definition of hypothesis in plants

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

Table of Contents

What Is Hypothesis?

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

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

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

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

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

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

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

Characteristics Of Hypothesis

A useful hypothesis must have the following qualities:

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

Sources Of Hypothesis

Sources of hypothesis are:

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

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

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

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

Types Of Hypothesis

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

Simple Hypothesis

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

Complex Hypothesis

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

Directional Hypothesis

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

Non-directional Hypothesis

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

Null Hypothesis

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

Associative and Causal Hypothesis

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

Examples Of Hypothesis

Examples of simple hypotheses:

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

Examples of a complex hypothesis:

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

Examples of Directional Hypothesis:

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

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

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

Examples of a null hypothesis:

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

Examples of Associative Hypothesis:

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

Functions Of Hypothesis

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

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

How will Hypothesis help in the Scientific Method?

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

Importance Of Hypothesis

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

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

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

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Biology library

Course: biology library > unit 1.

The scientific method

Controlled experiments

The scientific method and experimental design

Introduction

How are hypotheses tested.

One pot of seeds gets watered every afternoon.
The other pot of seeds doesn't get any water at all.

Control and experimental groups

Independent and dependent variables, independent variables, dependent variables, variability and repetition, controlled experiment case study: co 2 ‍ and coral bleaching.

What your control and experimental groups would be
What your independent and dependent variables would be
What results you would predict in each group

Experimental setup

Some corals were grown in tanks of normal seawater, which is not very acidic ( pH ‍ around 8.2 ‍ ). The corals in these tanks served as the control group .
Other corals were grown in tanks of seawater that were more acidic than usual due to addition of CO 2 ‍ . One set of tanks was medium-acidity ( pH ‍ about 7.9 ‍ ), while another set was high-acidity ( pH ‍ about 7.65 ‍ ). Both the medium-acidity and high-acidity groups were experimental groups .
In this experiment, the independent variable was the acidity ( pH ‍ ) of the seawater. The dependent variable was the degree of bleaching of the corals.
The researchers used a large sample size and repeated their experiment. Each tank held 5 ‍ fragments of coral, and there were 5 ‍ identical tanks for each group (control, medium-acidity, and high-acidity). Note: None of these tanks was "acidic" on an absolute scale. That is, the pH ‍ values were all above the neutral pH ‍ of 7.0 ‍ . However, the two groups of experimental tanks were moderately and highly acidic to the corals , that is, relative to their natural habitat of plain seawater.

Analyzing the results

Non-experimental hypothesis tests, case study: coral bleaching and temperature, attribution:, works cited:.

Hoegh-Guldberg, O. (1999). Climate change, coral bleaching, and the future of the world's coral reefs. Mar. Freshwater Res. , 50 , 839-866. Retrieved from www.reef.edu.au/climate/Hoegh-Guldberg%201999.pdf.
Anthony, K. R. N., Kline, D. I., Diaz-Pulido, G., Dove, S., and Hoegh-Guldberg, O. (2008). Ocean acidification causes bleaching and productivity loss in coral reef builders. PNAS , 105 (45), 17442-17446. http://dx.doi.org/10.1073/pnas.0804478105 .
University of California Museum of Paleontology. (2016). Misconceptions about science. In Understanding science . Retrieved from http://undsci.berkeley.edu/teaching/misconceptions.php .
Hoegh-Guldberg, O. and Smith, G. J. (1989). The effect of sudden changes in temperature, light and salinity on the density and export of zooxanthellae from the reef corals Stylophora pistillata (Esper, 1797) and Seriatopora hystrix (Dana, 1846). J. Exp. Mar. Biol. Ecol. , 129 , 279-303. Retrieved from http://www.reef.edu.au/ohg/res-pic/HG%20papers/HG%20and%20Smith%201989%20BLEACH.pdf .

Additional references:

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1.2 Science and Experimentation

Learning objectives

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

Describe why science is considered a discipline of philosophy.
Summarize the four basic types of experiments.
Apply the principles of experimental design in this course and in your daily life.

Thinking about science

The primary goal of this section is to help you think about the nature of science. You might be taking this course to fulfill an undergraduate requirement for a biology course with a lab. This course fulfills that requirement because we investigate the process behind using science as a way of learning about the natural world around us. If you’re starting down the path to becoming a plant scientist, understanding the nature of science will be essential for you in your career

Regardless of whether you’re going to pursue a career as a scientist, now is a good time reflect on the nature of science, and to understand how scientific thinking can become a strategy for resolving many issues that you confront during daily life.

Watch this video about connecting science and experimentation to real life:

Scientific inquiry

While “science” is a word commonly used in our culture, in popular use it is rarely spoken of as a philosophy. By identifying science as a philosophy we are taking an epistemic view , one focusing on how knowledge is acquired.

At its core, science is a mode of inquiry: a way of acquiring new knowledge about the world around us and a strategy for understanding the inner workings of elements in that world. Scientists believe that if we follow the principles of this philosophy we will continue to expand our knowledge about how things work in the world around us. This systematic approach is called the “scientific method.”

There are two key steps in the scientific method:

Hypothesis building through reflective observation.
Hypothesis testing through experimentation.

A “hypothesis” is a question or proposed explanation made on the basis of limited evidence and used as a starting point for experimentation. Experimentation is commonly equated with science—rightly so, because hypotheses evaluated on the basis of evidence generated through experiments. Experimentation, however, isn’t the whole story. Science—including the development and testing of new hypotheses—is also a creative endeavor.

Watch this video about scientific inquiry:

Scientific inquiry has generated a vast body of knowledge about the world around us. Your school science classes might have required you to memorize facts and relationships, and pay attention to detail. Sometimes such memorization leads students to believe that science is just an accumulation of facts rather than the process behind discovering all of that information.

Scientific discovery builds on what is already known. Even the most accomplished scientists initially approach a problem by learning what is already known. Armed with that information, they then apply their own creativity to form new hypotheses about something they have observed, and design experiments to test those hypotheses. They also communicate their results publicly so that others can benefit from their work and have the opportunity to challenge conclusions. In this way, science builds on itself.

The foundational knowledge you learn in science classes prepares you to develop and test hypotheses and to make new discoveries of your own. While a good memory may help you pass a science classes, you will absorb a body of knowledge more effectively when you learn how facts fit and work together in systems rather than learning through the brute force of memorization.

In this section we work from the point of view that science is a way of acquiring knowledge—a mode of inquiry—and that this mode of inquiry follows a process called the scientific method. Those who follow the philosophy of science:

Use it to understand how the natural world works.
Start by learning what is already known.
Carefully observe the subjects of their scientific inquiry and look for details about form, function, and interaction with the environment.
Develop hypotheses about the inner workings of natural phenomena not yet understood.
Test their hypotheses by making observations, conducting experiments and collecting and evaluating evidence.
Communicate with others about their hypotheses, experiments, and the outcomes of their studies so that others can repeat, validate, and build upon their work.

Although science is typically used to understand how the natural world works, it is also regularly applied to the development of new technologies that are based on these natural phenomena and to the solving of problems associated with the natural world.

Putting the scientific method to work

As noted, the scientific method relies on building hypotheses and then testing them through experimentation. In the lab section of this course you will develop hypotheses about the effects of various treatments on propagation success and then conduct experiments to test those hypotheses. Because experimentation is such a key component of the scientific method, we’ll spend time characterizing and examining four types of experimentation and explore whether they are part of the scientific method. While each is valuable when applied in the right circumstances, only one clearly follows each step of the scientific method to uncover new knowledge about the natural world.

Types of experiments

The types of experimentation we will cover are:

Demonstration
Exploration

Demonstration experiments

Students at a picnic table using a microscope.

Demonstration experiments are a classic method used in educational settings to help students learn and understand known relationships already discovered by others. Learners will usually have had prior exposure to the relationships through preliminary observations, lectures, reading, and discussions, and will have some sense of what the experimental outcome might be.

Good demonstration experiments actively involve the learner, who manipulates the experimental materials, applies the treatments, and observes the outcomes, then gathers, analyzes, and interprets the resulting data. Poor demonstration experiments, in contrast, make learners only passive witnesses to something done by an expert at the front of the classroom.

In the plant propagation labs for this course, you will be actively engaged in demonstration experiments. Although you won’t be creating new knowledge, the knowledge will likely be new to you. The hands-on experience of conducting the experiments will help you to learn the concepts more effectively than if you only read a textbook or listened to a lecture. The techniques you learn and use in demonstration experiments often contribute to the learning experience as much as the relationships revealed at the experiment’s conclusion. Employing these techniques will help you gain an understanding of many biological functions, such as the production of adventitious roots and mechanisms for seed dispersal.

While demonstration experiments are valuable for actively learning a body of scientific knowledge previously discovered and communicated by others, the experience is specifically orchestrated for teaching and learning, not for the discovery of new information. Yet since the knowledge is new to the learner, it can still bring the joy of personal discovery and a sense of accomplishment.

In summary, demonstration experiments:

Are designed for teaching and learning.
Address relationships that may be new to you, but are otherwise known.
In their best forms, actively involve the learner.
May emphasize experimental techniques, in addition to outcomes, as part of the learning experience.
Are not the types of experiments that are at the core of practicing science as a way to uncover new knowledge.

Evaluation experiments

Evaluation experiments are designed to help us make decisions, and to choose from a number of options. They might, for instance, help us determine the efficacy of a new treatment relative to a known treatment, or decide on further experimentation. An evaluation experiment will highlight a compound, a technique, a piece of equipment, or an organism, and will include a control and/or other alternatives.

Evaluation experiments are common in horticultural and agronomic research, where the purpose of the experiment is to identify, for example, the best cultivar , production method, pest control, fertility regime, or light intensity for growing a crop. Correct experimental design is crucial for assuring that conclusions from the experiment are meaningful and credible.

Strawberry field experiment divided into treatment blocks

These experiments are typically used in the development of new technologies to identify the best method for the desired purpose (e.g., which pesticides are effective against the target insect, but not harmful to non-target insects). They are not used to discover new knowledge about how the world works, as they typically don’t advance our understanding of the natural world. The information from an evaluation experiment might, however, point the way to additional experimentation that does help us discover new knowledge. This is particularly true if the outcome of an evaluation experiment is unexpected or novel.

In summary, evaluation experiments:

Are used to help in decision-making.
Help users choose a winner or determine efficacy relative to other alternatives.
Are commonly used when evaluating and recommending horticultural production methods.
Can be useful in solving problems and developing technologies.
Require proper experimental design (e.g., comparison to a control) for credibility and meaningfulness.

Exploration experiments

Some scientists specialize in observing and cataloging nature, and some aggressively search for previously unknown phenomena. In the botanical realm, such scientists study the diversity of organisms within habitats, discover new species, or are in other ways very skilled in “seeing” nature. Explorer-scientists recognize and appreciate detail and can identify the enormous diversity among plants by comparing characteristics that might be overlooked by others. They may also have the capacity to recognize possible interrelationships among organisms and with habitats, making their work particularly important to science. They might notice, for instance, that a particular species of plant is commonly found in wet areas but not in dry, or that a particular vegetable tastes sweeter when grown at higher altitudes than when grown closer to sea level. They don’t confirm the cause of these relationships, but are the first to notice them.

Scientist collecting plants in the field.

Explorers’ observations are essential to stimulating the development of sound, testable hypotheses. The possible relationships they propose must be tested to determine whether those relationships actually exist, or are artifacts of other effects. Explorers help develop hypotheses, but the work of exploration, cataloging, and seeing possible relationships don’t prove or disprove the hypotheses or necessarily generate new knowledge about relationships. The work does, however, result in new information about the existence of the object or phenomenon itself. An exception is exploration done to test a hypothesis, such as a mission to test the hypothesis that a particular type of ecosystem is required for reproduction of a particular plant species.

Scientists must resist jumping to conclusions based on exploration and observation alone. If you see two people together many times, for example, you might conclude that they are a romantic couple, when in fact they are brother and sister. Relationships hypothesized as a result of exploration and observation must be experimentally tested before they are accepted or rejected.

Exploration experiments uncover new things, many of which can be exciting and eventually change our view of the world. While one of their greatest values is that they lead to the development of new and stronger hypotheses about how the world works, they go so far as to test those hypotheses or fully engage in the cycle of knowledge generation associated with the scientific method. Additional experiments based on this new information are required to put this new information in context and to advance our understanding of how the natural world works.

In summary then, exploration experiments:

Focus on detailed observation of organisms and habitats.
Increase our knowledge of the natural world.
Identify potential relationships that need to be tested.
Are essential to sound and testable hypothesis-building.

Discovery experiments

Discovery experiments are central to the use of the scientific method in tasks ranging from problem solving to the discovery of new knowledge. They focus on uncovering new relationships and solving problems, follow the scientific method, test hypotheses and their predicted outcomes, and utilize a careful design in order to maintain meaningfulness and credibility.

The similarity between the scientific method and Kolb’s Experiential Learning Cycle is not an accident. The scientific method is a practical strategy based on how we sense and experience the world around us and used to solve problems encountered during those experiences.

Continual cycle of experimentation, actual experience, reflective observation, and hypothesis building

The diagram above illustrates a combination of the scientific method and Kolb’s four-step experiential learning, describing a cyclic process for solving problems that can be applied to disciplines as diverse as molecular biology, global warming, and even appliance repair. While you might initially think that appliance repair doesn’t belong in that list, the difference is one of application, not method. Though far removed from the esoteric scientific discoveries we associate with scientific method, appliance repair follows the same steps. Appliances are often, and quite literally, boxes, where you don’t know what is going on inside. But what’s going on inside is knowable, and through that knowledge comes repair.

Illustration of the parts of a washing machine.

The learning/problem solving/scientific process could theoretically start anywhere in Kolb’s cycle. But it will likely start with a problem that needs to be solved, something you don’t understand but would like to know more about. You become aware that there is a problem or that you lack understanding because you have an experience where you observe something and then step back and said, “I wonder how that works,” or perhaps, “why is that broken?” Through observation you develop a sufficiently adequate description of the problem to start doing some research on what is already known.

With a good description of the problem in hand, you can begin to review what is known through the work of others, and think about what might be going on in your situation and how your new understanding can be applied to the problem. This is “reflective observation.” It isn’t just sitting back and thinking in a vacuum. You need raw material for your mind to work on, and that only comes through the tough task of gathering and engaging with the background information. There is a very important quiet phase in this process when you let your mind assemble and sort through ideas until alternatives begin to emerge that might lead to a solution. Talking with others and sharing ideas is an important part of this quiet phase.

Sometimes the alternatives are no- brainers (blown fuse?), and sometimes they’re more creative (residue from the wrong detergent gunking up the water level sensor?). Regardless of their simplicity or complexity, these become hypotheses that need to be tested. The hypothesis-building stage includes both a statement of how something works or why it isn’t working, and predictions about what might happen if the hypothesis is true. In appliance repair, for example, the prediction will likely be that the appliance will function normally. In horticultural molecular biology, it might be that you will see accumulation of a particular type of fatty acid in the cotyledons .

You put the hypothesis to the test by designing an experiment that assesses whether your predictions were right. If the outcome doesn’t match your prediction, you reject the hypothesis (the fuse was ok, so that wasn’t the problem). If the outcome does match your prediction, you tentatively accept the hypothesis pending further observation (when the fuse was replaced the washing machine worked again, so it might have been a blown fuse, but on the other hand maybe it was just because the motor had time to cool down). As with evaluation experimentation, experimental design is important in assuring that the conclusions from the experiment are meaningful and credible.

Experimentation leads to new experiences and an incremental increase in knowledge, and then the cycle begins again.

In summary, discovery experiments:

Focus on uncovering new relationships and solving problems.
Follow scientific method.
Test hypotheses and their predicted outcomes.
Utilize a careful design in order to maintain meaningfulness and credibility.

Of the four types of experiments, only the discovery experiments are core to the process of science in the narrow sense of being a way of acquiring new knowledge. The other three types of experimentation are still important; demonstration and evaluation experiments are valuable for learning and decision-making and for technology development, and exploration experiments are essential for developing testable hypotheses. But discovery experiments are core to science.

Remember: the methodology of effective washing machine repair, when applied to what is unknown about the physical world, is the methodology of science. It’s not esoteric; it’s good appliance repair.

You might argue that, when applied to a broken washing machine, a discovery experiment results in knowledge that is probably already known by those skilled in appliance repair, so it isn’t really new knowledge about how the world works. That’s a fair criticism. Use of the scientific method can result in new knowledge about how the world works, but whether it uncovers new knowledge depends on the object of experimentation.

Review questions

Experimental design

The methods for designing experiments are carefully studied and often discipline-specific. Methods used in molecular biology, for instance, will be somewhat different from those used in chemistry or in field evaluations of horticultural plants. There are, however, some generalizations we can make about good experimental designs.

Emphasize comparisons

Experiments include more than just one treatment. “Treatment” refers to the factor that you are varying in your experiment—for example, different cultivars of tomato, different fertilizers, or different amounts of light. Experimental designs incorporate comparison of treatments. You usually compare the treatments to one another and often to a control, which is either the application of no treatment or the application of a customary or standard level of treatment.

If you grow a particular type of tomato in your garden, and find that it produces tasty fruit, would you declare it to be the best tomato variety you could grow? Certainly not. You couldn’t even say with certainty that it was the best tomato variety you have ever grown (unless it is the only one you have grown). Next year, however, you could grow that tomato as your control, and grow two other varieties that your neighbors like, and compare fruit quality (appearance, flavor, yield, sugar content). You could then say something definitive about the three tomato varieties because you have compared them to each other after growing them next to each other in the same year and environment.

Replicate treatments

The same treatment is applied to more than one “experimental unit”—the object that receives the treatment. In the example above, the tomato plant is the experimental unit, and you would perhaps plant two or three seedlings of each tomato variety rather than just one. Think of a treatment as something like a fertilizer spread on a patch of land. The patch of land is the experimental unit, while the fertilizer is the treatment.

By applying the treatment to more than one experimental unit you can estimate the variation you get when two experimental units are treated the same, and compare this to the variation when experimental units are given different treatments. If the treatments actually differ in their effectiveness, you would expect the variation between experimental units given different treatments to be much greater than the variation between those given the same treatment. This is one of the fundamental ways in which experiments are statistically analyzed and treatments declared significantly different or not.

Randomize treatments

Once you know how many treatments you are going to apply, and how many replications you want, the product of these two quantities (# treatments × # replications) equals the number of experimental units you need. For instance, if you have three fertilizers you want to test, plus a control, you have four treatments. If you want three replications of each treatment, then you 4 treatments x 3 replications = 12 experimental units or patches of land where you will apply the fertilizers. The treatments will be randomly assigned to each experimental unit (patch of land). This is done using a random number table and is not just haphazard picking. Randomization helps minimize any bias you haven’t recognized in advance and controlled for in other ways.

What are two types of control treatments?
Does increasing the number of replications increase the number of treatments or the number of experimental units?
Can you think of an example of how randomization can protect against bias?

Process of scientific inquiry; it builds on what is known by testing hypotheses.

A very valuable method for actively learning the body of scientific knowledge that has been previously discovered and communicated by others; and it is specifically orchestrated for teaching and learning, not for the discovery of new information about the world around us.

Typically used during the development of new technologies to identify the best products for the desired purpose (eg. which pesticides are effective against the target insect, but not harmful to non-target insects), but are not used to discover new knowledge about how the world works so they typically don't advance our understanding of the natural world. Used to pick a winner from among a number of options.

Used to verify or regulate a scientific experiment by conducting a parallel experiment or by comparing with another standard

A plant variety that has been produced in cultivation by selective breeding. The term comes from combining the words 'cultivated' and 'variety'.

Process of planning an experiment to test a hypothesis.

An embryonic leaf in seed-bearing plants, one or more of which are the first leaves to appear from a germinating seed.

When the same treatment is applied to more than one experimental unit.

Act of randomly assigning treatments to experimental units using a random number table or computer-generated randomization to help minimize any bias that has not been recognized in advance and controlled for in other ways.

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Developing a Hypothesis

Two girls exploring plant life in the woods (Christine Glade, iStockphoto)

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Learn what makes a good hypothesis, and how to develop one.

Developing a Scientific Hypothesis

After identifying a testable question , it is important for students to research and or/review what they already know about the scientific principles involved in their experimental inquiries. After that, and before conducting the inquiry through testing and observation, students need to develop a scientific hypothesis .

Is a hypothesis the same as a guess?

The short answer is no! Anyone can make a guess about anything. Guesses are not generally based on knowledge, but rather are rough estimates that people give when they don’t know the answer to a question. A scientific hypothesis, on the other hand, is not only based on prior knowledge and experiences but also on known factual information obtained through research.

Misconception Alert Like making an estimate in math, a hypothesis should be written before doing an inquiry, not after!

Is a hypothesis the same as a prediction?

Again, the answer is no, although the distinction between these two terms is not always clear. A prediction is an estimate or forecast about something that might happen or the way that something will be based on prior knowledge and experience and known facts (e.g., I predict it will rain tomorrow, I predict that my plant will have two seed leaves, etc.).

Shown is a colour photograph of a boat near a beach in the rain. The camera lens is sprinkled with rain drops. These are in sharp focus, in the foreground. Most of the rest of the photograph is out of focus in the background. There is a strip of dark green grass along the bottom of the frame. Above that is a strip of beige sand. The water is shaded from light grey at the bottom to medium grey at the top. A dark green hill rises up behind the water, to the left. The sky is mottled with grey, white and blueish grey clouds. A small, yellow wooden rowboat is in sharp focus. It is moored to the shore with a white rope, next to a round, orange float.

Like a prediction, a hypothesis forecasts what might happen, but a hypothesis goes beyond a prediction. It includes not only what might happen, but why something might happen. In other words, it explains the relationship between variables. The most significant difference between a prediction and an hypothesis is that a hypothesis is intended to lead to a testable investigation, whereas a prediction is not.

To put it in a different way, a prediction is an estimate of an end result (e.g., I predict that the plant will be tall) whereas a hypothesis is a statement that attempts to explain a phenomena by relating cause and effect (e.g., if we give plants more water, then they will grow taller).

Shown is a colour photograph of water falling from a blue watering can onto a pink flowering plant. The frame is filled with green foliage. In the background, out of focus, is a row of pink, flowering plants in boxes along a wooden railing. In the foreground, a gloved hand tips a large, cornflower blue watering can over the first plant.

Misconception Alert Not every inquiry lends itself to the testing of a hypothesis. Many inquiries involve research questions that ask if relationships exist among variables or involve situations where testing is not possible, such as population inquiries, historical inquiries, etc. For example, you could never test a hypothesis about which type of food a given dinosaur preferred to eat!

Shown is a colour photograph of miniature plastic dinosaurs gathered around a piece of broccoli that resembles a tree. A piece of broccoli stands upright in the middle of the photograph. It has a long, pale green stem and a full, dark green floret. It looks like a green tree with branches and tiny leaves. Five toy dinosaurs have been placed around the broccoli so they look like they're snacking it. They are a little bit shorter than the broccoli, so their mouths reach the bushiest parts of it. The dinosaur in the foreground is dark reddish brown with scaly-looking skin and tiny arms. Behind it, a dark green dinosaur with a long neck stretches to the low branches. In the background, a dark brown triceratops looks on. The long neck of a black dinosaur reaches in from the left, to get the higher leaves. On the far left, a bright yellow dinosaur is about to join the meal.

How do you develop a scientific hypothesis?

In order to develop a hypothesis, one should have:

A good testable question
Understanding of the dependent, independent and control variables of interest
Some prior knowledge, such as from observations and research
Thoughts about how the inquiry could be done (the method)

For example, students may begin with the question:

How does the duration of light exposure affect the surface area of tomato plant leaves?

The variables are:

Independent = duration of light
Dependent = surface area of plant leaves
Controlled = water, soil, seed source, etc.

How then do we formulate a hypothesis from this testable question? A good hypothesis tends to follow the format:

If we do/change this

Then this will happen/be observed, because we know this., if these changes are made to a certain independent variable,, then will we observe a change in a specific dependent variable, because of our prior knowledge and research..

In the example above, the students have identified that they are interested in exploring how the duration of light affects plants, perhaps exposing plants from the same batch of seeds to light for different numbers of hours (e.g., one hour, two hours, etc.). Knowing that plants need light to grow (from prior knowledge or research), then they may hypothesize that the leaves of a plant may be larger given a longer exposure to light. Knowing all of this, their hypothesis might be:

If we expose plants to a greater number of hours of light, then the surface area of the tomato plant leaves will be larger because light affects plant growth.

What makes for a good hypothesis.

A good hypothesis is:

A statement The hypothesis is not the same as the testable question. The hypothesis is a tentative explanation of what is thought will happen during the inquiry.
Testable What is changed (independent variable) and what is affected by the change (dependent variable) should be measurable and observable.
Falsifiable A good hypothesis can be either supported or shown to be false by the data collected.
Clear. It should be obvious what will be tested, how it will be tested (what will be measured to prove or disprove the hypothesis), and what is expected to happen.

A good question and hypothesis should also help students find answers that are not obvious to them or generally known. For example, most students will know that if you do not water a plant, it will die, so developing a hypothesis such as:

If we stop watering our plants then the plants will die because plants need water in order to live.

is overly simplistic and will not help students expand their knowledge. A good experimental inquiry will help students discover things they do not already know.

Misconception Alert The goal of a hypothesis is NOT for a student to be “right.” Having evidence that shows a hypothesis to be false is just as important as having evidence that shows it to be true. A hypothesis is NOT something you prove – it is something you test!

How to develop a Tomatosphere™ hypothesis

In the Seed Investigation, a testable question is provided to the students:

How does exposure to the space environment or space-like conditions affect the number of tomato seeds that germinate?

In the variables section, the dependent and independent variables were identified.

Independent variable : Seed treatment – Some seeds have been to space or are exposed to space-like conditions in years when seeds do not go to space, while some seeds have not been to space or exposed to space-like conditions.

Dependent variable : Number of seeds that germinate.

What is not provided to the students is a hypothesis to follow from this question. Using the “if…..then…because….” format, have the students develop their hypotheses for the Tomatosphere™ testable question. For example:

If tomato plant seeds are exposed to the conditions of space, then fewer ‘space’ seeds will germinate than non- ‘space’ seeds because radiation levels found in space may damage cells in the seeds.

This is not the only possible hypothesis, but it shows some understanding of how plants might be affected by space conditions (e.g., radiation affecting DNA in cells, microgravity affecting growth, etc.) which might be derived from prior knowledge or research.

Is this a good hypothesis? Yes

It is a statement.
It is testable. What is changed (being in space or not) and what is affected by the change (number of seeds germinated) can be measured and observed.
It is falsifiable. The student can use the data collected to be able to decide if it supports their hypothesis or if it shows the hypothesis is false (statement is false – more ‘space’ seeds germinate or the germination rate is the same).
It is clear. It should be obvious what will be tested (seed germination), how it will be tested (seeds are grown to the point of germination), and what is expected to happen (fewer space seeds will germinate).

To assist with practicing writing a hypothesis, students could be provided with a checklist, such as this one, also available as a [ Google doc ] and [ PDF ].

Writing a Strong Hypothesis Checklist

Hypothesis is a statement that correctly follows the format:

"If _____ then ______ because _________

Hypothesis relates to the Testable Question

Hypothesis makes sense (based on observations and/or research)

Hypothesis can be falsified

Hypothesis includes a cause and effect relationship

Hypothesis could be tested with measurements

Hypothesis is easy to understand

Guided Practice

Have students read the following statements and determine if these are good, okay, or poor hypotheses and why.

Have students use the Writing a Strong Hypothesis Checklist for creating a Tomatosphere™ or other hypothesis.

Why is this a good hypothesis? ✓ It is a statement that follows the “if….then…because” format. ✓ It is testable. What is changed (red light vs. green light) and what is affected by the change (size of leaves) is measurable and observable. ✓ It is falsifiable. It can be supported by evidence (statement is true – leaves will be bigger, statement is untrue – leaves will be smaller or the same size). ✓ It is clear. It is obvious what will be tested (two colours of light), how it will be tested (at six weeks of age the plant leaves will be measured), and what is expected to happen (plants grown in red light will have bigger leaves). B):

Why is this a poor hypothesis? ✓ It is a statement that follows the “if….then…because” format. ✓ It is not testable. The variables are very vague. What are the classroom conditions compared to the outdoor conditions? Is the interest in soil? Light? Temperature? What kinds of plants will be grown. ✗ It is not falsifiable. It would be difficult to support or falsify with evidence because it is vague. ✗ It is not clear. It is not obvious what will be tested (Soil? Temperature? Light?), how it will be tested and what is expected to happen (what does “better” mean? Taller? Bigger leaves? Flower sooner?). Have students work on changing this vague hypothesis into a more specific one by identifying variables to explore. C):

Why is this just an “okay” hypothesis? ✓ It is a statement that follows the “if….then…because” format. ✗ It is somewhat testable. What is changed (sugar water vs. regular water) is clear, but what is affected by the change (“better”) is vague. Will the plants be taller? Grow faster? ✗ It is not falsifiable. It would be difficult to support or falsify with evidence because the “better” is vague. ✗ It is somewhat clear. It is obvious what will be tested (maple syrup being added to the water) and how it will be tested, but what is expected to happen is not clear (what does “better” mean? Taller? Bigger leaves? Flower sooner?). Have students work on changing this somewhat vague hypothesis into a more specific one by identifying a dependent variable.

A Strong Hypothesis - Science Buddies (2010) This blog post by Science Buddies explains the parts of a good hypothesis, and the role a hypothesis plays in the scientific process.

Theory vs. Hypothesis vs. Law… Explained! (2015) This video (7:11 min.) from PBS Studios Be Smart explains how these words mean something totally different in science than in everyday speech, and how they all help us understand how the universe works.

Misconceptions about Science This page by Understanding Science at UC Berkeley gives a thorough definition of the word hypothesis, in a scientific context, as opposed to everyday language.

What is a Scientific Hypothesis? (2022) This article by Alina Bradford at Live Science discusses what makes a hypothesis testable, the different types of hypotheses, and hypothesis vs. theory.

Writing a Hypothesis (2013) This video (4:58 min.) by mreppsclassroom explains the purpose of a hypothesis and how to construct one.

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4.5.2: Translocation (Assimilate Transport)

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Melissa Ha, Maria Morrow, & Kammy Algiers

Melissa Ha, Maria Morrow, & Kammy Algiers
Yuba College, College of the Redwoods, & Ventura College via ASCCC Open Educational Resources Initiative

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Learning Objectives

Distinguish between sources and sinks and provide examples of each.
Explain the process of phloem loading, distinguishing between apoplastic and symplastic pathways.
Explain how assimilate translocations through the phloem according to the pressure-flow hypothesis.

Plants need an energy source to grow. In seeds and bulbs, food is stored in polymers (such as starch) that are converted by metabolic processes into sucrose for newly developing plants. Once green shoots and leaves are growing, plants are able to produce their own food through photosynthesis. The products of photosynthesis (mainly the sugar sucrose) are a major component of the substance found in the phloem, called assimilate . Ions, amino acids, certain hormones, and other molecules are also found in assimilate. The movement of assimilate is called translocation , or assimilate transport.

Sources and Sinks

Structures that produce or release sugars for the growing plant are referred to as sources . Examples include mature leaves, which produce sugar through photosynthesis, and storage organs, such bulbs, tubers, or storage roots. Sources produce or store more sugars than they need themselves and can thus export sugars. The points of sugar delivery, such as most roots, young shoots, and developing fruits and seeds, are called sinks (F igure \(\PageIndex{1}\)). Because sinks do not produce enough sugars to meet their energy needs, they must import sugars from sources.

A tree with red apples and healthy green leaves. Some of the apples have fallen to the ground.

The pattern of assimilate flow changes as the plant grows and develops. Sugars are directed primarily to the roots early on, to shoots and leaves during vegetative growth, and to seeds and fruits during reproductive development. They are also directed to storage structures. Thus, the same organ may function as a source or a sink depending on the stage of development. For example, a young leaf may initially be a sink, but it will eventually grow and conduct enough photosynthesis to become a source. Similarly, a developing seed is a sink as the embryo develops. Once the seed germinates, however, starches stored in the seed break down, act as a source for the growing seedling structures.

The products from the source are usually translocated to the nearest sink through the phloem. For example, the highest leaves will send sugars upward to the growing shoot tip, whereas lower leaves will direct sugars downward to the roots. Intermediate leaves will send products in both directions, unlike the flow in the xylem, which is always unidirectional (soil to leaf to atmosphere).

Phloem Loading

Before discussing how sources transport sugars into the phloem ( phloem loading ), let's first review the conducting cells of the phloem. Stacks of these cylindrical cells called sieve-tube elements form column-like structures. Each cell is separated by a sieve plate (sieve-tube plate). The sieve plate has holes in it, like a slice of Swiss cheese, allowing for translocation. Lateral sieve areas on the side of the column allow different phloem tubes to interact. Sieve-tube elements have reduced cytoplasmic contents and rely on special parenchyma cells called companion cells, which assist with metabolic activities and provide energy (Figure \(\PageIndex{2}\)). In addition to sieve-tube elements and companion cells, the phloem contains other parenchyma cells and may contain sclerenchyma fibers. In leaves, the phloem is found in vascular bundles (leaf veins), which are surrounded by a bundle sheath. Sugars are produced through photosynthesis in the mesophyll cells that fill most of the leaf (see diagram in Transpiration section).

Illustration of the phloem. Sieve-tube elements are interconnected by sieve plants, forming a tube.

There are two pathways of phloem loading (Figure \(\PageIndex{3}\)). Both pathways begin the same way. The cytoplasms of most plant cells are interconnected via plasmodesmata.Augars through plasmodesmata from mesophyll cells to bundle sheath cells to the parenchyma cells of the phloem. From there, the pathways differ. The first pathway called apoplastic phloem loading , occurs when the regular parenchyma cells of phloem are not connected to the companion cells. In this case, sucrose must exist the regular parenchyma cells and enter the apoplast. It enters the companion cell cytoplasm as well as the inside of the sieve tube through secondary active transport . Protons are pumped into the apoplast (outside of the cells), when the protons re-enter the cells (move down their concentration gradient), they bring sucrose with them by flowing through carrier proteins called sucrose-proton symporters . In this way, sucrose is actively transported against its concentration gradient, and high sucrose concentrations accumulate in the assimilate of the phloem. The second pathway is symplastic phloem loading . When the regular parenchyma cells of the phloem are connected to companion cells via plamodesmata, sucrose can flow through the symplast all the way to the sieve-tube elements. There is no need for sucrose to leave the cytoplasm (enter the apoplast) and re-enter cells via sucrose-proton symport.

The mesophyll cells, bundle sheath cell, phloem parenchyma, companion cell, and sieve-tube element are interconnected with plasmodesmata.

Pressure-Flow Hypothesis

What drives the movement of assimilate in the phloem? According to the pressure-flow hypothesis , assimilate moves due to osmosis of water to and from the xylem. While this is a passive process, it ultimately results from the active transport of sugars during phloem loading and unloading. In contrast with transpiration, an entirely passive process, translocation as a passive process that is indirectly driven by active processes.

Assimilate contains up to 30 percent sugar, and this high solute concentration decreases Ψ s , which decreases the total water potential. The sugar concentration of the assimilate near sinks, where phloem loading occurs, is highest. This causes water to move by osmosis from the adjacent xylem into sieve tubes, thereby increasing pressure. The increase in pressure increases in total water potential causes the bulk flow of phloem from source to sink (Figure \(\PageIndex{4}\)). Sugar concentration in the sink cells is lower than in the sieve-tube elements because the sink sucrose has been metabolized for growth, or converted to starch for storage or other polymers, such as cellulose, for structural integrity. Unloading at the sink end of the phloem tube occurs by either diffusion or active transport of sucrose molecules from an area of high concentration to one of low concentration. Unloading reduces the sugar concentration of assimilate near sinks, increasing water potential. As a result, water moves from the phloem by osmosis and is then transpired or recycled via the xylem back into the phloem sap.

Sucrose moves from source to sink. Changing sucrose concentrations drives osmosis between adjacent xylem vessels and sieve tubes.

Attribution

Curated and authored by Melissa Ha using 30.5 Transport of Water and Solutes in Plants from Biology 2e by OpenStax (licensed CC-BY ). Access for free at openstax.org .

What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By: Derek Jansen (MBA) | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing.

Research Hypothesis 101

What is a hypothesis ?
What is a research hypothesis (scientific hypothesis)?
Requirements for a research hypothesis
Definition of a research hypothesis
The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

A good research hypothesis needs to be very clear about what’s being assessed and very specific about the expected outcome.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference.

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

A good research hypothesis must be testable. In other words, you must able to collect observable data in a scientifically rigorous fashion to test it.

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell.

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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16 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

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Null Hypothesis Definition and Examples

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In a scientific experiment, the null hypothesis is the proposition that there is no effect or no relationship between phenomena or populations. If the null hypothesis is true, any observed difference in phenomena or populations would be due to sampling error (random chance) or experimental error. The null hypothesis is useful because it can be tested and found to be false, which then implies that there is a relationship between the observed data. It may be easier to think of it as a nullifiable hypothesis or one that the researcher seeks to nullify. The null hypothesis is also known as the H 0, or no-difference hypothesis.

The alternate hypothesis, H A or H 1 , proposes that observations are influenced by a non-random factor. In an experiment, the alternate hypothesis suggests that the experimental or independent variable has an effect on the dependent variable .

How to State a Null Hypothesis

There are two ways to state a null hypothesis. One is to state it as a declarative sentence, and the other is to present it as a mathematical statement.

For example, say a researcher suspects that exercise is correlated to weight loss, assuming diet remains unchanged. The average length of time to achieve a certain amount of weight loss is six weeks when a person works out five times a week. The researcher wants to test whether weight loss takes longer to occur if the number of workouts is reduced to three times a week.

The first step to writing the null hypothesis is to find the (alternate) hypothesis. In a word problem like this, you're looking for what you expect to be the outcome of the experiment. In this case, the hypothesis is "I expect weight loss to take longer than six weeks."

This can be written mathematically as: H 1 : μ > 6

In this example, μ is the average.

Now, the null hypothesis is what you expect if this hypothesis does not happen. In this case, if weight loss isn't achieved in greater than six weeks, then it must occur at a time equal to or less than six weeks. This can be written mathematically as:

H 0 : μ ≤ 6

The other way to state the null hypothesis is to make no assumption about the outcome of the experiment. In this case, the null hypothesis is simply that the treatment or change will have no effect on the outcome of the experiment. For this example, it would be that reducing the number of workouts would not affect the time needed to achieve weight loss:

H 0 : μ = 6

Null Hypothesis Examples

"Hyperactivity is unrelated to eating sugar " is an example of a null hypothesis. If the hypothesis is tested and found to be false, using statistics, then a connection between hyperactivity and sugar ingestion may be indicated. A significance test is the most common statistical test used to establish confidence in a null hypothesis.

Another example of a null hypothesis is "Plant growth rate is unaffected by the presence of cadmium in the soil ." A researcher could test the hypothesis by measuring the growth rate of plants grown in a medium lacking cadmium, compared with the growth rate of plants grown in mediums containing different amounts of cadmium. Disproving the null hypothesis would set the groundwork for further research into the effects of different concentrations of the element in soil.

Why Test a Null Hypothesis?

You may be wondering why you would want to test a hypothesis just to find it false. Why not just test an alternate hypothesis and find it true? The short answer is that it is part of the scientific method. In science, propositions are not explicitly "proven." Rather, science uses math to determine the probability that a statement is true or false. It turns out it's much easier to disprove a hypothesis than to positively prove one. Also, while the null hypothesis may be simply stated, there's a good chance the alternate hypothesis is incorrect.

For example, if your null hypothesis is that plant growth is unaffected by duration of sunlight, you could state the alternate hypothesis in several different ways. Some of these statements might be incorrect. You could say plants are harmed by more than 12 hours of sunlight or that plants need at least three hours of sunlight, etc. There are clear exceptions to those alternate hypotheses, so if you test the wrong plants, you could reach the wrong conclusion. The null hypothesis is a general statement that can be used to develop an alternate hypothesis, which may or may not be correct.

Difference Between Independent and Dependent Variables
Examples of Independent and Dependent Variables
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What is Heterosis?

An introduction to heterosis.

Plant breeding is the application of genetic principles in developing new plant varieties, known as cultivar development, crop improvement, and seed improvement. Heterosis in plant breeding is described as the superiority of an F 1 hybrid over both parents in terms of yield or other characteristics. Heterosis contributes to increased vigour, size, growth rate, yield, or other attributes. However, in exceptional cases, the hybrid may be inferior to the weaker parent. The methods of estimation of heterosis and the genetic basis of heterosis are described here.

Heterosis Definition

Heterosis refers to the superiority of F, hybrids over their parents in one or more characteristics. The word hybrid vigour is a synonym for heterosis. George Harrison Shull coined the term heterosis in 1914.

Some features of heterosis are described below.

Superiority Over Parents: Heterosis results in superiority over its parents in adaptability, yield, quality, disease resistance, maturity, and general vigour. Positive heterosis is often seen as desirable. However, in some circumstances, negative heterosis is preferable. Negative heterosis for plant height, maturity time, and hazardous chemicals, for example, is beneficial in many circumstances since it demonstrates superiority over the parents. In most agricultural plants , heterosis of 40% or more over the superior parent is regarded as substantial from a practical standpoint.

Confined to F 1 : Heterosis is restricted to the F 1 , resulting in the production of a cross. As a result of segregation and recombination, it diminishes and vanishes in F 1 and later generations of a cross. As a result, heterosis is linked to the F 1 generation.

Genetic Control: Nuclear genes regulate the expression of heterosis. In certain cases, heterosis is caused by the interaction of nuclear genes and cytoplasm.

Reproducible: Once recognised, heterosis may be easily reproduced in a specific environment. The expression of heterosis, on the other hand, is more evident in the region of hybrid adaptability.

Relationship with SCA: Heterosis shows a positive relationship with specific combining ability (SCA) variation. The SCA is a measure of dominance variance, and having a high degree of dominance variance is required to carry out a heterosis breeding program.

Heterozygosity Effect: The degree of heterosis is related to heterozygosity since dominance variance is related to heterozygosity. The dominance effects should be most significant in cross-pollinated species and least significant in self-pollinated species. As a result, heterosis occurs more frequently in cross-pollinated crops than in self-pollinated crops.

Masks Recessive Genes: When there is heterosis, the beneficial influence of dominant genes masks harmful recessive genes. As a result, recessive mutant genes are concealed in heterozygous individuals.

Low Frequency: The frequency of good heterotic pairings is quite low. Only a few good heterotic pairings are discovered after screening thousands of F 1 crosses. All of the F 1 crosses lack desired heterosis.

Genetic Basis of Heterosis

To explain the mechanism of heterosis, two significant theories have been suggested. The first is the dominance theory, while the second is the overdominance hypothesis . Epistasis is also probably related to heterosis. As a result, there are three potential genetic origins of heterosis, which are:

Overdominance

Dominance Hypothesis

Davenport (1908), Bruce (1910), and Keeble and Pellew (1910) proposed this hypothesis. This is the most commonly accepted explanation for heterosis. According to this theory, heterosis is caused by the superiority of dominant alleles when recessive alleles are harmful. The hybrid shows heterosis because the deleterious recessive genes of one parent are concealed by the dominant genes of the other parent. Both parents have different dominant genes.

Assume one parent's genetic make-up is AABBccdd and the other's is aabbCCDD. A hybrid of these two parents will have four dominant genes, giving it superiority over both parents having two dominant genes. Thus, heterosis is proportional to the number of dominant genes contributed by each parent.

Overdominance Hypothesis

Shull and East separately presented this hypothesis in 1908. This hypothesis is known as stimulation of heterozygosis, cumulative action of divergent alleles, single-gene heterosis, super-dominance, and overdominance. Even though Shull and East proposed this hypothesis in 1908, Hull used the word overdominance in 1945 when working on maize. According to this theory, heterosis is caused by the heterozygote's superiority over both of its homozygous parents. Thus, heterosis is proportional to heterozygosity.

The superiority of the heterozygote over both homozygotes may result from:

The production of a superior hybrid substance in the heterozygote that is entirely different from either of the homozygous.

Greater buffering capacity in the heterozygote due to cumulative action of divergent alleles or stimulation of divergent alleles. East explained this theory in 1936, suggesting a set of alleles a1, a2, a3, and a4 with steadily increasing divergence in function. As a result, a combination of more divergent alleles will have more heterosis than a combination of less divergent alleles. Combinations of a1a4, for example, demonstrate more heterosis than combinations of a1a2, a2a3, and a3a4. Overdominance has been reported in barley.

The interaction of alleles from two or more distinct loci is referred to as epistasis. It is sometimes referred to as nonallelic interaction. Non-allelic interactions are classified into three types: additive x additive, dominance x dominance, and additive x dominance. It is widely documented that the presence and size of non-interaction have a positive relationship with the incidence and magnitude of heterosis. Epistasis, especially dominance effects (dominance x dominance), may lead to heterosis. Cotton and maize have both shown this (Moll and Stuber 1974). Various biometrical models can detect or estimate epistasis.

Methods of Estimation of Heterosis

Heterosis is estimated in three ways:

Over mid parent

Over better parent

Over a commercial hybrid

Thus, based on estimation, heterosis is classified into three types, as shown below.

Average Heterosis: When the heterosis is estimated over the mid parent, i.e., the average value of the two parents, it is known as average heterosis, which is calculated as Average Heterosis= {(F 1 -MP)/MP} X 100

Where F 1 is the mean value of F 1 and MP is the mean value of the two parents involved in the cross.

Heterobeltiosis: It occurs when the heterosis is estimated to be superior or better than the superior or a better parent. It is known as heterobeltiosis. It is calculated as follows:

Heterobeltiosis= {((F 1 -BP)/BP) X 100}

BP is the mean value (across replications) of the cross's better parents.

Useful Heterosis: Meredith and Bridge coined the term useful heterosis in 1972. It is also known as economic heterosis and refers to F 1 's superiority over the normal commercial check type. This sort of heterosis has direct use in plant breeding. It is calculated as follows.

Useful heterosis= {((F 1 -CC)/CC) X 100}

Where CC is the mean value (across replications) of the local commercial hybrid. Over the conventional commercial hybrid, heterosis is sometimes worked out.

Standard Heterosis: Heterosis is estimated in crops where hybrids are already available for comparison. Standard heterosis refers to this sort of heterosis. This is also directly applicable in plant breeding. It is calculated as follows.

{(F 1 -SH)/SH} X 100 = Standard heterosis

Where SH is the mean value of the standard hybrid.

Heterosis refers to the superiority of F 1 hybrids in one or more characteristics over their parents. The term hybrid vigour is used interchangeably with heterosis. Dr. G. H. Shull coined the term "heterosis" in 1914. Heterosis is the process by which a less vigorous organism is turned into a more vigorous organism by absorbing DNA from the media.

FAQs on What is Heterosis?

1. What are the three main types of heterosis?

Individual, maternal, and paternal heterosis are the three types of heterosis. According to Bourdon (2000), retained heterosis is the improvement in the performance of crossbred progeny over purebred parents. Individual heterosis refers to the advantage of the crossbred individual over the purebred average. A Limousin x Hereford calf, for example, may grow faster than a purebred Limousin and Hereford's calf.

Maternal heterosis is defined as a cow's output exceeding the average of her parent breeds, such as in terms of maternal ability, reproduction, longevity, calf survivability, pounds of calf weaned, and younger age at puberty.

Paternal heterosis is the improvement of the bull's productive and reproductive characteristics. Reduced puberty age, increased scrotal circumference, improved sperm concentration, increased pregnancy rate, and weaning rate when mated to cows are examples.

2. What are the differences between heterosis and inbreeding depression?

The primary distinction between heterosis and inbreeding depression is that heterosis is characterized by beneficial augmentations of phenotypic trait values in offspring of genetically distant parents. Inbreeding depression, on the other hand, is characterized by negative reductions in phenotypic trait values in the offspring of genetically related parents. As a result, heterosis results from outbreeding enhancement, whereas inbreeding depression results from inbreeding. Furthermore, heterosis is caused by increased offspring heterozygosity, whereas inbreeding depression is caused by increased offspring homozygosity.

3. What are the uses of heterosis?

Heterosis is typically used to increase vigour, size, growth rate, yield, etc. Uses can be in the following form.

Increased yield

Increased reproductive ability

Increase in size and vigour

Better quality

Greater adaptability

Heterosis is also increased by a rise in the rate of DNA reduplication, transcription, and translation, enzymatic activity, other regulatory systems, and the formation of hybrid protein molecules. However, in exceptional cases, the hybrid may be inferior to the weaker parent.

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Hypothesis is a testable statement that explains what is happening or observed. It proposes the relation between the various participating variables. Hypothesis is also called Theory, Thesis, Guess, Assumption, or Suggestion. Hypothesis creates a structure that guides the search for knowledge.

In this article, we will learn what is hypothesis, its characteristics, types, and examples. We will also learn how hypothesis helps in scientific research.

What is Hypothesis?

A hypothesis is a suggested idea or plan that has little proof, meant to lead to more study. It’s mainly a smart guess or suggested answer to a problem that can be checked through study and trial. In science work, we make guesses called hypotheses to try and figure out what will happen in tests or watching. These are not sure things but rather ideas that can be proved or disproved based on real-life proofs. A good theory is clear and can be tested and found wrong if the proof doesn’t support it.

Hypothesis Meaning

A hypothesis is a proposed statement that is testable and is given for something that happens or observed.

It is made using what we already know and have seen, and it’s the basis for scientific research.
A clear guess tells us what we think will happen in an experiment or study.
It’s a testable clue that can be proven true or wrong with real-life facts and checking it out carefully.
It usually looks like a “if-then” rule, showing the expected cause and effect relationship between what’s being studied.

Characteristics of Hypothesis

Here are some key characteristics of a hypothesis:

Testable: An idea (hypothesis) should be made so it can be tested and proven true through doing experiments or watching. It should show a clear connection between things.
Specific: It needs to be easy and on target, talking about a certain part or connection between things in a study.
Falsifiable: A good guess should be able to show it’s wrong. This means there must be a chance for proof or seeing something that goes against the guess.
Logical and Rational: It should be based on things we know now or have seen, giving a reasonable reason that fits with what we already know.
Predictive: A guess often tells what to expect from an experiment or observation. It gives a guide for what someone might see if the guess is right.
Concise: It should be short and clear, showing the suggested link or explanation simply without extra confusion.
Grounded in Research: A guess is usually made from before studies, ideas or watching things. It comes from a deep understanding of what is already known in that area.
Flexible: A guess helps in the research but it needs to change or fix when new information comes up.
Relevant: It should be related to the question or problem being studied, helping to direct what the research is about.
Empirical: Hypotheses come from observations and can be tested using methods based on real-world experiences.

Sources of Hypothesis

Hypotheses can come from different places based on what you’re studying and the kind of research. Here are some common sources from which hypotheses may originate:

Existing Theories: Often, guesses come from well-known science ideas. These ideas may show connections between things or occurrences that scientists can look into more.
Observation and Experience: Watching something happen or having personal experiences can lead to guesses. We notice odd things or repeat events in everyday life and experiments. This can make us think of guesses called hypotheses.
Previous Research: Using old studies or discoveries can help come up with new ideas. Scientists might try to expand or question current findings, making guesses that further study old results.
Literature Review: Looking at books and research in a subject can help make guesses. Noticing missing parts or mismatches in previous studies might make researchers think up guesses to deal with these spots.
Problem Statement or Research Question: Often, ideas come from questions or problems in the study. Making clear what needs to be looked into can help create ideas that tackle certain parts of the issue.
Analogies or Comparisons: Making comparisons between similar things or finding connections from related areas can lead to theories. Understanding from other fields could create new guesses in a different situation.
Hunches and Speculation: Sometimes, scientists might get a gut feeling or make guesses that help create ideas to test. Though these may not have proof at first, they can be a beginning for looking deeper.
Technology and Innovations: New technology or tools might make guesses by letting us look at things that were hard to study before.
Personal Interest and Curiosity: People’s curiosity and personal interests in a topic can help create guesses. Scientists could make guesses based on their own likes or love for a subject.

Types of Hypothesis

Here are some common types of hypotheses:

Simple Hypothesis

Complex hypothesis, directional hypothesis.

Non-directional Hypothesis

Null Hypothesis (H0)

Alternative hypothesis (h1 or ha), statistical hypothesis, research hypothesis, associative hypothesis, causal hypothesis.

Simple Hypothesis guesses a connection between two things. It says that there is a connection or difference between variables, but it doesn’t tell us which way the relationship goes.

Complex Hypothesis tells us what will happen when more than two things are connected. It looks at how different things interact and may be linked together.

Directional Hypothesis says how one thing is related to another. For example, it guesses that one thing will help or hurt another thing.

Non-Directional Hypothesis

Non-Directional Hypothesis are the one that don’t say how the relationship between things will be. They just say that there is a connection, without telling which way it goes.

Null hypothesis is a statement that says there’s no connection or difference between different things. It implies that any seen impacts are because of luck or random changes in the information.

Alternative Hypothesis is different from the null hypothesis and shows that there’s a big connection or gap between variables. Scientists want to say no to the null hypothesis and choose the alternative one.

Statistical Hypotheis are used in math testing and include making ideas about what groups or bits of them look like. You aim to get information or test certain things using these top-level, common words only.

Research Hypothesis comes from the research question and tells what link is expected between things or factors. It leads the study and chooses where to look more closely.

Associative Hypotheis guesses that there is a link or connection between things without really saying it caused them. It means that when one thing changes, it is connected to another thing changing.

Causal Hypothesis are different from other ideas because they say that one thing causes another. This means there’s a cause and effect relationship between variables involved in the situation. They say that when one thing changes, it directly makes another thing change.

Hypothesis Examples

Following are the examples of hypotheses based on their types:

Simple Hypothesis Example

Studying more can help you do better on tests.
Getting more sun makes people have higher amounts of vitamin D.

Complex Hypothesis Example

How rich you are, how easy it is to get education and healthcare greatly affects the number of years people live.
A new medicine’s success relies on the amount used, how old a person is who takes it and their genes.

Directional Hypothesis Example

Drinking more sweet drinks is linked to a higher body weight score.
Too much stress makes people less productive at work.

Non-directional Hypothesis Example

Drinking caffeine can affect how well you sleep.
People often like different kinds of music based on their gender.
The average test scores of Group A and Group B are not much different.
There is no connection between using a certain fertilizer and how much it helps crops grow.

Alternative Hypothesis (Ha)

Patients on Diet A have much different cholesterol levels than those following Diet B.
Exposure to a certain type of light can change how plants grow compared to normal sunlight.
The average smarts score of kids in a certain school area is 100.
The usual time it takes to finish a job using Method A is the same as with Method B.
Having more kids go to early learning classes helps them do better in school when they get older.
Using specific ways of talking affects how much customers get involved in marketing activities.
Regular exercise helps to lower the chances of heart disease.
Going to school more can help people make more money.
Playing violent video games makes teens more likely to act aggressively.
Less clean air directly impacts breathing health in city populations.

Functions of Hypothesis

Hypotheses have many important jobs in the process of scientific research. Here are the key functions of hypotheses:

Guiding Research: Hypotheses give a clear and exact way for research. They act like guides, showing the predicted connections or results that scientists want to study.
Formulating Research Questions: Research questions often create guesses. They assist in changing big questions into particular, checkable things. They guide what the study should be focused on.
Setting Clear Objectives: Hypotheses set the goals of a study by saying what connections between variables should be found. They set the targets that scientists try to reach with their studies.
Testing Predictions: Theories guess what will happen in experiments or observations. By doing tests in a planned way, scientists can check if what they see matches the guesses made by their ideas.
Providing Structure: Theories give structure to the study process by arranging thoughts and ideas. They aid scientists in thinking about connections between things and plan experiments to match.
Focusing Investigations: Hypotheses help scientists focus on certain parts of their study question by clearly saying what they expect links or results to be. This focus makes the study work better.
Facilitating Communication: Theories help scientists talk to each other effectively. Clearly made guesses help scientists to tell others what they plan, how they will do it and the results expected. This explains things well with colleagues in a wide range of audiences.
Generating Testable Statements: A good guess can be checked, which means it can be looked at carefully or tested by doing experiments. This feature makes sure that guesses add to the real information used in science knowledge.
Promoting Objectivity: Guesses give a clear reason for study that helps guide the process while reducing personal bias. They motivate scientists to use facts and data as proofs or disprovals for their proposed answers.
Driving Scientific Progress: Making, trying out and adjusting ideas is a cycle. Even if a guess is proven right or wrong, the information learned helps to grow knowledge in one specific area.

How Hypothesis help in Scientific Research?

Researchers use hypotheses to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method:

Initiating Investigations: Hypotheses are the beginning of science research. They come from watching, knowing what’s already known or asking questions. This makes scientists make certain explanations that need to be checked with tests.
Formulating Research Questions: Ideas usually come from bigger questions in study. They help scientists make these questions more exact and testable, guiding the study’s main point.
Setting Clear Objectives: Hypotheses set the goals of a study by stating what we think will happen between different things. They set the goals that scientists want to reach by doing their studies.
Designing Experiments and Studies: Assumptions help plan experiments and watchful studies. They assist scientists in knowing what factors to measure, the techniques they will use and gather data for a proposed reason.
Testing Predictions: Ideas guess what will happen in experiments or observations. By checking these guesses carefully, scientists can see if the seen results match up with what was predicted in each hypothesis.
Analysis and Interpretation of Data: Hypotheses give us a way to study and make sense of information. Researchers look at what they found and see if it matches the guesses made in their theories. They decide if the proof backs up or disagrees with these suggested reasons why things are happening as expected.
Encouraging Objectivity: Hypotheses help make things fair by making sure scientists use facts and information to either agree or disagree with their suggested reasons. They lessen personal preferences by needing proof from experience.
Iterative Process: People either agree or disagree with guesses, but they still help the ongoing process of science. Findings from testing ideas make us ask new questions, improve those ideas and do more tests. It keeps going on in the work of science to keep learning things.

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Summary – Hypothesis

A hypothesis is a testable statement serving as an initial explanation for phenomena, based on observations, theories, or existing knowledge. It acts as a guiding light for scientific research, proposing potential relationships between variables that can be empirically tested through experiments and observations. The hypothesis must be specific, testable, falsifiable, and grounded in prior research or observation, laying out a predictive, if-then scenario that details a cause-and-effect relationship. It originates from various sources including existing theories, observations, previous research, and even personal curiosity, leading to different types, such as simple, complex, directional, non-directional, null, and alternative hypotheses, each serving distinct roles in research methodology. The hypothesis not only guides the research process by shaping objectives and designing experiments but also facilitates objective analysis and interpretation of data, ultimately driving scientific progress through a cycle of testing, validation, and refinement.

FAQs on Hypothesis

What is a hypothesis.

A guess is a possible explanation or forecast that can be checked by doing research and experiments.

What are Components of a Hypothesis?

The components of a Hypothesis are Independent Variable, Dependent Variable, Relationship between Variables, Directionality etc.

What makes a Good Hypothesis?

Testability, Falsifiability, Clarity and Precision, Relevance are some parameters that makes a Good Hypothesis

Can a Hypothesis be Proven True?

You cannot prove conclusively that most hypotheses are true because it’s generally impossible to examine all possible cases for exceptions that would disprove them.

How are Hypotheses Tested?

Hypothesis testing is used to assess the plausibility of a hypothesis by using sample data

Can Hypotheses change during Research?

Yes, you can change or improve your ideas based on new information discovered during the research process.

What is the Role of a Hypothesis in Scientific Research?

Hypotheses are used to support scientific research and bring about advancements in knowledge.

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Hypothesis
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What Is Hypothesis?

Characteristics Of Hypothesis

Sources Of Hypothesis

Types Of Hypothesis

Simple Hypothesis

Complex Hypothesis

Directional Hypothesis

Non-directional Hypothesis

Null Hypothesis

Associative and Causal Hypothesis

Examples Of Hypothesis

Functions Of Hypothesis

Importance Of Hypothesis

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Additional references:

1.2 Science and Experimentation

Thinking about science

Scientific inquiry

Putting the scientific method to work

Types of experiments

Demonstration experiments

Evaluation experiments

Exploration experiments

Discovery experiments

Experimental design

Emphasize comparisons

Replicate treatments

Randomize treatments

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Developing a Hypothesis

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Developing a Scientific Hypothesis

Is a hypothesis the same as a guess?

Is a hypothesis the same as a prediction?

How do you develop a scientific hypothesis?

How does the duration of light exposure affect the surface area of tomato plant leaves?

If we do/change this

If we expose plants to a greater number of hours of light, then the surface area of the tomato plant leaves will be larger because light affects plant growth.

If we stop watering our plants then the plants will die because plants need water in order to live.

How to develop a Tomatosphere™ hypothesis

How does exposure to the space environment or space-like conditions affect the number of tomato seeds that germinate?

If tomato plant seeds are exposed to the conditions of space, then fewer ‘space’ seeds will germinate than non- ‘space’ seeds because radiation levels found in space may damage cells in the seeds.

Writing a Strong Hypothesis Checklist

Guided Practice

Related Topics

Margin Size

4.5.2: Translocation (Assimilate Transport)

Learning Objectives

Sources and Sinks

Phloem Loading

Attribution

What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

Research Hypothesis 101

What is a hypothesis?

What is a research hypothesis?

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

Hypothesis Essential #2: Testability (Provability)

Defining A Research Hypothesis

What about the null hypothesis?

Psst... there’s more!

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Null Hypothesis Definition and Examples

How to State a Null Hypothesis