hypothesis science vs discovery

1.2 The Process of Science

Learning objectives.

• Identify the shared characteristics of the natural sciences
• Understand the process of scientific inquiry
• Compare inductive reasoning with deductive reasoning
• Describe the goals of basic science and applied science

Like geology, physics, and chemistry, biology is a science that gathers knowledge about the natural world. Specifically, biology is the study of life. The discoveries of biology are made by a community of researchers who work individually and together using agreed-on methods. In this sense, biology, like all sciences is a social enterprise like politics or the arts. The methods of science include careful observation, record keeping, logical and mathematical reasoning, experimentation, and submitting conclusions to the scrutiny of others. Science also requires considerable imagination and creativity; a well-designed experiment is commonly described as elegant, or beautiful. Like politics, science has considerable practical implications and some science is dedicated to practical applications, such as the prevention of disease (see Figure 1.15 ). Other science proceeds largely motivated by curiosity. Whatever its goal, there is no doubt that science, including biology, has transformed human existence and will continue to do so.

The Nature of Science

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

Science is a very specific way of learning, or knowing, about the world. The history of the past 500 years demonstrates that science is a very powerful way of knowing about the world; it is largely responsible for the technological revolutions that have taken place during this time. There are however, areas of knowledge and human experience that the methods of science cannot be applied to. These include such things as answering purely moral questions, aesthetic questions, or what can be generally categorized as spiritual questions. Science cannot investigate these areas because they are outside the realm of material phenomena, the phenomena of matter and energy, and cannot be observed and measured.

The scientific method is a method of research with defined steps that include experiments and careful observation. The steps of the scientific method will be examined in detail later, but one of the most important aspects of this method is the testing of hypotheses. A hypothesis is a suggested explanation for an event, which can be tested. Hypotheses, or tentative explanations, are generally produced within the context of a scientific theory . A generally accepted scientific theory is thoroughly tested and confirmed explanation for a set of observations or phenomena. Scientific theory is the foundation of scientific knowledge. In addition, in many scientific disciplines (less so in biology) there are scientific laws , often expressed in mathematical formulas, which describe how elements of nature will behave under certain specific conditions. There is not an evolution of hypotheses through theories to laws as if they represented some increase in certainty about the world. Hypotheses are the day-to-day material that scientists work with and they are developed within the context of theories. Laws are concise descriptions of parts of the world that are amenable to formulaic or mathematical description.

Natural Sciences

What would you expect to see in a museum of natural sciences? Frogs? Plants? Dinosaur skeletons? Exhibits about how the brain functions? A planetarium? Gems and minerals? Or maybe all of the above? Science includes such diverse fields as astronomy, biology, computer sciences, geology, logic, physics, chemistry, and mathematics ( Figure 1.16 ). However, those fields of science related to the physical world and its phenomena and processes are considered natural sciences . Thus, a museum of natural sciences might contain any of the items listed above.

There is no complete agreement when it comes to defining what the natural sciences include. For some experts, the natural sciences are astronomy, biology, chemistry, earth science, and physics. Other scholars choose to divide natural sciences into life sciences , which study living things and include biology, and physical sciences , which study nonliving matter and include astronomy, physics, and chemistry. Some disciplines such as biophysics and biochemistry build on two sciences and are interdisciplinary.

Scientific Inquiry

One thing is common to all forms of science: an ultimate goal “to know.” Curiosity and inquiry are the driving forces for the development of science. Scientists seek to understand the world and the way it operates. Two methods of logical thinking are used: inductive reasoning and deductive reasoning.

Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. This type of reasoning is common in descriptive science. A life scientist such as a biologist makes observations and records them. These data can be qualitative (descriptive) or quantitative (consisting of numbers), and the raw data can be supplemented with drawings, pictures, photos, or videos. From many observations, the scientist can infer conclusions (inductions) based on evidence. Inductive reasoning involves formulating generalizations inferred from careful observation and the analysis of a large amount of data. Brain studies often work this way. Many brains are observed while people are doing a task. The part of the brain that lights up, indicating activity, is then demonstrated to be the part controlling the response to that task.

Deductive reasoning or deduction is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning. Deductive reasoning is a form of logical thinking that uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid. For example, a prediction would be that if the climate is becoming warmer in a region, the distribution of plants and animals should change. Comparisons have been made between distributions in the past and the present, and the many changes that have been found are consistent with a warming climate. Finding the change in distribution is evidence that the climate change conclusion is a valid one.

Both types of logical thinking are related to the two main pathways of scientific study: descriptive science and hypothesis-based science. Descriptive (or discovery) science aims to observe, explore, and discover, while hypothesis-based science begins with a specific question or problem and a potential answer or solution that can be tested. The boundary between these two forms of study is often blurred, because most scientific endeavors combine both approaches. Observations lead to questions, questions lead to forming a hypothesis as a possible answer to those questions, and then the hypothesis is tested. Thus, descriptive science and hypothesis-based science are in continuous dialogue.

Hypothesis Testing

Biologists study the living world by posing questions about it and seeking science-based responses. This approach is common to other sciences as well and is often referred to as the scientific method. The scientific method was used even in ancient times, but it was first documented by England’s Sir Francis Bacon (1561–1626) ( Figure 1.17 ), who set up inductive methods for scientific inquiry. The scientific method is not exclusively used by biologists but can be applied to almost anything as a logical problem-solving method.

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

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

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

A hypothesis must be testable to ensure that it is valid. For example, a hypothesis that depends on what a bear thinks is not testable, because it can never be known what a bear thinks. It should also be falsifiable , meaning that it can be disproven by experimental results. An example of an unfalsifiable hypothesis is “Botticelli’s Birth of Venus is beautiful.” There is no experiment that might show this statement to be false. To test a hypothesis, a researcher will conduct one or more experiments designed to eliminate one or more of the hypotheses. This is important. A hypothesis can be disproven, or eliminated, but it can never be proven. Science does not deal in proofs like mathematics. If an experiment fails to disprove a hypothesis, then we find support for that explanation, but this is not to say that down the road a better explanation will not be found, or a more carefully designed experiment will be found to falsify the hypothesis.

Each experiment will have one or more variables and one or more controls. A variable is any part of the experiment that can vary or change during the experiment. A control is a part of the experiment that does not change. Look for the variables and controls in the example that follows. As a simple example, an experiment might be conducted to test the hypothesis that phosphate limits the growth of algae in freshwater ponds. A series of artificial ponds are filled with water and half of them are treated by adding phosphate each week, while the other half are treated by adding a salt that is known not to be used by algae. The variable here is the phosphate (or lack of phosphate), the experimental or treatment cases are the ponds with added phosphate and the control ponds are those with something inert added, such as the salt. Just adding something is also a control against the possibility that adding extra matter to the pond has an effect. If the treated ponds show lesser growth of algae, then we have found support for our hypothesis. If they do not, then we reject our hypothesis. Be aware that rejecting one hypothesis does not determine whether or not the other hypotheses can be accepted; it simply eliminates one hypothesis that is not valid ( Figure 1.18 ). Using the scientific method, the hypotheses that are inconsistent with experimental data are rejected.

In recent years a new approach of testing hypotheses has developed as a result of an exponential growth of data deposited in various databases. Using computer algorithms and statistical analyses of data in databases, a new field of so-called "data research" (also referred to as "in silico" research) provides new methods of data analyses and their interpretation. This will increase the demand for specialists in both biology and computer science, a promising career opportunity.

Visual Connection

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

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

In practice, the scientific method is not as rigid and structured as it might at first appear. Sometimes an experiment leads to conclusions that favor a change in approach; often, an experiment brings entirely new scientific questions to the puzzle. Many times, science does not operate in a linear fashion; instead, scientists continually draw inferences and make generalizations, finding patterns as their research proceeds. Scientific reasoning is more complex than the scientific method alone suggests.

Basic and Applied Science

The scientific community has been debating for the last few decades about the value of different types of science. Is it valuable to pursue science for the sake of simply gaining knowledge, or does scientific knowledge only have worth if we can apply it to solving a specific problem or bettering our lives? This question focuses on the differences between two types of science: basic science and applied science.

Basic science or “pure” science seeks to expand knowledge regardless of the short-term application of that knowledge. It is not focused on developing a product or a service of immediate public or commercial value. The immediate goal of basic science is knowledge for knowledge’s sake, though this does not mean that in the end it may not result in an application.

In contrast, applied science or “technology,” aims to use science to solve real-world problems, making it possible, for example, to improve a crop yield, find a cure for a particular disease, or save animals threatened by a natural disaster. In applied science, the problem is usually defined for the researcher.

Some individuals may perceive applied science as “useful” and basic science as “useless.” A question these people might pose to a scientist advocating knowledge acquisition would be, “What for?” A careful look at the history of science, however, reveals that basic knowledge has resulted in many remarkable applications of great value. Many scientists think that a basic understanding of science is necessary before an application is developed; therefore, applied science relies on the results generated through basic science. Other scientists think that it is time to move on from basic science and instead to find solutions to actual problems. Both approaches are valid. It is true that there are problems that demand immediate attention; however, few solutions would be found without the help of the knowledge generated through basic science.

One example of how basic and applied science can work together to solve practical problems occurred after the discovery of DNA structure led to an understanding of the molecular mechanisms governing DNA replication. Strands of DNA, unique in every human, are found in our cells, where they provide the instructions necessary for life. During DNA replication, new copies of DNA are made, shortly before a cell divides to form new cells. Understanding the mechanisms of DNA replication enabled scientists to develop laboratory techniques that are now used to identify genetic diseases, pinpoint individuals who were at a crime scene, and determine paternity. Without basic science, it is unlikely that applied science could exist.

Another example of the link between basic and applied research is the Human Genome Project, a study in which each human chromosome was analyzed and mapped to determine the precise sequence of DNA subunits and the exact location of each gene. (The gene is the basic unit of heredity represented by a specific DNA segment that codes for a functional molecule.) Other organisms have also been studied as part of this project to gain a better understanding of human chromosomes. The Human Genome Project ( Figure 1.19 ) relied on basic research carried out with non-human organisms and, later, with the human genome. An important end goal eventually became using the data for applied research seeking cures for genetically related diseases.

While research efforts in both basic science and applied science are usually carefully planned, it is important to note that some discoveries are made by serendipity, that is, by means of a fortunate accident or a lucky surprise. Penicillin was discovered when biologist Alexander Fleming accidentally left a petri dish of Staphylococcus bacteria open. An unwanted mold grew, killing the bacteria. The mold turned out to be Penicillium , and a new critically important antibiotic was discovered. In a similar manner, Percy Lavon Julian was an established medicinal chemist working on a way to mass produce compounds with which to manufacture important drugs. He was focused on using soybean oil in the production of progesterone (a hormone important in the menstrual cycle and pregnancy), but it wasn't until water accidentally leaked into a large soybean oil storage tank that he found his method. Immediately recognizing the resulting substance as stigmasterol, a primary ingredient in progesterone and similar drugs, he began the process of replicating and industrializing the process in a manner that has helped millions of people. Even in the highly organized world of science, luck—when combined with an observant, curious mind focused on the types of reasoning discussed above—can lead to unexpected breakthroughs.

Reporting Scientific Work

Whether scientific research is basic science or applied science, scientists must share their findings for other researchers to expand and build upon their discoveries. Communication and collaboration within and between sub disciplines of science are key to the advancement of knowledge in science. For this reason, an important aspect of a scientist’s work is disseminating results and communicating with peers. Scientists can share results by presenting them at a scientific meeting or conference, but this approach can reach only the limited few who are present. Instead, most scientists present their results in peer-reviewed articles that are published in scientific journals. Peer-reviewed articles are scientific papers that are reviewed, usually anonymously by a scientist’s colleagues, or peers. These colleagues are qualified individuals, often experts in the same research area, who judge whether or not the scientist’s work is suitable for publication. The process of peer review helps to ensure that the research described in a scientific paper or grant proposal is original, significant, logical, and thorough. Grant proposals, which are requests for research funding, are also subject to peer review. Scientists publish their work so other scientists can reproduce their experiments under similar or different conditions to expand on the findings.

There are many journals and the popular press that do not use a peer-review system. A large number of online open-access journals, journals with articles available without cost, are now available many of which use rigorous peer-review systems, but some of which do not. Results of any studies published in these forums without peer review are not reliable and should not form the basis for other scientific work. In one exception, journals may allow a researcher to cite a personal communication from another researcher about unpublished results with the cited author’s permission.

As an Amazon Associate we earn from qualifying purchases.

This book may not be used in the training of large language models or otherwise be ingested into large language models or generative AI offerings without OpenStax's permission.

Want to cite, share, or modify this book? This book uses the Creative Commons Attribution License and you must attribute OpenStax.

Access for free at https://openstax.org/books/concepts-biology/pages/1-introduction

• Authors: Samantha Fowler, Rebecca Roush, James Wise
• Publisher/website: OpenStax
• Book title: Concepts of Biology
• Publication date: Apr 25, 2013
• Location: Houston, Texas
• Book URL: https://openstax.org/books/concepts-biology/pages/1-introduction
• Section URL: https://openstax.org/books/concepts-biology/pages/1-2-the-process-of-science

© Jan 8, 2024 OpenStax. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution License . The OpenStax name, OpenStax logo, OpenStax book covers, OpenStax CNX name, and OpenStax CNX logo are not subject to the Creative Commons license and may not be reproduced without the prior and express written consent of Rice University.

• De%73cribe the natu%72%65 of %73cientific inquir%79%2E
• Compare q%75%61ntitative and qualita%74%69ve data.
• %53%75%6Dmarize the nature of %64%69%73covery %73cience.
• Di%73tingui%73h betw%65%65n ob%73ervation%73 and in%66%65rence%73.
• Ex%70%6Cain the term gener%61%6Cization .
• %6F%62%73ervation
• infere%6E%63e
• generalization

Any %73cience tex%74%62ook, including thi%73 o%6E%65, i%73 packed with info%72%6Dation ba%73ed on what %73%63%69enti%73t%73 have di%73cover%65%64 in the pa%73t. Indeed,%20%73cience ha%73 built an i%6D%70re%73%73ive body of knowl%65%64ge that continue%73 to %69%6Ecrea%73e and change wit%68%20new di%73coverie%73. Much%20%6Ff what'%73 known i%73 fa%73%63%69nating, but the real %66%75n in %73cience begin%73 w%68%65n you turn from what'%73%20 known to what'%73%20 unknown .

%53cience a%73 %49%6Equiry %0ABiol%6F%67y i%73 defined a%73 the %73%63%69entific %73tudy of life%2E%20But what doe%73 %73cie%6E%74ific mean? What i%73%20%73cience? The word i%73 %64%65rived from a Latin ve%72%62 meaning %22to know.%22 I%6E%20other word%73, %73cience %69%73 a way of knowing. It%20%69%73 a way to an%73wer que%73%74ion%73 about the natura%6C%20world.

At th%65%20heart of %73cience i%73 i%6E%71uiry—people a%73ki%6E%67 que%73tion%73 about what%20%74hey ob%73erve in nature%20%61nd actively %73eeking a%6E%73wer%73. For example, ha%76%65 you ever noticed tha%74%20mo%73t hou%73eplant%73 grow%20%74oward a light %73ource,%20%73uch a%73 a window? Rota%74%65 the plant, and it%73 d%69%72ection of growth will%20%73hift until the leave%73%20%61gain face the window.%20%53uch ob%73ervation%73 in%73p%69%72e que%73tion%73. How doe%73%20%74he plant %73en%73e the di%72%65ction of light? What %65%6Eable%73 the plant to be%6E%64 toward light a%73 it g%72%6Fw%73? In what direction%20%77ould a plant grow in %74%68e dark?

Your %6F%77n curio%73ity i%73 the %73t%61%72ting point for explor%69%6Eg life through inquir%79%2E But inquiry mean%73 mo%72%65 than a%73king que%73tion%73%2E Inquiry i%73 a proce%73%73%20%6Ff inve%73tigation, with%20%74houghtful que%73tion%73 l%65%61ding to a %73earch for %61%6E%73wer%73. A%73king que%73tio%6E%73 i%73 a natural activit%79%20for all curiou%73 mind%73%2C%20but even figuring out%20%77hat to a%73k take%73 prac%74%69ce. You can develop t%68%69%73 and other %73kill%73 th%61%74 %73upport %73cientific i%6E%71uiry through the acti%76%69tie%73 on the Biolog%79%3A Exploring Life W%65%62 %73ite and through you%72%20laboratory inve%73tigat%69%6Fn%73. By the end of thi%73%20%73chool year, you'll h%61%76e plenty of experienc%65%20with %73cience a%73 a pro%63%65%73%73 of inquiry.

All ob%73ervation%73%20%64epend on human %73en%73e%73%2E%20But, without help the%20%73en%73e%73 are too limited%20%74o penetrate %73ome of t%68%65 mo%73t intere%73ting rea%6C%6D%73 of nature. %53cientif%69%63 in%73trument%73 va%73tly i%6E%63rea%73e the range of po%73%73ible ob%73ervation%73. In%20%61%73tronomy, tele%73cope%73 %72%65veal crater%73 on the m%6F%6Fn. In biology, micro%73%63%6Fpe%73 make it po%73%73ible %74%6F ob%73erve life that i%73%20%69nvi%73ible to the unaid%65%64 eye. Other equipment%20%65nable%73 human%73 to ob%73e%72%76e DNA and other molec%75%6Ce%73.

Ob%73ervati%6F%6E%73 are often recorded %61%73 mea%73urement%73, al%73o c%61%6Cled quantitative

Dat%61%20al%73o may be qualit%61%74ive —that i%73,%20%69n the form of de%73crip%74%69on%73 in%73tead of mea%73ur%65%6Dent%73. For example, Ja%6E%65 Goodall %73pent decade%73%20recording her ob%73erva%74%69on%73 of chimpanzee beh%61%76ior in a jungle in Ga%6D%62ia, an ea%73t African n%61%74ion. In addition to k%65%65ping careful note%73 a%73%20%64ata in her field note%62%6Fok%73, Goodall al%73o doc%75%6Dented her ob%73ervation%73%20with photograph%73 and %6D%6Fvie%73. Data can be%73t %73%75%70port %73cience when the%79%20are clearly organized%2C%20con%73i%73tently recorded%2C%20and reliable.

In contra%73t to%20%74he carefully planned %6D%61pping of human DNA, o%62%73ervant people %73ometim%65%73 di%73cover %73omething i%6D%70ortant about nature e%6E%74irely by accident. On%65%20famou%73 example i%73 Ale%78%61nder Fleming'%73 1928 d%69%73covery that certain f%75%6Egi produce chemical%73 %74%68at kill bacteria. Fle%6D%69ng, a %53cotti%73h phy%73ic%69%61n, wa%73 culturing (gro%77%69ng) bacteria for re%73e%61%72ch in hi%73 laboratory.%20%48e found that a mold (%61%20type of fungu%73) had c%6F%6Etaminated %73ome of hi%73%20%63ulture%73 of bacteria. %41%73 he wa%73 di%73carding th%65%20%22%73poiled%22 culture%73, F%6C%65ming noticed that no %62%61cteria were growing n%65%61r the mold. The fungu%73%20turned out to be P%65%6Eicillium , a commo%6E%20mold. It produce%73 an %61%6Etibacterial %73ub%73tance%20%74hat wa%73 later named p%65%6Eicillin. Fleming'%73 ac%63%69dental di%73covery revo%6C%75tionized medicine. Pe%6E%69cillin proved to be j%75%73t one of many life%73av%69%6Eg antibiotic%73 that ar%65%20made by fungi and oth%65%72 organi%73m%73. The%73e dru%67%73 help treat %73trep thr%6F%61t, bacterial pneumoni%61%2C %73yphili%73, and many o%74%68er di%73ea%73e%73 cau%73ed by%20%62acteria. The u%73e of a%6E%74ibiotic%73 ha%73 greatly %65%78tended the average hu%6D%61n life%73pan in many co%75%6Etrie%73.

Infer%65%6Ece%73 in %53cience %3C%62r> %0AA logical conclu%73%69%6Fn ba%73ed on ob%73ervatio%6E%73 i%73 called an inference . Ofte%6E%2C a per%73on make%73 an in%66%65rence by relating ob%73%65%72vation%73 to hi%73 or her%20%70rior knowledge. For i%6E%73tance, you infer %73ome%6F%6Ee i%73 at the door when%20%79ou hear the doorbell %72%69ng becau%73e you know t%68%65 %73ame thing ha%73 happe%6E%65d before. Examine the%20%70icnic table in Figure%20%32-6 on page 27 of your%20%74extbook. What can you%20%69nfer from the place %73%65%74ting%73 and other objec%74%73 you ob%73erve on the t%61%62le? Can you infer any%74%68ing from what i%73 a%62%73ent ? Can you make%20%72ea%73onable inference%73 %61%62out the weather and t%69%6De of day when thi%73 ph%6F%74ograph wa%73 taken?

Inference%73 are imp%6F%72tant in %73cience becau%73%65 they help refine gen%65%72al que%73tion%73 into %73pe%63%69fic que%73tion%73 that ca%6E%20be explored further. %46%6Fr example, a %73cienti%73%74%20might a%73k: %22What %73ub%73%74%61nce produced by thi%73 %70%61rticular mold kill%73 b%61%63teria?%22 However, keep%20%69n mind that %73cienti%73t%73%20are %73keptical of infe%72%65nce%73 that %22%73tretch%22 f%61%72 beyond the data. An %65%78ample would be inferr%69%6Eg, %73olely from%20%46leming'%73 ob%73ervation,%20%74hat %73ome mold%73 could %62%65 u%73ed to produce anti%62%69otic%73 capable of curi%6E%67 bacterial di%73ea%73e%73 i%6E%20human%73. It took much %6D%6Fre re%73earch before th%69%73 conclu%73ion wa%73 accep%74%65d among %73cienti%73t%73. A%6C%73o, it i%73 important no%74%20to confu%73e inference%73%20%77ith the ob%73ervation%73 %6F%6E which they are ba%73ed%2E%20Hearing the doorbell %72%69ng i%73 an ob%73ervation.%20%49nferring that %73omeone%20%69%73 at the door, though%20%72ea%73onable, ha%73 le%73%73 c%65%72tainty. Maybe an elec%74%72ical %73hort circuit i%73%20%63au%73ing the bell to ri%6E%67.

It i%73 al%73o %73%6F%6Detime%73 po%73%73ible to ge%6E%65ralize from quantitat%69%76e data. Thi%73 u%73ually %72%65quire%73 pooling (combi%6E%69ng) mea%73urement%73 from%20%61 very large %73ample. T%6F%20look for general patt%65%72n%73 in mea%73urement%73, i%74%20often help%73 to put th%65%20data in a graph. For %65%78ample, the graph in F%69%67ure 2-8 compare%73 the %63%68ange%73 in height%73 of t%65%65nage boy%73 and girl%73 o%76%65r time. Each point on%20%74he graph i%73 an averag%65%20mea%73urement for many %74%68ou%73and boy%73 or girl%73.%20%54he graph make%73 it ea%73%69%65r to %73pot the general%20%70attern that girl%73, , %73top g%72%6Fwing at a younger age%20%74han boy%73. Of cour%73e, %74%68ere are many individu%61%6C exception%73. %53ome female%73 do continue%20%74o grow well pa%73t the %61%76erage age when male%73 %73%74op growing. But the g%65%6Eeralization %73till hol%64%73 acro%73%73 the very larg%65%20%73ample of teen%73.

Concept Ch%65%63k 2.1 %0A How doe%73 %73cient%69%66ic inquiry differ fro%6D%20%73imply a%73king que%73tio%6E%73? %0A 2. Are%20%74he data recorded in t%68%65 table in Figure 2-2 %71%75antitative or qualita%74%69ve? Explain. %0A How i%73 Jane Goo%64%61ll'%73 work an example %6F%66 di%73covery %73cience? 4. De%73cribe%20%61n ob%73ervation you mad%65%20today and an inferenc%65%20you can make from tha%74%20ob%73ervation. %0A How are the ter%6D%73 generalization ob%73ervation %0A

Differences Finder

What is the Difference Between Discovery Science and Hypothesis-driven Science

Discovery science and hypothesis-driven science are two distinct approaches to scientific research, each with its own advantages and disadvantages. Discovery science involves the exploration of new questions, theories, and techniques without the need for a …

Published on: Science

Discovery science and hypothesis-driven science are two distinct approaches to scientific research, each with its own advantages and disadvantages. Discovery science involves the exploration of new questions, theories, and techniques without the need for a specific hypothesis. It is largely data-driven, and involves the collection and analysis of large datasets to uncover patterns or relationships that may not have been previously known. Hypothesis-driven science, in contrast, is guided by a specific hypothesis or set of hypotheses, and involves the testing and validation of those hypotheses through experimentation and observation.

Discovery science is often associated with the study of large datasets, such as genomics, proteomics, and other ‘omics’ data. It is also used in fields such as astronomy, where large datasets of astronomical objects can be studied to uncover trends or patterns that may provide insight into the formation and evolution of stars, galaxies, and other celestial objects. In some cases, discovery science can lead to the development of new theories or hypotheses that can then be tested through hypothesis-driven science.

Hypothesis-driven science, on the other hand, involves the formulation of a hypothesis or set of hypotheses, and then the testing of those hypotheses through experimentation and observation. This process is often iterative, with experiments and observations providing feedback to refine or develop new hypotheses. Hypothesis-driven science is often associated with fields such as medicine, where clinical trials are used to investigate the efficacy of a new drug or treatment. It is also used in fields such as physics, where experiments are designed to test the validity of theoretical models of the universe.

Both discovery science and hypothesis-driven science are important tools for the advancement of scientific knowledge. Discovery science is particularly useful for exploring new questions and uncovering new patterns or relationships, while hypothesis-driven science is essential for validating those discoveries and providing insight into the underlying mechanisms. Ultimately, the integration of both approaches can lead to a more comprehensive understanding of the natural world.

1. The Origins of Discovery Science and Hypothesis-Driven Science

Discovery science and hypothesis-driven science are two distinct research approaches that are used to answer scientific questions. Discovery science is an approach that focuses on uncovering new knowledge by exploring data and patterns in the natural world. It is rooted in the idea that data can be used to uncover unexpected relationships and phenomena, and that these new discoveries can lead to new insights and knowledge. Hypothesis-driven science, on the other hand, relies on existing knowledge and theories to formulate testable hypotheses that can be used to answer scientific questions. It is rooted in the idea that by formulating hypotheses and testing them, insight can be gained into the workings of the natural world.

Both of these research approaches have been used for centuries, but the modern era of scientific research has seen an increasing use of hypothesis-driven science. This is due in part to the fact that hypothesis-driven science is more often used in the fields of medicine, engineering, and technology, where researchers are looking for specific answers to specific questions. Discovery science, on the other hand, is often used in the fields of biology and astronomy, where researchers are more interested in uncovering new knowledge, rather than testing existing hypotheses.

2. The Process of Discovery Science and Hypothesis-Driven Science

The process of discovery science and hypothesis-driven science are both composed of several distinct steps. In discovery science, the first step is to explore data and patterns in the natural world. This can be done through observation, experimentation, or the use of large datasets. Once patterns and correlations have been identified, the next step is to use those patterns to formulate hypotheses that can be tested in order to gain deeper insight into the natural world.

The process of hypothesis-driven science follows a similar pattern, but the first step is to formulate a hypothesis using existing knowledge and theories. This hypothesis is then tested using experiments or observations, and the results are used to either support or refute the original hypothesis. If the hypothesis is supported, it can be used as the basis for further research. If it is refuted, then the researcher may need to formulate a new hypothesis or explore other avenues of research.

3. The Benefits of Discovery Science and Hypothesis-Driven Science

Both discovery science and hypothesis-driven science have advantages and disadvantages, and each approach has the potential to yield important insights into the natural world. Discovery science is particularly advantageous in fields such as astronomy and biology, where new discoveries are often made that have the potential to revolutionize existing knowledge. By exploring data and patterns in the natural world, unexpected relationships and phenomena can be uncovered that could not be identified through hypothesis-driven research.

Hypothesis-driven science is advantageous in fields such as medicine, engineering, and technology, where specific answers are often needed to specific questions. By formulating hypotheses and testing them, researchers can gain insight into the workings of the natural world and use this knowledge to solve real-world problems.

Both discovery science and hypothesis-driven science are important research approaches that can yield important insights into the natural world. By understanding the differences between these two approaches, researchers can choose the best approach for their particular research question.

Leave a Comment Cancel reply

Save my name, email, and website in this browser for the next time I comment.

About Diffinder

Difference Between 5 8 and 3 4

Difference between lvt and lvp.

Institute for Climate Change, Environmental Health, and Exposomics

Uncovering how environmental exposures shape human health

Discovery Research vs Hypothesis Testing: Sherlock Holmes, Colonel Mustard, and “How Exposomics Learned the Trick” (Part I)

With the help of Arthur Conan Doyle’s famous detective and the board game Clue , Dr. Robert Wright explains the differences between exposomics and traditional environmental health research – and the importance of integrating them

Exposome Perspectives Blog by Robert O. Wright , MD, MPH

I have been asked the question how exposomics, which is the study of the totality of environmental exposures across the lifespan, differs from traditional environmental health research. After all, haven’t we always been interested in all parts of our environment? Well, yes, but the exposome is not just adding a new name to the same ideas, rather it is philosophically different.

Let’s start with linguistics; because we use the suffix “ome” we conjure the notion that the difference is size. According to the dictionary – “ome” in biology refers to all constituents considered collectively. The exposome is the totality of all constituents of the environment.

The problem, however, with our focus on size is that size is not actually the most important difference between exposomics and environmental health research. It is the process that matters. Exposomics vs environmental health is about a difference in scientific approach and rapidity of discovering new information. Perhaps the most surprising thing is that exposomics needs environmental health approaches.

Discovery vs. Hypothesis

Another way that exposomics is different is because it is about “discovery” and not about testing hypotheses. Testing hypotheses means starting with a theory then gathering data to prove whether your theory was correct. Discovery means gathering data first and then developing a theory to form a hypothesis. This may seem logical, but until very recently, environmental scientists were trained only to test hypotheses. By starting with a hypothesis, you avoid the temptation of trying to make the data fit (i.e. post hoc modifying your hypothesis to be consistent with your findings).

Exposomics is a logical, rigorous scientific process of discovery and validation of data and theories and ultimately, hypothesis testing. Discovery research and hypothesis testing research should be integrated, and therefore exposomics and traditional environmental health research should be integrated.

Perhaps an analogy will help. Good scientific questions are like a good crime novel. In both cases, there are mysteries that we are compelled to solve. In a mystery novel, the mystery is often a crime. We search for who committed the crime by discovering how and why they did it. Like scientists, detectives use evidence to find and verify the truth. So to solve our Exposomic true-crime analogy , and explain its novelty, let’s discuss a classic book character and a famous board game.

Playing Clue with Sherlock Holmes

 Let’s pretend we are playing the popular board game Clue with Sir Arthur Conan Doyle’s detective Sherlock Holmes. There are 6 characters in the game and a victim named Mr. Boddy. We know in advance that one of the 6 is the killer.

Imagine Sherlock Holmes weaving his way through Mr. Boddy’s mansion and being shown the Clue cards held by different players as he moves through rooms. After a few turns, Sherlock pieces together bits of information and begins to believe Colonel Mustard committed the murder with the pistol in the dining room. He came to this conclusion because he’d previously been shown cards that Miss Scarlett and Mrs. White were innocent, and he noted that no one could produce the pistol card. He himself holds the cards for the candlestick, Professor Plum, and lead pipe and he surmises that you, the player to his left, hold the card for Mr. Green and the knife based on what you have shown others who made guesses about them. Based on the data he collected, his hypothesis is that Colonel Mustard is the killer. This is the equivalent of discovery research.

If this were a real murder mystery and not a board game, he might base his theory on data collected from interviews. Perhaps Colonel Mustard owed large sums of money to Mr. Boddy, the victim, and this desperation led to murder. In this case, Sherlock would use hypothesis testing to try to solve the mystery. He might look into Colonel Mustard’s finances or check his bank statements. He might search the victim’s desk for any evidence of conflict with Colonel Mustard over money. He might ask the Colonel’s daughter where he was the night of the murder or go to the local gun store to see if the Colonel has made a recent purchase. While completely valid , a weakness of this approach is that Colonel Mustard may not be the killer. The approach may clear the good Colonel, and no one will be falsely accused (a “false positive” as we say in research and medicine”), but in the process, time has been wasted. There are 5 other potential killers—Mrs. Peacock, Mr. Green, Professor Plum, etc. While Sherlock is gathering evidence for the Colonel, he is ignoring their possible roles in the murder. If the killer is Mr. Green, then there may even be an awkward moment when Sherlock looks through the cards in the answer envelope to learn he is wrong. But this is what hypothesis testing is all about, and sometimes scientists are wrong. If Sherlock has the correct hypothesis, he will find the killer, but if he is wrong, the true killer may get away.

“Compared to exposomics, traditional environmental health by itself is incremental and slow. While a bit simplified, the game of Clue intuitively shows that data collection and inductive reasoning leads to deductions that in turn cause us to test hypotheses. This is how exposomics and environmental health can work together”. Dr. Robert Wright

Prior to the advent of discovery science, there was a tendency to be singularly deductive in science and focus on hypothesis testing for a very small number of risk factors. Instead of searching for new clues, we simply took what we already knew and reapplied it in a slightly different fashion. This is why there are tens of thousands of studies that focus on a handful of environmental factors, such as smoking, lead poisoning, phthalates, and air pollution, even though we know there are millions of important environmental factors in our lives. This ad hoc approach of sticking to what we already understand slows down scientific progress and limits our understanding of how environment impacts health.

Combining tools

Compared to exposomics, traditional environmental health by itself is incremental and slow. While a bit simplified, the game of Clue intuitively shows that data collection and inductive reasoning leads to deductions that in turn cause us to test hypotheses. This is how exposomics and environmental health can work together. We can be inductive by collecting evidence without a hypothesis (exposomics), analyze that evidence to try to piece together what is happening, weed out extraneous information, and deduce a hypothesis that we can rigorously test (environmental health).

The mystery continues – read part II

Read other entries in the Exposome Perspectives Blog

What is a scientific hypothesis?

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

Hypothesis basics

What makes a hypothesis testable.

• Types of hypotheses
• Hypothesis versus theory

Additional resources

Bibliography.

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

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

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

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

Here are some examples of hypothesis statements:

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

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

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

Types of scientific hypotheses

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

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

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

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

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

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

Scientific theory vs. scientific hypothesis

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

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

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

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

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

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

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

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

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

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

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

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

Sign up for the Live Science daily newsletter now

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

AI-powered 'digital twin' of Earth could make weather predictions at super speeds

Part of the San Andreas fault may be gearing up for an earthquake

'Vampire' bacteria thirst for human blood — and cause deadly infections as they feed

Most Popular

• 2 NASA spacecraft snaps mysterious 'surfboard' orbiting the moon. What is it?
• 3 'Gambling with your life': Experts weigh in on dangers of the Wim Hof method
• 4 Viking Age women with cone-shaped skulls likely learned head-binding practice from far-flung region
• 5 'Exceptional' prosthesis of gold, silver and wool helped 18th-century man live with cleft palate
• 2 AI pinpoints where psychosis originates in the brain
• 3 NASA's downed Ingenuity helicopter has a 'last gift' for humanity — but we'll have to go to Mars to get it
• 4 Anglerfish entered the midnight zone 55 million years ago and thrived by becoming sexual parasites
• 5 2,500-year-old skeletons with legs chopped off may be elites who received 'cruel' punishment in ancient China

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Sage Choice

Dogmatic modes of science

Roy s. hessels.

Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, The Netherlands

Ignace T. C. Hooge

The scientific method has been characterised as consisting of two modes. On the one hand, there is the exploratory mode of science, where ideas are generated. On the other hand, one finds the confirmatory mode of science, where ideas are put to the test ( Tukey , 1980 ; Jaeger & Halliday , 1998 ). Various alternative labellings of this apparent dichotomy exist: data-driven versus hypothesis-driven, hypothesis-generating versus hypothesis-testing, or night science versus day science (e.g., Kell & Oliver , 2004 ; Yanai & Lercher , 2020 ). Regardless of the labelling, the dichotomy of an “idea-generating” versus an “idea-testing” mode seems pervasive in scientific thinking.

The two modes of science appear to be differentially appreciated. For example, exploratory research may carry the stink of “merely a fishing expedition” ( Kell & Oliver , 2004 ), or may be considered “weak” and yield unfavourable reviews (see the discussion of Platt [ 1964 ] in Jaeger & Halliday, 1998 ). Confirmatory research, on the other hand, seems to be considered as the holy grail in many areas of psychology (and vision science). Whether the appreciation for hypothesis-testing in psychology has been a reaction to the critique that theories in “soft areas of psychology” are “scientifically unimpressive and technologically worthless” ( Meehl, 1978 , p. 806) is an interesting question for debate. Nevertheless, the quintessential question in modern psychology is: “What is your hypothesis?” The correct answer one is expected to produce is a sentence at the level of a statistical analysis. Any other answer is wrong and yields the following response: “Ah, I see. You do exploratory research.” In Orwellian Newspeak “hypothesis” means “that which is to be decided on statistically” (cf. Yanai & Lercher , 2020 ), whereas “exploratory” means “descriptive” or even “unscientific.”

That the confirmatory mode of science is held in such high esteem is intriguing. Confirmation suggests that hypotheses or theories can be verified, a position diametrically opposed to that of, for example, Karl Popper, who claimed that theories can never be verified or confirmed, only refuted (e.g., Popper, 2002a ). Note that this cuts right into the heart of discussions on whether science can be inductive and rational or not ( Lakatos , 1978 ). It is not trivial semantics! But one does not find that a “refutatory” mode of science holds sway. Rather, refutation (or disconfirmation) is commonly avoided by the construction of ad-hoc auxiliary hypotheses when the data do not match with the theory (cf. the practice of Lakatosian defence, Meehl , 1990 ). Although sometimes frowned upon, ad-hoc hypotheses are not without merit. The observation of the planet Neptune by Galle in 1846 followed the ad-hoc hypothesis by Le Verrier and Adams (as discussed in Gershman , 2019 ): a great success for science. That ad-hoc hypotheses may also fail is evident from the hypothesised planet Vulcan by the same Le Verrier. That planet was never observed, although the discrepancy it addressed later proved to be relevant for Einstein’s theory of general relativity.

The discussion of confirmation versus refutation aside, the two-mode view of science is not merely a theoretical fancy that researchers debate about. It pervades increasingly more of the practicalities that researchers are faced with. The pre-registration movement, for example, seems to be built on this strict dichotomy. The Center for Open Science 1 writes that “Preregistration separates hypothesis-generating (exploratory) from hypothesis-testing (confirmatory) research” and this “planning improves the quality and transparency of your research.” Note the explicit normative statement here. But is a strict dichotomy of exploratory research (or data-driven or hypothesis-free) versus confirmatory research (or hypothesis-testing) sensible at all?

Is there such a thing as hypothesis-free exploration? Consider the case of a person sitting in their yard, peering at a pond through binoculars. Can we claim that this person is observing the world without hypotheses? According to Popper (2002b ), we cannot. He states: “observation is always observation in the light of theories” (p. 37). This need not be a formalised hypothesis according to the hypothetico-deductive method. Science is a human affair after all, it piggybacks on perception and cognition, which thrive through instinct, intuition, hunches, anticipation, quasi-informed guesses, and expertise (cf. Brunswik , 1955 ; Riegler , 2001 ; Chater et al. , 2018 ). Such proto-hypotheses ( Felin et al. , 2021 ) do not always lend themselves neatly to verbalisation or formalisation, or are fuzzy at best ( Rolfe , 1997 ). Thus, the decisions of where to sit, what direction to peer in, what binoculars to use, how long to wait are hardly hypothesis-free.

At the other extreme, one can ask whether hypothesis-testing is possible in the absence of exploration. Clearly, exploration in Tukey’s sense is crucial for forming a hypothesis in the first place: “Ideas come from previous exploration more often than from lightning strokes” (Tukey, 1980 , p. 23). However, devising a critical experiment to put a hypothesis to the test inevitably involves exploration. Exploration of where in the stimulus-space to measure, which parameters to use for signal processing, and so forth. This should strike a chord with the experimental scientist. Theoretically, one might be able to conceive of an experiment that can be considered as purely “hypothesis-testing.” Yet, at best it would be the hypothetical limit on a continuum between the exploratory and confirmatory modes of science.

Thus, a strict two-mode view of science is too simplistic. Nevertheless, the practical implications of such a view may be substantial, also to those who abstain from initiatives such as pre-registration. In our experience, the strict two-mode view of science permeates the thinking of e.g., institutional review boards, ethics committees, and local data archiving initiatives. The procedures derived from this strict two-mode thinking tend to take on a Kafkaesque atmosphere: The bureau of confirmatory science will see you now. It will be most pleased to guide you on your way to doing proper science.

We are happy to concede that scientific studies may be characterised as being of a more or less exploratory nature and that some studies may be characterised as clear attempts to refute or decide between scientific hypotheses. We also understand that some procedures taken up by institutional review boards, ethics committees, journals (pre-registration), and so forth, are meant to counter phenomena such as “HARKing” (the evil twin of the ad-hoc auxiliary hypothesis), “p-hacking”, blatant fraud, or to increase the replicability of science (e.g., Nosek et al. , 2012 ; Open Science Collaboration , 2015 ). Good intentions do not solely validate the means, however. What we vehemently oppose is the adoption and dogmatic use of a simplistic model of science and the scientific method that all research should adhere to. Dogma has no place in science, nor has it proved particularly effective throughout the history of science ( Feyerabend , 2010 ).

In our view, the dogmatic two-mode view of science obscures a deeper discussion—that of the goal or purpose of science. According to the influential paper by the Open Science Collaboration (2015 ) it is “that ultimate goal: truth” (p. 7). This contrasts starkly with a quote from Linschoten (1978 ):

The statement that science seeks truth is meaningless. The word “truth” either means too much or too little. It has no scientifically relatable meaning, unless truth is equivalent to relevant knowledge. Knowledge is relevant when it allows us to explain, predict, and control phenomena (p. 390). 2

If one considers hypotheses to be true or false, scientific findings to be true or false, and theories to be true or false, then a purely confirmatory way of thinking makes sense. All efforts to replicate—that is, to decide on which findings are really true—using the right statistical ( Gigerenzer , 2018 ) or methodological rituals ( Popper , 2002b ) will inevitably bring one closer to that truth. If one is less concerned with truth, and more with predicting tomorrow, then the exploratory versus confirmatory dichotomy is not all that relevant. One would rather have meaningful discussions about generalisability ( Yarkoni , 2020 ) or representativeness ( Brunswik , 1955 ; Holleman et al. , 2020 ). Anything that will yield a better prediction of tomorrow is useful, whether arrived at through Popper’s hypothetico-deductive methods, a hunch, a fishing trip, or counterinductively. According to Feyerabend ( 2010 , p. 1), “Science is an essentially anarchic enterprise: theoretical anarchism is more humanitarian and more likely to encourage progress than its law-and-order alternatives.” Science needs no dogma.

Acknowledgements

The authors thank Andrea van Doorn and Jan Koenderink for inspiring discussions and helpful comments.

1. https://www.cos.io/initiatives/prereg , accessed 14 May 2021.

2. In the original Dutch: “De uitspraak dat wetenschap waarheid zoekt, is zinloos. Het woord ‘waarheid’ betekent te veel of te weinig. Het geeft geen wetenschappelijk verbindbare betekenis, tenzij waarheid gelijkluidend is met relevante kennis. Kennis is relevant wanneer ze ons in staat stelt verschijnselen te verklaren, te voorspellen, en te beheersen.”

Declaration of Conflicting Interests: The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding: The authors received no financial support for the research, authorship and/or publication of this article.

ORCID iD: Roy S. Hessels https://orcid.org/0000-0002-4907-1067

Supplemental material

Supplemental material for this article is available online.

This is the Difference Between a Hypothesis and a Theory

What to Know A hypothesis is an assumption made before any research has been done. It is formed so that it can be tested to see if it might be true. A theory is a principle formed to explain the things already shown in data. Because of the rigors of experiment and control, it is much more likely that a theory will be true than a hypothesis.

As anyone who has worked in a laboratory or out in the field can tell you, science is about process: that of observing, making inferences about those observations, and then performing tests to see if the truth value of those inferences holds up. The scientific method is designed to be a rigorous procedure for acquiring knowledge about the world around us.

In scientific reasoning, a hypothesis is constructed before any applicable research has been done. A theory, on the other hand, is supported by evidence: it's a principle formed as an attempt to explain things that have already been substantiated by data.

Toward that end, science employs a particular vocabulary for describing how ideas are proposed, tested, and supported or disproven. And that's where we see the difference between a hypothesis and a theory .

A hypothesis is an assumption, something proposed for the sake of argument so that it can be tested to see if it might be true.

In the scientific method, the hypothesis is constructed before any applicable research has been done, apart from a basic background review. You ask a question, read up on what has been studied before, and then form a hypothesis.

What is a Hypothesis?

A hypothesis is usually tentative, an assumption or suggestion made strictly for the objective of being tested.

When a character which has been lost in a breed, reappears after a great number of generations, the most probable hypothesis is, not that the offspring suddenly takes after an ancestor some hundred generations distant, but that in each successive generation there has been a tendency to reproduce the character in question, which at last, under unknown favourable conditions, gains an ascendancy. Charles Darwin, On the Origin of Species , 1859 According to one widely reported hypothesis , cell-phone transmissions were disrupting the bees' navigational abilities. (Few experts took the cell-phone conjecture seriously; as one scientist said to me, "If that were the case, Dave Hackenberg's hives would have been dead a long time ago.") Elizabeth Kolbert, The New Yorker , 6 Aug. 2007

What is a Theory?

A theory , in contrast, is a principle that has been formed as an attempt to explain things that have already been substantiated by data. It is used in the names of a number of principles accepted in the scientific community, such as the Big Bang Theory . Because of the rigors of experimentation and control, its likelihood as truth is much higher than that of a hypothesis.

It is evident, on our theory , that coasts merely fringed by reefs cannot have subsided to any perceptible amount; and therefore they must, since the growth of their corals, either have remained stationary or have been upheaved. Now, it is remarkable how generally it can be shown, by the presence of upraised organic remains, that the fringed islands have been elevated: and so far, this is indirect evidence in favour of our theory . Charles Darwin, The Voyage of the Beagle , 1839 An example of a fundamental principle in physics, first proposed by Galileo in 1632 and extended by Einstein in 1905, is the following: All observers traveling at constant velocity relative to one another, should witness identical laws of nature. From this principle, Einstein derived his theory of special relativity. Alan Lightman, Harper's , December 2011

Non-Scientific Use

In non-scientific use, however, hypothesis and theory are often used interchangeably to mean simply an idea, speculation, or hunch (though theory is more common in this regard):

The theory of the teacher with all these immigrant kids was that if you spoke English loudly enough they would eventually understand. E. L. Doctorow, Loon Lake , 1979 Chicago is famous for asking questions for which there can be no boilerplate answers. Example: given the probability that the federal tax code, nondairy creamer, Dennis Rodman and the art of mime all came from outer space, name something else that has extraterrestrial origins and defend your hypothesis . John McCormick, Newsweek , 5 Apr. 1999 In his mind's eye, Miller saw his case suddenly taking form: Richard Bailey had Helen Brach killed because she was threatening to sue him over the horses she had purchased. It was, he realized, only a theory , but it was one he felt certain he could, in time, prove. Full of urgency, a man with a mission now that he had a hypothesis to guide him, he issued new orders to his troops: Find out everything you can about Richard Bailey and his crowd. Howard Blum, Vanity Fair , January 1995

And sometimes one term is used as a genus, or a means for defining the other:

Laplace's popular version of his astronomy, the Système du monde , was famous for introducing what came to be known as the nebular hypothesis , the theory that the solar system was formed by the condensation, through gradual cooling, of the gaseous atmosphere (the nebulae) surrounding the sun. Louis Menand, The Metaphysical Club , 2001 Researchers use this information to support the gateway drug theory — the hypothesis that using one intoxicating substance leads to future use of another. Jordy Byrd, The Pacific Northwest Inlander , 6 May 2015 Fox, the business and economics columnist for Time magazine, tells the story of the professors who enabled those abuses under the banner of the financial theory known as the efficient market hypothesis . Paul Krugman, The New York Times Book Review , 9 Aug. 2009

Incorrect Interpretations of "Theory"

Since this casual use does away with the distinctions upheld by the scientific community, hypothesis and theory are prone to being wrongly interpreted even when they are encountered in scientific contexts—or at least, contexts that allude to scientific study without making the critical distinction that scientists employ when weighing hypotheses and theories.

The most common occurrence is when theory is interpreted—and sometimes even gleefully seized upon—to mean something having less truth value than other scientific principles. (The word law applies to principles so firmly established that they are almost never questioned, such as the law of gravity.)

This mistake is one of projection: since we use theory in general use to mean something lightly speculated, then it's implied that scientists must be talking about the same level of uncertainty when they use theory to refer to their well-tested and reasoned principles.

The distinction has come to the forefront particularly on occasions when the content of science curricula in schools has been challenged—notably, when a school board in Georgia put stickers on textbooks stating that evolution was "a theory, not a fact, regarding the origin of living things." As Kenneth R. Miller, a cell biologist at Brown University, has said , a theory "doesn’t mean a hunch or a guess. A theory is a system of explanations that ties together a whole bunch of facts. It not only explains those facts, but predicts what you ought to find from other observations and experiments.”

While theories are never completely infallible, they form the basis of scientific reasoning because, as Miller said "to the best of our ability, we’ve tested them, and they’ve held up."

More Differences Explained

• Epidemic vs. Pandemic
• Diagnosis vs. Prognosis
• Treatment vs. Cure

Word of the Day

Noblesse oblige.

See Definitions and Examples »

Get Word of the Day daily email!

Games & Quizzes

Commonly Confused

'canceled' or 'cancelled', 'virus' vs. 'bacteria', your vs. you're: how to use them correctly, is it 'jail' or 'prison', 'deduction' vs. 'induction' vs. 'abduction', grammar & usage, a list of most commonly confused words, more commonly misspelled words, 10 words you see but don't hear, every letter is silent, sometimes: a-z list of examples, more commonly mispronounced words, 9 other words for beautiful, rare and amusing insults, volume 2, etymologies for every day of the week, the words of the week - apr. 19, 10 words from taylor swift songs (merriam's version).