be revealed. Heger and colleagues ( 2019 ) suggested a future application of the HoH approach for organizing and structuring research on effects of global change on organisms, communities, and ecosystems. Under the broad header of “ecological novelty,” more specific research questions addressed in various disciplines (e.g., climate change research, biodiversity research, urban ecology, restoration ecology, evolutionary ecology, microbial ecology) could be organized and therefore conceptually connected.

Importantly, the HoH approach can be easily combined with existing synthesis tools. For example, as was outlined above and in figure  1 , a systematic literature review can be used to identify and structure primary studies to be used for building an HoH. Statistical approaches, such as machine learning, can be used to optimize branching with respect to levels of evidence (Ryo et al. 2019 ), and empirical data structured in an HoH can be analyzed with formal meta-analysis—for example, separately for each subhypothesis (Jeschke and Pyšek 2018 ). In future applications, an HoH could also be used to visualize the results of a research-weaving process, in which systematic mapping is combined with bibliometric approaches (Nakagawa et al. 2019 ). Furthermore, HoHs can be linked to a larger network. An example is the website https://hi-knowledge.org/invasion-biology/ (Jeschke et al. 2018b ) where the conceptual connections of 12 major hypotheses of invasion ecology are displayed as a hierarchical network. We believe that the combination of HoH with other knowledge synthesis tools, such as Venn diagrams, ontologies, controlled vocabularies, and systematic maps, can be useful as well and should be explored in the future.

We emphasize, however, that the HoH method is by far no panacea for managing complexity. Not all topics interesting for scientific inquiry can be organized hierarchically, and imposing a hierarchy may even lead to wrong conclusions, thus actually hindering theory development. For example, to focus a conceptual synthesis on one major overarching hypothesis may conceal that other factors not addressed by this single hypothesis have a major effect on underlying processes as well. Evidence assessed with respect to this one hypothesis can in such cases only be used to derive partial explanations, whereas for a more complete understanding of the underlying processes, interactions with other factors need to be considered. Furthermore, displaying interacting aspects of a system as discrete entities within a hierarchy can obfuscate the true dynamics of a system.

In our three examples—the enemy release hypothesis, the hare–lynx cycles, and the escalation hypothesis (figures  3 and  4 )—connections between the different levels of the hierarchies do not necessarily depict causal relationships. Also, the fact that multicausality is ubiquitous in ecological systems is not covered. It has been argued that approaches directly focusing on explicating causal relationships and multicausality could be more helpful for advancing theory (Scheiner and Fox 2018 ). The HoH approach is currently primarily a tool to provide conceptual structure. We suggest that revealing causal networks and multicausalities represent additional objectives and regard them as important aims also for further developing the HoH approach. Combining existing approaches for revealing causal relationships (e.g., Eco Evidence, Norris et al. 2012 , or CADDIS, www.epa.gov/caddis ) with the HoH approach seems to be a promising path forward. Also, a future aim could be to develop a version of the HoH approach with enhanced formalization, allowing different kinds of relationships among subhypotheses to be disclosed (e.g., applying semantic web methods. Such a formalized version of the HoH approach could be used for scrutinizing the logical structure of hypotheses (e.g., compatibility and incompatibility of subhypotheses) and identifying inevitable interdependencies (e.g., likelihood of cooccurrence of evidence along two branches).

The guidelines on how to build an HoH presented above and in figures  1 and  2 will help to increase the reproducibility of the process. Full reproducibility is unlikely to be reached for most applications because researchers need to make individual choices. For example, step 1 involves creative reasoning and may therefore potentially lead to differing results if repeated by different researchers. The process of creating an HoH can therefore lead to a whole set of outcomes. Usually, there will be not one single HoH that is the one “correct” answer to the research questions. Certain steps of the process can be automated using artificial intelligence, such as with the use of decision-tree algorithms to enhance reproducibility (Ryo et al. 2019 ). But even if such techniques are applied, the choice of which information is fed into the algorithms is made by a researcher. We suggest that this ambiguity should not be considered a flaw of the method, but instead an important and necessary concession to creativity, offering the chance to closely match the outcome of the process to the concrete requirements of the research project. Also, it should be noted that other approaches for knowledge synthesis do not necessarily yield reproducible results either, not even formal meta-analysis (de Vrieze 2018 ).

Conclusions

The current emphasis on statistical approaches for synthesizing evidence with the purpose of facilitating decision making in environmental management and nature conservation is undoubtedly important and necessary. However, knowledge and understanding of ecological systems would profit largely if results from empirical studies would in addition, and on a regular basis, be used to improve theory. With this contribution, we present one possibility for creating close links between evidence and theory, and we hope to stimulate future studies that feed results from case studies back into theory. Our goal is to motivate more conceptual work aimed at refining major hypotheses on how complex systems work. Above, we provided examples for how to develop a nuanced representation of major hypotheses, focusing on their mechanistic components.

Ecological systems are highly complex, and therefore, the theories describing them typically need to incorporate complexity. Nested, hierarchical structures in our view represent one possible path forward, because they allow zooming in and out and, therefore, moving between different levels of complexity. We propose that alternative tools such as causal networks should be further developed for application in ecology and evolution as well. Combining complementary conceptual tools would in our view be most promising for an efficient enhancement of knowledge and understanding in ecology.

Supplementary Material

Biaa130_supplemental_file, acknowledgments.

The ideas presented in this article were developed during the workshop “The hierarchy-of-hypotheses approach: Exploring its potential for structuring and analyzing theory, research, and evidence across disciplines,” 19–21 July 2017, and refined during the workshop “Research synthesis based on the hierarchy-of-hypotheses approach,” 10–12 October 2018, both in Hanover, Germany. We thank William Bausman, Adam Clark, Francesco DePrentis, Carsten Dormann, Alexandra Erfmeier, Gordon Fox, Jeremy Fox, James Griesemer, Volker Grimm, Thierry Hanser, Frank Havemann, Yuval Itescu, Marie Kaiser, Julia Koricheva, Peter Kraker, Ingolf Kühn, Andrew Latimer, Chunlong Liu, Bertram Ludäscher, Klaus Mainzer, Elijah Millgram, Bob O'Hara, Masahiro Ryo, Raphael Scholl, Gerhard Schurz, Philip Stephens, Koen van Benthem and Meike Wittman for participating in our lively discussions and Alkistis Elliot-Graves and Birgitta König-Ries for help with refining terminology. Furthermore, we thank Sam Scheiner and five anonymous reviewers for comments that helped to improve the manuscript. The workshops were funded by Volkswagen Foundation (Az 92,807 and 94,246). TH, CAA, ME, PG, ADS, and JMJ received funding from German Federal Ministry of Education and Research within the Collaborative Project “Bridging in Biodiversity Science” (grant no. 01LC1501A). ME additionally received funding from the Foundation of German Business, JMJ from the Deutsche Forschungsgemeinschaft (grants no. JE 288/9–1 and JE 288/9–2), and IB from German Federal Ministry of Education and Research (grant no. FKZ 01GP1710). CJL was supported by a grant from The Natural Sciences and Engineering Research Council of Canada and in-kind synthesis support from the US National Center for Ecological Analysis and Synthesis. LGA was supported by the Spanish Ministry of Science, Innovation, and Universities through project no. CGL2014–56,739-R, and RRB received funding from the Brazilian National Council for Scientific and Technological Development (process no. 152,289/2018–6).

Author Biographical

Tina Heger ( [email protected] ) is affiliated with the Department of Biodiversity Research and Systematic Botany and Alexis D. Synodinos is affiliated with the Department of Plant Ecology and Nature Conservation at the University of Potsdam, in Potsdam, Germany. Tina Heger and Kurt Jax are affiliated with the Department of Restoration Ecology at the Technical University of Munich, in Freising, Germany. Tina Heger, Carlos A. Aguilar-Trigueros, Martin Enders, Pierre Gras, Jonathan M. Jeschke, Sophie Lokatis, and Alexis Synodinos are affiliated with the ­Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), in Berlin, Germany. Carlos Aguilar, Isabelle Bartram, Martin Enders, Jonathan M. Jeschke, and Sophie Lokatis are affiliated with the Institute of Biology at Freie Universität Berlin, in Berlin, Germany. Martin Enders, Jonathan M. Jeschke, and Sophie Lokatis are also affiliated with the Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), in Berlin, Germany. Pierre Gras is also affiliated with the Department of Ecological Dynamics at the Leibniz Institute for Zoo and Wildlife Research (IZW), also in Berlin, Germany. Isabelle Bartram is affiliated with the Institute of Sociology, at the University of Freiburg, in Freiburg. Kurt Jax is also affiliated with the Department of Conservation Biology at the Helmholtz Centre for Environmental Research—UFZ, in Leipzig, Germany. Raul R. Braga is located at the Universidade Federal do Paraná, Laboratório de Ecologia e Conservação, in Curitiba, Brazil. Gregory P. Dietl has two affiliations: the Paleontological Research Institution and the Department of Earth and Atmospheric Sciences at Cornell University, in Ithaca, New York. David J. Gibson is affiliated with the School of Biological Sciences at Southern Illinois University Carbondale, in Carbondale, Illinois. Lorena Gómez-Aparicio's affiliation is the Instituto de Recursos Naturales y Agrobiología de Sevilla, CSIC, LINCGlobal, in Sevilla, Spain. Christopher J. Lortie is affiliated with the Department of Biology at York University, in York, Canada, as well as with the National Center for Ecological Analysis and Synthesis, at the University of California Santa Barbara, in Santa Barbara, California. Anne-Christine Mupepele has two affiliations as well: the Chair of Nature Conservation and Landscape Ecology at the University of Freiburg, in Freiburg, and the Senckenberg Biodiversity and Climate Research Centre, in Frankfurt am Main, both in Germany. Stefan Schindler is working at the Environment Agency Austria and the University of Vienna's Division of Conservation Biology, Vegetation, and Landscape Ecology, in Vienna, Austria, and his third affiliation is with Community Ecology and Conservation, at the Czech University of Life Sciences Prague, in Prague, Czech Republic. Finally, Jostein Starrfelt is affiliated with the University of Oslo's Centre for Ecological and Evolutionary Synthesis and with the Norwegian Scientific Committee for Food and Environment, Norwegian Institute of Public Health, both in Oslo, Norway. Alexis D. Synodinos is affiliated with the Centre for Biodiversity Theory and Modelling, Theoretical, and Experimental Ecology Station, CNRS, in Moulis, France.

Contributor Information

Tina Heger, Department of Biodiversity Research and Systematic Botany, University of Potsdam, Potsdam, Germany. Department of Restoration Ecology, Technical University of Munich, Freising, Germany. Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany.

Carlos A Aguilar-Trigueros, Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany. Institute of Biology, Freie Universität, Berlin, Berlin, Germany.

Isabelle Bartram, Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany. Institute of Biology, Freie Universität, Berlin, Berlin, Germany. Institute of Sociology, University of Freiburg, Freiburg.

Raul Rennó Braga, Universidade Federal do Paraná, Laboratório de Ecologia e Conservação, Curitiba, Brazil.

Gregory P Dietl, Paleontological Research Institution and the Department of Earth and Atmospheric Sciences at Cornell University, Ithaca, New York.

Martin Enders, Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany. Institute of Biology, Freie Universität, Berlin, Berlin, Germany. Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany.

David J Gibson, School of Biological Sciences, Southern Illinois University Carbondale, Carbondale, Illinois.

Lorena Gómez-Aparicio, Instituto de Recursos Naturales y Agrobiología de Sevilla, CSIC, LINCGlobal, Sevilla, Spain.

Pierre Gras, Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany. Department of Ecological Dynamics, Leibniz Institute for Zoo and Wildlife Research (IZW), also in Berlin, Germany.

Kurt Jax, Department of Restoration Ecology, Technical University of Munich, Freising, Germany. Department of Conservation Biology, Helmholtz Centre for Environmental Research—UFZ, Leipzig, Germany.

Sophie Lokatis, Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany. Institute of Biology, Freie Universität, Berlin, Berlin, Germany. Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany.

Christopher J Lortie, Department of Biology, York University, York, Canada, as well as with the National Center for Ecological Analysis and Synthesis, University of California Santa Barbara, Santa Barbara, California.

Anne-Christine Mupepele, Chair of Nature Conservation and Landscape Ecology, University of Freiburg, Freiburg, and the Senckenberg Biodiversity and Climate Research Centre, Frankfurt am Main, both in Germany.

Stefan Schindler, Environment Agency Austria and University of Vienna's Division of Conservation, Biology, Vegetation, and Landscape Ecology, Vienna, Austria, and his third affiliation is with Community Ecology and Conservation, Czech University of Life Sciences Prague, Prague, Czech Republic, Finally.

Jostein Starrfelt, University of Oslo's Centre for Ecological and Evolutionary Synthesis and with the Norwegian Scientific Committee for Food and Environment, Norwegian Institute of Public Health, both in Oslo, Norway.

Alexis D Synodinos, Department of Plant Ecology and Nature Conservation, University of Potsdam, Potsdam, Germany. Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany. Centre for Biodiversity Theory and Modelling, Theoretical, and Experimental Ecology Station, CNRS, Moulis, France.

Jonathan M Jeschke, Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany. Institute of Biology, Freie Universität, Berlin, Berlin, Germany. Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany.

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Practices of Science: Opinion, Hypothesis & Theory

An opinion is a statement describing a personal belief or thought that cannot be tested (or has not been tested) and is unsupported by evidence. A hypothesis is usually a prediction based on some observation or evidence. Hypotheses must be testable, and once tested, they can be supported by evidence. If a statement is made that cannot be tested and disproved, then it is not a hypothesis. Sometimes it is possible to restate an opinion so that it can become a hypothesis.

A scientific theory is a hypothesis that has been extensively tested, evaluated by the scientific community, and is strongly supported. Theories often describe a large set of observations, and provide a cohesive explanation for those observations. An individual cannot come up with a theory. Theories require extensive testing and agreement within the scientific community. Theories are not described as true or right, but as the best-supported explanation of the world based on evidence.

SF Fig. 7.9. Alfred Wegener first proposed the idea of continental drift.

Image courtesy of Deutsches Dokumentationszentrum für Kunstgeschichte - Bildarchiv Foto Marburg, Wikimedia Commons

German-born geophysicist Alfred Wegener is credited with proposing a hypothesis of continental drift in the late 1800’s, but it was not until the 1960’s that his concept became widely accepted by the scientific community. Part of the problem Wegener faced in presenting his hypothesis of continental drift was that he did not have a sufficient evidence to be able to propose the mechanism of continental movement. Wegener suggested that the continents moved across the ocean floor, but the lack of disturbance on the ocean floor did not support this part of his hypothesis. The elevation of continental drift to the status of a theory came largely from evidence supporting new ideas about the mechanism of plate movement: plate tectonics. It was only over time, as more scientists evaluated and added to Wegener’s original hypothesis, that it became widely accepted as a theory.

• Arc-shaped island chains like the Aleutian Islands are found at subduction zones.
• Dinosaurs were mean animals.
• Mammals are superior to reptiles.
• An asteroid impact contributed to the extinction of dinosaurs.
• Science can answer any question.
• The climate on Antarctica was once warmer than it is now.
• The center of the earth is made of platinum.  
• You have a hypothesis that the land near your school was once at the bottom of the ocean, but due to continental movement, it is now miles inland from any water source. How would you test your hypothesis? What evidence would you use to support your claim?

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Exploring Our Fluid Earth, a product of the Curriculum Research & Development Group (CRDG), College of Education. © University of Hawai‘i, . This document may be freely reproduced and distributed for non-profit educational purposes.

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March 4, 2020

New insights into evolution: Why genes appear to move around

by Uppsala University

Scientists at Uppsala University have proposed an addition to the theory of evolution that can explain how and why genes move on chromosomes. The hypothesis, called the SNAP Hypothesis, is presented in the scientific journal PLOS Genetics .

Life originated on Earth almost 4 billion years ago and diversified into a vast array of species. How did this diversification occur? The Theory of Evolution, together with the discovery of DNA and how it replicates, provide an answer and a mechanism. Mutations in DNA occur from generation to generation, and can be selected if they help individuals to adapt better to their environment. Over time, this has led to the separation of organisms into the different species that now inhabit all ecosystems.

Current theory holds that evolution involves mistakes made when replicating a gene. This explains how genes can mutate over time and acquire new functions. However, a mystery in biology is that the relative locations of genes on chromosomes also change over time. This is obvious in bacteria, as different species often have the same genes in very different relative locations. Since the origin of life , genes have apparently been changing location. The questions are, how and why do genes move their relative locations?

Now, scientists at Uppsala University have proposed an addition to the theory of evolution that can explain how and why genes move on chromosomes. The hypothesis, called the SNAP hypothesis , is based on the observation that tandem duplications of sections of chromosome occur very frequently in bacteria (more than 1 million times more frequently than most mutations). These duplications are lost spontaneously unless they are selected. Selection to maintain a duplication can occur whenever bacteria find themselves in a sub-optimal environment, where having two copies of a particular gene could increase fitness (for example, if the duplicated region includes a gene that increases growth rate on a poor diet).

Duplications typically contain hundreds of genes, even if only one is selected. The scientists Gerrit Brandis and Diarmaid Hughes argue that mutations can quickly accumulate in the hundreds of non-selected genes, including genes that are normally essential when there is only a single copy in the chromosome. Once two different essential genes are inactivated, one in each copy of the duplication, the duplication can no longer be lost. From this point on, the bacteria will have many genes unnecessarily duplicated, and mutations to inactivate or delete them will be positively selected because they increase fitness.

Over time, all of the unnecessary duplicated genes may be lost by mutation, but this will happen randomly in each copy of the duplication. By this process of random loss of unnecessary duplicated genes in each copy of the duplication, the relative order of the remaining genes can be completely changed. The SNAP process can rearrange gene order very rapidly and it may contribute to separating different species.

Journal information: PLoS Genetics

Provided by Uppsala University

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• How Science Unifies the World

In this Issue:

• Summer 2016 View All Other Issues
• A Look at the Facts, Issues, and Opinions‎ Shaping Our World
• Welcome to Trend
• Crunch: Migration in Europe: A Record Breaking Shift
• How Geography Shapes Our Identity
• Global Migration's Rapid Rise
• A Growing Middle Class
• Africa Is Changing Faith
• Quiz: Do You Know How Africa Could Change the Future of Religion?
• Antimicrobial Resistance
• High Seas at Risk: Why the World Must Act
• Five Questions: The Future of Ocean Conservation
• View All Other Issues

Nobel Laureate Craig Mello Reflects on the Unifying Force of Science

We all have questions. Where did we come from? Why are we here? Although the “whys” are generally left to philosophers, the “wheres” and “hows” are fodder for scientific exploration.

Science is a unifying enterprise, one that brings us together to solve problems, fuel our sense of wonder, and understand our place in the world. Through scientific inquiry, we peer deeply into the infinitesimal workings of individual cells and outward into the unimaginable expanses of the cosmos. And I am optimistic that science can point us toward a path to a brighter and sustainable future, one in which we can together realize our common destiny as inhabitants of this small and fragile planet.

There are so many forces that divide us: barriers of language, customs, ideology, and belief. But science transcends these forces. Science is a path to knowledge that begins with a simple and humbling admission: “I don’t know, but I wonder how, where, or why.” Science values questions, not beliefs. Science looks beyond dogma and demands evidence. The resulting knowledge crosses all borders and makes its way around the world. Knowledge belongs to us all. As a scientist, every day you’re confronted with your ignorance. Every day, you’re exploring a new frontier, seeking new vistas, and often realizing that your ideas were wrong, that there are so many things you don’t yet understand.

Science reveals things so profound, they change the way we see the world and ourselves. The sequence of letters in our DNA has shown how closely we are related to each other and to every living thing on the planet today—the house plants in your window and the goldfish in your bowl are your relatives. It seems incredible, but even bacteria are your relatives. Indeed you are actually part bacteria, as you arose from an ancestral cell that combined with a bacterial cell and incorporated their genes into your own DNA. A literal fusion of two organisms into one.

This may seem a humbling tale, but the rewards for this humility are tremendous. Some are very practical. Some hit close to home. My daughter has Type 1 diabetes and is alive today thanks to countless scientific breakthroughs. The insulin that she needs to live was discovered by biochemists nearly 100 years ago. Originally insulin was extracted from animal tissue, but today it is made by genetically engineered bacteria. Amazingly, these humble  E. coli , when supplied with the blueprint for human insulin (the insulin gene), can read the human genetic code and produce functional human insulin protein that keeps my daughter and millions of other diabetics alive. That’s right, not all genetically modified organisms are bad; this human insulin-producing  E. coli  is a GMO that saves millions of lives.

Science shows us how much we still have to learn. Which is what makes the whole process so beautiful. Because the more deeply we look, the more mysterious and breathtaking our world is shown to be. Science deepens the mystery of our existence and opens our eyes to the wonder and beauty of nature. Take, for example, the Hubble Space Telescope. When astronomers pointed it at an apparently empty segment of space, they revealed not utter blackness but galaxies upon galaxies—star formations whose light has been traveling for 13.7 billion years, since the beginning of the universe.

But more than holding up a mirror to the beauty of the universe, science holds the power to transform us. There are serious problems with our world today. Too many people living in the world’s cities, producing too much pollution. Humans are disturbing the balance of the whole Earth biosphere. Like bacteria growing in a culture dish, eventually we are going to run out of nutrients and the things we need to sustain us. We need to make some changes before society reaches this breaking point.

I am confident that we can come up with ways to promote sustainable living—and that science can help lead the way. We need to invest in technologies that will allow us to produce food locally and in abundance, houses that produce sufficient energy to power their occupants’ needs—with enough left over to charge the family’s electric car.

As a society, we should focus on unlocking the potential of our people, both young and old, and work on educating everyone who has a desire to learn. And we should make sure we continue to invest in the kinds of exploration that inspire people—perhaps even attempt to colonize space. Our greatest achievements often come from doing things that do not seem immediately practical.

You don’t have to be a rocket scientist to know that this is the right way to go: to raise enthusiasm about the future of humankind and about delving together into the great unknowns. It’s an exciting time to be alive, and I would love for all people to be able to realize what a grand adventure life is and, when you stop to look closely at the world we share, how incredible it really is—more so than anything we could ever have imagined.

Craig C. Mello is a professor of molecular medicine at the University of Massachusetts Medical School, and chairman of the national advisory committee of the Pew Scholars Program in the Biomedical Sciences. He received the 2006 Nobel Prize for physiology or medicine.

Torsten Wiesel: How Science Can Bridge Divides

Every day, it seems, the news is filled with stories about wars and insurrections, suicide bombers and mass shootings, global climate change, and the collapse of the world economy. Confronted with such chaos and destruction, dangerous ideologies, and suppression of civil liberties, how can we even think about transcending borders, promoting peace, and fostering positive alliances across races, cultures, and religions?

Of course, we must. Fortunately, by nature, I am an optimist. And by training, I am a medical doctor and a neuroscientist. I have seen firsthand how, in the world of science, cooperation between disciplines occurs as a natural part of the work we do. That is because the language of science, and the ultimate purpose of science—to learn all that we can about the world at large and about ourselves—crosses races, cultures, and religions. For this reason, the scientific establishment can serve as an instrument of peace.

Individual scientists have a history of campaigning for the cause of peace. Albert Einstein spoke often of “using man’s powers of reason in order to settle disputes between nations … and have peace in the world from now on.”

Of course, science on its own is not guaranteed to provide a cure for societal maladies. Indeed, scientists were instrumental in the development of our most destructive weapons—chemical weapons and nuclear bombs. Even Swedish chemist Alfred Nobel made his fortune from the invention of dynamite.

But many of these same scientists became prominent, outspoken advocates for peace. Physicists Robert Oppenheimer in the United States and Andrei Sakharov in the then-Soviet Union, who participated in the formulation of atomic bombs, led the charge in opposing their use. And Nobel bequeathed the bulk of his estate to establish the prizes that bear his name—awards that recognize advances in science as well as services to promote international fraternity.

Primarily, scientists can do their part by simply doing their science—a practice that involves interacting with colleagues from around the globe. As physicist Freeman Dyson elegantly states in his book  Imagined Worlds , “The international community of scientists may help to abolish war by setting an example to the world of practical cooperation across barriers of nationality, language, and culture.”

I have been involved in several programs that strive to fulfill Dyson’s dream of working across cultural and national barriers. For nearly a decade, I served as secretary general of the Human Frontier Science Program, an international organization that gives scientists from different countries and disciplines the opportunity to not only work together but also get to know each other and broaden their understanding and appreciation of life beyond their borders. In addition, the program supports the training of postdoctoral students outside their home country, again an effective means of facilitating a deeper understanding between nations.

Similarly, the New York Academy of Sciences, a 200-year-old organization with members in 140 different countries for which I served as chair of the board of governors for six years, strives to create a global community of scientists and to benefit humanity by advancing knowledge about science and related issues.

For a decade I had the honor of chairing the Committee on Human Rights, created by the National Academies of Sciences, Engineering, and Medicine to protect and assist scientists, doctors, and scholars defined as prisoners of conscience.

These venerable organizations contribute to building bridges between scientists, who work together across international boundaries and scientific disciplines, practicing the kind of mutual respect essential to peaceful relations. But bridges cannot be built without a solid foundation. Science by itself cannot succeed in the absence of public understanding—which brings us to the need for educational opportunities for all of the world’s citizens.

Having access to information—and a chance to learn—is a fundamental human right. Now, thanks to the global reach of the Internet, we are making great progress toward having worldwide, affordable education become a reality. More and more universities are offering virtual classes and other online programs. I recently joined the council of the University of the People, an online university that has enrolled students in more than 180 countries. Through this institution, students can obtain an accredited associate degree for about $2,000 and a Bachelor of Arts for about $4,000.

Such online opportunities represent a revolution in education worth recognizing. With the spread of knowledge and understanding to all corners of the globe, we can hope that science—and scientists—will be better able to transcend borders, unite humankind, and, as Einstein said, “make peace in the world from now on.”

The very possibility is truly a cause for optimism.

Torsten Wiesel is a neurobiologist, former president of Rockefeller University, and chairman of the national advisory committee of the Pew Latin American Fellows Program in the Biomedical Sciences. He received the 1981 Nobel Prize in physiology or medicine.

Biologist Paula Licona-Limón: My Scientific Journey Abroad

It was always clear to me that I would work in a field related to biology. Both of my parents are physicians, and when I was young, my father and I once constructed a microscope. It was very primitive, but it worked.

My mother and father encouraged my siblings and me to try new things, to travel and learn about other places. They considered it part of our education, which is not a common view in the small town in Mexico where I grew up and where many young people never attend university.

That’s unfortunate—especially in Mexico, where a public university education is all but free. I received my Ph.D. in immunology from the National Autonomous University of Mexico at no cost. This is something I love about my country. Anyone who is willing to put in the energy and the effort has open access to an education.

I started working in a lab at the age of 17, and from the very beginning, my plan was to become a scientist. At the same time, I knew I would have to leave my country to continue my postdoctoral studies. In science, as in other professions, jobs are given to those best qualified. And I knew that if I wanted to be a principal investigator and run my own lab, I needed to finish my training abroad.

While growing up, I lived in Chiapas, the southernmost state in Mexico, so I’m not sure what I was thinking when I decided to move to New Haven in the middle of February. It was insanely cold and snowing. But I joined the lab of Dr. Richard Flavell at Yale University, brimming with energy and excitement, and immediately started working like crazy. I learned many new techniques, new approaches, and new ways of thinking about problems. The science made me forget about the weather and feel at home.

And so did the friends I made in the lab. In many ways, science has few borders. Anyone can learn about scientific advances by reading journals, no matter where they are. But spending every day with people from other countries further erases any divisions of nationality or culture, because we are all working toward a common purpose: searching for knowledge, for understanding.

In Richard’s lab, only two of the 25 or so postdocs were American. The others came from all over the world—China, Iran, Israel, Spain, Italy, Ireland, Austria, Germany, Belgium, Argentina, Poland, Colombia, Japan, Korea, and India. We all worked side by side in two big rooms.

Being with each other, all day long, we would talk not just about our science but about our families, our cultures, our countries, and our food as well. We discussed our religions and beliefs, and we learned how different people say “hi.”

The experience taught us tolerance and respect for different traditions—although when I learned that some cultures do not eat meat, I wanted to say, “How could your parents do that to you?”

Some of these colleagues remained in the United States. For me, that was never the plan. Just as I knew I needed to go somewhere else to expand my horizons and extend my training, I also knew I wanted to return home. When I first interviewed in Richard’s lab, I told him, “I’m only here for two years.” Two years became seven, but I was always sure that I wanted to come back to Mexico. Part of the reason was my family. For Mexicans, family is very important, and my family and I are especially close.

But I also wanted to return home to try to give back to my university and my country. They invested in me, and I owed it to them to bring back the new tools and technologies we could use to move our science forward. Now, in my studies of how parasites interact with the human immune system, I can use gene-editing technology like CRISPR to develop new transgenic models. Unfortunately, parasitic infections are still common in Mexico, so I have access to samples from patients. The good news is that this may allow me to move my research forward more quickly. This situation not only offers me a practical way to take advantage of my geographical location but could also allow me to implement what I’ve learned to directly benefit people in the region and in other parts of the developing world.

Now my sister, Ileana, is following in my footsteps. This year, she was awarded a Pew Latin American fellowship, and she will also travel to Yale, to work with Dr. Ruslan Medzhitov. Both of us are immunologists, and we decided that to get the best training possible, we had to move beyond the old ways of doing science and challenge ourselves to take advantage of the opportunities that are available away from home.

I know that, like me, she will choose to return to Mexico—to bring back new ideas, new approaches, and fresh perspectives—and that she will be able to share what she’s learned with everyone here so, together, we can continue to train future generations.

Paula Licona-Limón is an immunologist at the National Autonomous University of Mexico's Institute of Cellular Physiology. She was a Pew Latin American fellow from 2008 to 2010.

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