Let’s think about a simple problem that starts with an observation and apply the scientific method to solve the problem. Imagine that one morning when you wake up and flip a the switch to turn on your bedside lamp, the light won’t turn on. That is an observation that also describes a problem: the lights won’t turn on. Of course, you would next ask the question: “Why won’t the light turn on?”
A hypothesis is a suggested explanation that can be tested. A hypothesis is NOT the question you are trying to answer – it is what you think the answer to the question will be and why . Several hypotheses may be proposed as answers to one question. For example, one hypothesis about the question “Why won’t the light turn on?” is “The light won’t turn on because the bulb is burned out.” There are also other possible answers to the question, and therefore other hypotheses may be proposed. A second hypothesis is “The light won’t turn on because the lamp is unplugged” or “The light won’t turn on because the power is out.” A hypothesis should be based on credible background information. A hypothesis is NOT just a guess (not even an educated one), although it can be based on your prior experience (such as in the example where the light won’t turn on). In general, hypotheses in biology should be based on a credible, referenced source of information.
A hypothesis must be testable to ensure that it is valid. For example, a hypothesis that depends on what a dog thinks is not testable, because we can’t tell what a dog thinks. It should also be falsifiable, meaning that it can be disproven by experimental results. An example of an unfalsifiable hypothesis is “Red is a better color than blue.” There is no experiment that might show this statement to be false. To test a hypothesis, a researcher will conduct one or more experiments designed to eliminate one or more of the hypotheses. This is important: a hypothesis can be disproven, or eliminated, but it can never be proven. If an experiment fails to disprove a hypothesis, then that explanation (the hypothesis) is supported as the answer to the question. However, that doesn’t mean that later on, we won’t find a better explanation or design a better experiment that will disprove the first hypothesis and lead to a better one.
A variable is any part of the experiment that can vary or change during the experiment. Typically, an experiment only tests one variable and all the other conditions in the experiment are held constant.
Let’s start with the first hypothesis given above for the light bulb experiment: the bulb is burned out. When testing this hypothesis, the independent variable (the thing that you are testing) would be changing the light bulb and the dependent variable is whether or not the light turns on.
It would be important to hold all the other aspects of the environment constant, for example not messing with the lamp cord or trying to turn the lamp on using a different light switch. If the entire house had lost power during the experiment because a car hit the power pole, that would be a confounding variable.
You may have learned that a hypothesis can be phrased as an “If..then…” statement. Simple hypotheses can be phrased that way (but they must always also include a “because”), but more complicated hypotheses may require several sentences. It is also very easy to get confused by trying to put your hypothesis into this format. Don’t worry about phrasing hypotheses as “if…then” statements – that is almost never done in experiments outside a classroom.
The results of your experiment are the data that you collect as the outcome. In the light experiment, your results are either that the light turns on or the light doesn’t turn on. Based on your results, you can make a conclusion. Your conclusion uses the results to answer your original question.
We can put the experiment with the light that won’t go in into the figure above:
In practice, the scientific method is not as rigid and structured as it might at first appear. Sometimes an experiment leads to conclusions that favor a change in approach; often, an experiment brings entirely new scientific questions to the puzzle. Many times, science does not operate in a linear fashion; instead, scientists continually draw inferences and make generalizations, finding patterns as their research proceeds. Scientific reasoning is more complex than the scientific method alone suggests.
Another important aspect of designing an experiment is the presence of one or more control groups. A control group allows you to make a comparison that is important for interpreting your results. Control groups are samples that help you to determine that differences between your experimental groups are due to your treatment rather than a different variable – they eliminate alternate explanations for your results (including experimental error and experimenter bias). They increase reliability, often through the comparison of control measurements and measurements of the experimental groups. Often, the control group is a sample that is not treated with the independent variable, but is otherwise treated the same way as your experimental sample. This type of control group is treated the same way as the experimental group except it does not get treated with the independent variable. Therefore, if the results of the experimental group differ from the control group, the difference must be due to the change of the independent, rather than some outside factor. It is common in complex experiments (such as those published in scientific journals) to have more control groups than experimental groups.
Question: Which fertilizer will produce the greatest number of tomatoes when applied to the plants?
Hypothesis : If I apply different brands of fertilizer to tomato plants, the most tomatoes will be produced from plants watered with Brand A because Brand A advertises that it produces twice as many tomatoes as other leading brands.
Experiment: Purchase 10 tomato plants of the same type from the same nursery. Pick plants that are similar in size and age. Divide the plants into two groups of 5. Apply Brand A to the first group and Brand B to the second group according to the instructions on the packages. After 10 weeks, count the number of tomatoes on each plant.
Independent Variable: Brand of fertilizer.
Dependent Variable : Number of tomatoes.
Constants: amount of water, type of soil, size of pot, amount of light, type of tomato plant, length of time plants were grown.
Confounding variables : any of the above that are not held constant, plant health, diseases present in the soil or plant before it was purchased.
Results: Tomatoes fertilized with Brand A produced an average of 20 tomatoes per plant, while tomatoes fertilized with Brand B produced an average of 10 tomatoes per plant.
You’d want to use Brand A next time you grow tomatoes, right? But what if I told you that plants grown without fertilizer produced an average of 30 tomatoes per plant! Now what will you use on your tomatoes?
Results including control group : Tomatoes which received no fertilizer produced more tomatoes than either brand of fertilizer.
Conclusion: Although Brand A fertilizer produced more tomatoes than Brand B, neither fertilizer should be used because plants grown without fertilizer produced the most tomatoes!
Text adapted from: OpenStax , Biology. OpenStax CNX. May 27, 2016 http://cnx.org/contents/[email protected]:RD6ERYiU@5/The-Process-of-Science .
MHCC Biology 112: Biology for Health Professions Copyright © 2019 by Lisa Bartee is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.
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Methodology
Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.
A hypothesis is a statement that can be tested by scientific research. If you want to test a relationship between two or more variables, you need to write hypotheses before you start your experiment or data collection .
Daily apple consumption leads to fewer doctor’s visits.
What is a hypothesis, developing a hypothesis (with example), hypothesis examples, other interesting articles, frequently asked questions about writing hypotheses.
A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.
A hypothesis is not just a guess – it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).
Hypotheses propose a relationship between two or more types of variables .
If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias will affect your results.
In this example, the independent variable is exposure to the sun – the assumed cause . The dependent variable is the level of happiness – the assumed effect .
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Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project.
Your initial answer to the question should be based on what is already known about the topic. Look for theories and previous studies to help you form educated assumptions about what your research will find.
At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.
Now you should have some idea of what you expect to find. Write your initial answer to the question in a clear, concise sentence.
You need to make sure your hypothesis is specific and testable. There are various ways of phrasing a hypothesis, but all the terms you use should have clear definitions, and the hypothesis should contain:
To identify the variables, you can write a simple prediction in if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable.
In academic research, hypotheses are more commonly phrased in terms of correlations or effects, where you directly state the predicted relationship between variables.
If you are comparing two groups, the hypothesis can state what difference you expect to find between them.
If your research involves statistical hypothesis testing , you will also have to write a null hypothesis . The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0 , while the alternative hypothesis is H 1 or H a .
Research question | Hypothesis | Null hypothesis |
---|---|---|
What are the health benefits of eating an apple a day? | Increasing apple consumption in over-60s will result in decreasing frequency of doctor’s visits. | Increasing apple consumption in over-60s will have no effect on frequency of doctor’s visits. |
Which airlines have the most delays? | Low-cost airlines are more likely to have delays than premium airlines. | Low-cost and premium airlines are equally likely to have delays. |
Can flexible work arrangements improve job satisfaction? | Employees who have flexible working hours will report greater job satisfaction than employees who work fixed hours. | There is no relationship between working hour flexibility and job satisfaction. |
How effective is high school sex education at reducing teen pregnancies? | Teenagers who received sex education lessons throughout high school will have lower rates of unplanned pregnancy teenagers who did not receive any sex education. | High school sex education has no effect on teen pregnancy rates. |
What effect does daily use of social media have on the attention span of under-16s? | There is a negative between time spent on social media and attention span in under-16s. | There is no relationship between social media use and attention span in under-16s. |
If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.
Statistics
Research bias
A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).
Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.
Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.
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Durga prasanna misra.
1 Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, India.
2 Departments of Rheumatology and Research and Development, Dudley Group NHS Foundation Trust (Teaching Trust of the University of Birmingham, UK), Russells Hall Hospital, Dudley, UK.
3 Department of Internal Medicine #2, Danylo Halytsky Lviv National Medical University, Lviv, Ukraine.
4 Department of Biology and Biochemistry, South Kazakhstan Medical Academy, Shymkent, Kazakhstan.
George d. kitas.
5 Centre for Epidemiology versus Arthritis, University of Manchester, Manchester, UK.
Generating a testable working hypothesis is the first step towards conducting original research. Such research may prove or disprove the proposed hypothesis. Case reports, case series, online surveys and other observational studies, clinical trials, and narrative reviews help to generate hypotheses. Observational and interventional studies help to test hypotheses. A good hypothesis is usually based on previous evidence-based reports. Hypotheses without evidence-based justification and a priori ideas are not received favourably by the scientific community. Original research to test a hypothesis should be carefully planned to ensure appropriate methodology and adequate statistical power. While hypotheses can challenge conventional thinking and may be controversial, they should not be destructive. A hypothesis should be tested by ethically sound experiments with meaningful ethical and clinical implications. The coronavirus disease 2019 pandemic has brought into sharp focus numerous hypotheses, some of which were proven (e.g. effectiveness of corticosteroids in those with hypoxia) while others were disproven (e.g. ineffectiveness of hydroxychloroquine and ivermectin).
Science is the systematized description of natural truths and facts. Routine observations of existing life phenomena lead to the creative thinking and generation of ideas about mechanisms of such phenomena and related human interventions. Such ideas presented in a structured format can be viewed as hypotheses. After generating a hypothesis, it is necessary to test it to prove its validity. Thus, hypothesis can be defined as a proposed mechanism of a naturally occurring event or a proposed outcome of an intervention. 1 , 2
Hypothesis testing requires choosing the most appropriate methodology and adequately powering statistically the study to be able to “prove” or “disprove” it within predetermined and widely accepted levels of certainty. This entails sample size calculation that often takes into account previously published observations and pilot studies. 2 , 3 In the era of digitization, hypothesis generation and testing may benefit from the availability of numerous platforms for data dissemination, social networking, and expert validation. Related expert evaluations may reveal strengths and limitations of proposed ideas at early stages of post-publication promotion, preventing the implementation of unsupported controversial points. 4
Thus, hypothesis generation is an important initial step in the research workflow, reflecting accumulating evidence and experts' stance. In this article, we overview the genesis and importance of scientific hypotheses and their relevance in the era of the coronavirus disease 2019 (COVID-19) pandemic.
Broadly, research can be categorized as primary or secondary. In the context of medicine, primary research may include real-life observations of disease presentations and outcomes. Single case descriptions, which often lead to new ideas and hypotheses, serve as important starting points or justifications for case series and cohort studies. The importance of case descriptions is particularly evident in the context of the COVID-19 pandemic when unique, educational case reports have heralded a new era in clinical medicine. 5
Case series serve similar purpose to single case reports, but are based on a slightly larger quantum of information. Observational studies, including online surveys, describe the existing phenomena at a larger scale, often involving various control groups. Observational studies include variable-scale epidemiological investigations at different time points. Interventional studies detail the results of therapeutic interventions.
Secondary research is based on already published literature and does not directly involve human or animal subjects. Review articles are generated by secondary research. These could be systematic reviews which follow methods akin to primary research but with the unit of study being published papers rather than humans or animals. Systematic reviews have a rigid structure with a mandatory search strategy encompassing multiple databases, systematic screening of search results against pre-defined inclusion and exclusion criteria, critical appraisal of study quality and an optional component of collating results across studies quantitatively to derive summary estimates (meta-analysis). 6 Narrative reviews, on the other hand, have a more flexible structure. Systematic literature searches to minimise bias in selection of articles are highly recommended but not mandatory. 7 Narrative reviews are influenced by the authors' viewpoint who may preferentially analyse selected sets of articles. 8
In relation to primary research, case studies and case series are generally not driven by a working hypothesis. Rather, they serve as a basis to generate a hypothesis. Observational or interventional studies should have a hypothesis for choosing research design and sample size. The results of observational and interventional studies further lead to the generation of new hypotheses, testing of which forms the basis of future studies. Review articles, on the other hand, may not be hypothesis-driven, but form fertile ground to generate future hypotheses for evaluation. Fig. 1 summarizes which type of studies are hypothesis-driven and which lead on to hypothesis generation.
A review of the published literature did not enable the identification of clearly defined standards for working and scientific hypotheses. It is essential to distinguish influential versus not influential hypotheses, evidence-based hypotheses versus a priori statements and ideas, ethical versus unethical, or potentially harmful ideas. The following points are proposed for consideration while generating working and scientific hypotheses. 1 , 2 Table 1 summarizes these points.
Points to be considered while evaluating the validity of hypotheses |
---|
Backed by evidence-based data |
Testable by relevant study designs |
Supported by preliminary (pilot) studies |
Testable by ethical studies |
Maintaining a balance between scientific temper and controversy |
A scientific hypothesis should have a sound basis on previously published literature as well as the scientist's observations. Randomly generated (a priori) hypotheses are unlikely to be proven. A thorough literature search should form the basis of a hypothesis based on published evidence. 7
Unless a scientific hypothesis can be tested, it can neither be proven nor be disproven. Therefore, a scientific hypothesis should be amenable to testing with the available technologies and the present understanding of science.
If a hypothesis is based purely on a novel observation by the scientist in question, it should be grounded on some preliminary studies to support it. For example, if a drug that targets a specific cell population is hypothesized to be useful in a particular disease setting, then there must be some preliminary evidence that the specific cell population plays a role in driving that disease process.
The hypothesis should be testable by experiments that are ethically acceptable. 9 For example, a hypothesis that parachutes reduce mortality from falls from an airplane cannot be tested using a randomized controlled trial. 10 This is because it is obvious that all those jumping from a flying plane without a parachute would likely die. Similarly, the hypothesis that smoking tobacco causes lung cancer cannot be tested by a clinical trial that makes people take up smoking (since there is considerable evidence for the health hazards associated with smoking). Instead, long-term observational studies comparing outcomes in those who smoke and those who do not, as was performed in the landmark epidemiological case control study by Doll and Hill, 11 are more ethical and practical.
Novel findings, including novel hypotheses, particularly those that challenge established norms, are bound to face resistance for their wider acceptance. Such resistance is inevitable until the time such findings are proven with appropriate scientific rigor. However, hypotheses that generate controversy are generally unwelcome. For example, at the time the pandemic of human immunodeficiency virus (HIV) and AIDS was taking foot, there were numerous deniers that refused to believe that HIV caused AIDS. 12 , 13 Similarly, at a time when climate change is causing catastrophic changes to weather patterns worldwide, denial that climate change is occurring and consequent attempts to block climate change are certainly unwelcome. 14 The denialism and misinformation during the COVID-19 pandemic, including unfortunate examples of vaccine hesitancy, are more recent examples of controversial hypotheses not backed by science. 15 , 16 An example of a controversial hypothesis that was a revolutionary scientific breakthrough was the hypothesis put forth by Warren and Marshall that Helicobacter pylori causes peptic ulcers. Initially, the hypothesis that a microorganism could cause gastritis and gastric ulcers faced immense resistance. When the scientists that proposed the hypothesis themselves ingested H. pylori to induce gastritis in themselves, only then could they convince the wider world about their hypothesis. Such was the impact of the hypothesis was that Barry Marshall and Robin Warren were awarded the Nobel Prize in Physiology or Medicine in 2005 for this discovery. 17 , 18
Influential hypotheses are those that have stood the test of time. An archetype of an influential hypothesis is that proposed by Edward Jenner in the eighteenth century that cowpox infection protects against smallpox. While this observation had been reported for nearly a century before this time, it had not been suitably tested and publicised until Jenner conducted his experiments on a young boy by demonstrating protection against smallpox after inoculation with cowpox. 19 These experiments were the basis for widespread smallpox immunization strategies worldwide in the 20th century which resulted in the elimination of smallpox as a human disease today. 20
Other influential hypotheses are those which have been read and cited widely. An example of this is the hygiene hypothesis proposing an inverse relationship between infections in early life and allergies or autoimmunity in adulthood. An analysis reported that this hypothesis had been cited more than 3,000 times on Scopus. 1
The COVID-19 pandemic devastated the world like no other in recent memory. During this period, various hypotheses emerged, understandably so considering the public health emergency situation with innumerable deaths and suffering for humanity. Within weeks of the first reports of COVID-19, aberrant immune system activation was identified as a key driver of organ dysfunction and mortality in this disease. 21 Consequently, numerous drugs that suppress the immune system or abrogate the activation of the immune system were hypothesized to have a role in COVID-19. 22 One of the earliest drugs hypothesized to have a benefit was hydroxychloroquine. Hydroxychloroquine was proposed to interfere with Toll-like receptor activation and consequently ameliorate the aberrant immune system activation leading to pathology in COVID-19. 22 The drug was also hypothesized to have a prophylactic role in preventing infection or disease severity in COVID-19. It was also touted as a wonder drug for the disease by many prominent international figures. However, later studies which were well-designed randomized controlled trials failed to demonstrate any benefit of hydroxychloroquine in COVID-19. 23 , 24 , 25 , 26 Subsequently, azithromycin 27 , 28 and ivermectin 29 were hypothesized as potential therapies for COVID-19, but were not supported by evidence from randomized controlled trials. The role of vitamin D in preventing disease severity was also proposed, but has not been proven definitively until now. 30 , 31 On the other hand, randomized controlled trials identified the evidence supporting dexamethasone 32 and interleukin-6 pathway blockade with tocilizumab as effective therapies for COVID-19 in specific situations such as at the onset of hypoxia. 33 , 34 Clues towards the apparent effectiveness of various drugs against severe acute respiratory syndrome coronavirus 2 in vitro but their ineffectiveness in vivo have recently been identified. Many of these drugs are weak, lipophilic bases and some others induce phospholipidosis which results in apparent in vitro effectiveness due to non-specific off-target effects that are not replicated inside living systems. 35 , 36
Another hypothesis proposed was the association of the routine policy of vaccination with Bacillus Calmette-Guerin (BCG) with lower deaths due to COVID-19. This hypothesis emerged in the middle of 2020 when COVID-19 was still taking foot in many parts of the world. 37 , 38 Subsequently, many countries which had lower deaths at that time point went on to have higher numbers of mortality, comparable to other areas of the world. Furthermore, the hypothesis that BCG vaccination reduced COVID-19 mortality was a classic example of ecological fallacy. Associations between population level events (ecological studies; in this case, BCG vaccination and COVID-19 mortality) cannot be directly extrapolated to the individual level. Furthermore, such associations cannot per se be attributed as causal in nature, and can only serve to generate hypotheses that need to be tested at the individual level. 39
Traditionally, publication after peer review has been considered the gold standard before any new idea finds acceptability amongst the scientific community. Getting a work (including a working or scientific hypothesis) reviewed by experts in the field before experiments are conducted to prove or disprove it helps to refine the idea further as well as improve the experiments planned to test the hypothesis. 40 A route towards this has been the emergence of journals dedicated to publishing hypotheses such as the Central Asian Journal of Medical Hypotheses and Ethics. 41 Another means of publishing hypotheses is through registered research protocols detailing the background, hypothesis, and methodology of a particular study. If such protocols are published after peer review, then the journal commits to publishing the completed study irrespective of whether the study hypothesis is proven or disproven. 42 In the post-pandemic world, online research methods such as online surveys powered via social media channels such as Twitter and Instagram might serve as critical tools to generate as well as to preliminarily test the appropriateness of hypotheses for further evaluation. 43 , 44
Some radical hypotheses might be difficult to publish after traditional peer review. These hypotheses might only be acceptable by the scientific community after they are tested in research studies. Preprints might be a way to disseminate such controversial and ground-breaking hypotheses. 45 However, scientists might prefer to keep their hypotheses confidential for the fear of plagiarism of ideas, avoiding online posting and publishing until they have tested the hypotheses.
Publication of hypotheses is important, however, a balance is required between scientific temper and controversy. Journal editors and reviewers might keep in mind these specific points, summarized in Table 2 and detailed hereafter, while judging the merit of hypotheses for publication. Keeping in mind the ethical principle of primum non nocere, a hypothesis should be published only if it is testable in a manner that is ethically appropriate. 46 Such hypotheses should be grounded in reality and lend themselves to further testing to either prove or disprove them. It must be considered that subsequent experiments to prove or disprove a hypothesis have an equal chance of failing or succeeding, akin to tossing a coin. A pre-conceived belief that a hypothesis is unlikely to be proven correct should not form the basis of rejection of such a hypothesis for publication. In this context, hypotheses generated after a thorough literature search to identify knowledge gaps or based on concrete clinical observations on a considerable number of patients (as opposed to random observations on a few patients) are more likely to be acceptable for publication by peer-reviewed journals. Also, hypotheses should be considered for publication or rejection based on their implications for science at large rather than whether the subsequent experiments to test them end up with results in favour of or against the original hypothesis.
Points to be considered before a hypothesis is acceptable for publication |
---|
Experiments required to test hypotheses should be ethically acceptable as per the World Medical Association declaration on ethics and related statements |
Pilot studies support hypotheses |
Single clinical observations and expert opinion surveys may support hypotheses |
Testing hypotheses requires robust methodology and statistical power |
Hypotheses that challenge established views and concepts require proper evidence-based justification |
Hypotheses form an important part of the scientific literature. The COVID-19 pandemic has reiterated the importance and relevance of hypotheses for dealing with public health emergencies and highlighted the need for evidence-based and ethical hypotheses. A good hypothesis is testable in a relevant study design, backed by preliminary evidence, and has positive ethical and clinical implications. General medical journals might consider publishing hypotheses as a specific article type to enable more rapid advancement of science.
Disclosure: The authors have no potential conflicts of interest to disclose.
Author Contributions:
Now it's time to state your hypothesis . The hypothesis is an educated guess as to what will happen during your experiment.
The hypothesis is often written using the words "IF" and "THEN." For example, " If I do not study, then I will fail the test." The "if' and "then" statements reflect your independent and dependent variables .
The hypothesis should relate back to your original question and must be testable .
A word about variables...
Your experiment will include variables to measure and to explain any cause and effect. Below you will find some useful links describing the different types of variables.
Our editors will review what you’ve submitted and determine whether to revise the article.
scientific hypothesis , an idea that proposes a tentative explanation about a phenomenon or a narrow set of phenomena observed in the natural world. The two primary features of a scientific hypothesis are falsifiability and testability, which are reflected in an “If…then” statement summarizing the idea and in the ability to be supported or refuted through observation and experimentation. The notion of the scientific hypothesis as both falsifiable and testable was advanced in the mid-20th century by Austrian-born British philosopher Karl Popper .
The formulation and testing of a hypothesis is part of the scientific method , the approach scientists use when attempting to understand and test ideas about natural phenomena. The generation of a hypothesis frequently is described as a creative process and is based on existing scientific knowledge, intuition , or experience. Therefore, although scientific hypotheses commonly are described as educated guesses, they actually are more informed than a guess. In addition, scientists generally strive to develop simple hypotheses, since these are easier to test relative to hypotheses that involve many different variables and potential outcomes. Such complex hypotheses may be developed as scientific models ( see scientific modeling ).
Depending on the results of scientific evaluation, a hypothesis typically is either rejected as false or accepted as true. However, because a hypothesis inherently is falsifiable, even hypotheses supported by scientific evidence and accepted as true are susceptible to rejection later, when new evidence has become available. In some instances, rather than rejecting a hypothesis because it has been falsified by new evidence, scientists simply adapt the existing idea to accommodate the new information. In this sense a hypothesis is never incorrect but only incomplete.
The investigation of scientific hypotheses is an important component in the development of scientific theory . Hence, hypotheses differ fundamentally from theories; whereas the former is a specific tentative explanation and serves as the main tool by which scientists gather data, the latter is a broad general explanation that incorporates data from many different scientific investigations undertaken to explore hypotheses.
Countless hypotheses have been developed and tested throughout the history of science . Several examples include the idea that living organisms develop from nonliving matter, which formed the basis of spontaneous generation , a hypothesis that ultimately was disproved (first in 1668, with the experiments of Italian physician Francesco Redi , and later in 1859, with the experiments of French chemist and microbiologist Louis Pasteur ); the concept proposed in the late 19th century that microorganisms cause certain diseases (now known as germ theory ); and the notion that oceanic crust forms along submarine mountain zones and spreads laterally away from them ( seafloor spreading hypothesis ).
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A hypothesis is defined as a testable prediction , and is used primarily in scientific experiments as a potential or predicted outcome that scientists attempt to prove or disprove (Atkinson et al., 2021; Tan, 2022).
In my types of hypothesis article, I outlined 13 different hypotheses, including the directional hypothesis (which makes a prediction about an effect of a treatment will be positive or negative) and the associative hypothesis (which makes a prediction about the association between two variables).
This article will dive into some interesting examples of hypotheses and examine potential ways you might test each one.
1. “inadequate sleep decreases memory retention”.
Field: Psychology
Type: Causal Hypothesis A causal hypothesis explores the effect of one variable on another. This example posits that a lack of adequate sleep causes decreased memory retention. In other words, if you are not getting enough sleep, your ability to remember and recall information may suffer.
How to Test:
To test this hypothesis, you might devise an experiment whereby your participants are divided into two groups: one receives an average of 8 hours of sleep per night for a week, while the other gets less than the recommended sleep amount.
During this time, all participants would daily study and recall new, specific information. You’d then measure memory retention of this information for both groups using standard memory tests and compare the results.
Should the group with less sleep have statistically significant poorer memory scores, the hypothesis would be supported.
Ensuring the integrity of the experiment requires taking into account factors such as individual health differences, stress levels, and daily nutrition.
Relevant Study: Sleep loss, learning capacity and academic performance (Curcio, Ferrara & De Gennaro, 2006)
Field: Physics
Type: Deductive Hypothesis The deductive hypothesis applies the logic of deductive reasoning – it moves from a general premise to a more specific conclusion. This specific hypothesis assumes that as temperature increases, the kinetic energy of particles also increases – that is, when you heat something up, its particles move around more rapidly.
This hypothesis could be examined by heating a gas in a controlled environment and capturing the movement of its particles as a function of temperature.
You’d gradually increase the temperature and measure the kinetic energy of the gas particles with each increment. If the kinetic energy consistently rises with the temperature, your hypothesis gets supporting evidence.
Variables such as pressure and volume of the gas would need to be held constant to ensure validity of results.
Field: Psychology/Linguistics
Type: Comparative Hypothesis The comparative hypothesis posits a difference between two or more groups based on certain variables. In this context, you might propose that children raised in bilingual homes have superior cognitive skills compared to those raised in monolingual homes.
Testing this hypothesis could involve identifying two groups of children: those raised in bilingual homes, and those raised in monolingual homes.
Cognitive skills in both groups would be evaluated using a standard cognitive ability test at different stages of development. The examination would be repeated over a significant time period for consistency.
If the group raised in bilingual homes persistently scores higher than the other, the hypothesis would thereby be supported.
The challenge for the researcher would be controlling for other variables that could impact cognitive development, such as socio-economic status, education level of parents, and parenting styles.
Relevant Study: The cognitive benefits of being bilingual (Marian & Shook, 2012)
Field: Medicine/Nutrition
Type: Alternative Hypothesis The alternative hypothesis suggests an alternative to a null hypothesis. In this context, the implied null hypothesis could be that diet has no effect on cardiovascular health, which the alternative hypothesis contradicts by suggesting that a high-fiber diet leads to fewer instances of cardiovascular diseases.
To test this hypothesis, a longitudinal study could be conducted on two groups of participants; one adheres to a high-fiber diet, while the other follows a diet low in fiber.
After a fixed period, the cardiovascular health of participants in both groups could be analyzed and compared. If the group following a high-fiber diet has a lower number of recorded cases of cardiovascular diseases, it would provide evidence supporting the hypothesis.
Control measures should be implemented to exclude the influence of other lifestyle and genetic factors that contribute to cardiovascular health.
Relevant Study: Dietary fiber, inflammation, and cardiovascular disease (King, 2005)
Field: Agronomy / Botany
Type: Explanatory Hypothesis An explanatory hypothesis attempts to explain a phenomenon. In this case, the hypothesis proposes that gravity affects how plants direct their growth – both above-ground (toward sunlight) and below-ground (towards water and other resources).
The testing could be conducted by growing plants in a rotating cylinder to create artificial gravity.
Observations on the direction of growth, over a specified period, can provide insights into the influencing factors. If plants consistently direct their growth in a manner that indicates the influence of gravitational pull, the hypothesis is substantiated.
It is crucial to ensure that other growth-influencing factors, such as light and water, are uniformly distributed so that only gravity influences the directional growth.
Field: Education
Type: Relational Hypothesis The relational hypothesis describes the relation between two variables. Here, the hypothesis is that the implementation of gamified learning has a positive effect on the motivation of students.
To validate this proposition, two sets of classes could be compared: one that implements a learning approach with game-based elements, and another that follows a traditional learning approach.
The students’ motivation levels could be gauged by monitoring their engagement, performance, and feedback over a considerable timeframe.
If the students engaged in the gamified learning context present higher levels of motivation and achievement, the hypothesis would be supported.
Control measures ought to be put into place to account for individual differences, including prior knowledge and attitudes towards learning.
Relevant Study: Does educational gamification improve students’ motivation? (Chapman & Rich, 2018)
Field: Educational Psychology
Type: Research Hypothesis The research hypothesis involves making a prediction that will be tested. In this case, the hypothesis proposes that a student’s anxiety about math can negatively influence their performance in math-related tasks.
To assess this hypothesis, researchers must first measure the mathematics anxiety levels of a sample of students using a validated instrument, such as the Mathematics Anxiety Rating Scale.
Then, the students’ performance in mathematics would be evaluated through standard testing. If there’s a negative correlation between the levels of math anxiety and math performance (meaning as anxiety increases, performance decreases), the hypothesis would be supported.
It would be crucial to control for relevant factors such as overall academic performance and previous mathematical achievement.
Field: Organizational Psychology
Type: Operational Hypothesis The operational hypothesis involves defining the variables in measurable terms. In this example, the hypothesis posits that disrupting the natural sleep cycle, for instance through shift work or irregular working hours, can lessen productivity among workers.
To test this hypothesis, you could collect data from workers who maintain regular working hours and those with irregular schedules.
Measuring productivity could involve examining the worker’s ability to complete tasks, the quality of their work, and their efficiency.
If workers with interrupted sleep cycles demonstrate lower productivity compared to those with regular sleep patterns, it would lend support to the hypothesis.
Consideration should be given to potential confounding variables such as job type, worker age, and overall health.
Field: Health Psychology
Type: Predictive Hypothesis A predictive hypothesis involves making a prediction about the outcome of a study based on the observed relationship between variables. In this case, it is hypothesized that individuals who engage in regular physical activity are less likely to suffer from depression.
Longitudinal studies would suit to test this hypothesis, tracking participants’ levels of physical activity and their mental health status over time.
The level of physical activity could be self-reported or monitored, while mental health status could be assessed using standard diagnostic tools or surveys.
If data analysis shows that participants maintaining regular physical activity have a lower incidence of depression, this would endorse the hypothesis.
However, care should be taken to control other lifestyle and behavioral factors that could intervene with the results.
Relevant Study: Regular physical exercise and its association with depression (Kim, 2022)
Type: Empirical Hypothesis In the empirical hypothesis, predictions are based on amassed empirical evidence . This particular hypothesis theorizes that frequent meditation leads to improved emotional stability, resonating with numerous studies linking meditation to a variety of psychological benefits.
Earlier studies reported some correlations, but to test this hypothesis directly, you’d organize an experiment where one group meditates regularly over a set period while a control group doesn’t.
Both groups’ emotional stability levels would be measured at the start and end of the experiment using a validated emotional stability assessment.
If regular meditators display noticeable improvements in emotional stability compared to the control group, the hypothesis gains credit.
You’d have to ensure a similar emotional baseline for all participants at the start to avoid skewed results.
Type: Directional Hypothesis The directional hypothesis predicts the direction of an expected relationship between variables. Here, the hypothesis anticipates that early exposure to reading positively affects a child’s academic advancement.
A longitudinal study tracking children’s reading habits from an early age and their consequent academic performance could validate this hypothesis.
Parents could report their children’s exposure to reading at home, while standardized school exam results would provide a measure of academic achievement.
If the children exposed to early reading consistently perform better acadically, it gives weight to the hypothesis.
However, it would be important to control for variables that might impact academic performance, such as socioeconomic background, parental education level, and school quality.
Field: Environmental Science
Type: Descriptive Hypothesis A descriptive hypothesis predicts the existence of an association or pattern related to variables. In this scenario, the hypothesis suggests that industries adopting energy-efficient technologies will resultantly show a reduced carbon footprint.
Global industries making use of energy-efficient technologies could track their carbon emissions over time. At the same time, others not implementing such technologies continue their regular tracking.
After a defined time, the carbon emission data of both groups could be compared. If industries that adopted energy-efficient technologies demonstrate a notable reduction in their carbon footprints, the hypothesis would hold strong.
In the experiment, you would exclude variations brought by factors such as industry type, size, and location.
Type: Simple Hypothesis The simple hypothesis is a prediction about the relationship between two variables, excluding any other variables from consideration. This example posits that by reducing time spent on devices like smartphones and computers, an individual should experience improved sleep quality.
A sample group would need to reduce their daily screen time for a pre-determined period. Sleep quality before and after the reduction could be measured using self-report sleep diaries and objective measures like actigraphy, monitoring movement and wakefulness during sleep.
If the data shows that sleep quality improved post the screen time reduction, the hypothesis would be validated.
Other aspects affecting sleep quality, like caffeine intake, should be controlled during the experiment.
Relevant Study: Screen time use impacts low‐income preschool children’s sleep quality, tiredness, and ability to fall asleep (Waller et al., 2021)
Field: Gerontology
Type: Inductive Hypothesis Inductive hypotheses are based on observations leading to broader generalizations and theories. In this context, the hypothesis deduces from observed instances that engaging in brain-training games can help improve cognitive functioning in the elderly.
A longitudinal study could be conducted where an experimental group of elderly people partakes in regular brain-training games.
Their cognitive functioning could be assessed at the start of the study and at regular intervals using standard neuropsychological tests.
If the group engaging in brain-training games shows better cognitive functioning scores over time compared to a control group not playing these games, the hypothesis would be supported.
Type: Null Hypothesis A null hypothesis is a negative statement assuming no relationship or difference between variables. The hypothesis in this context asserts there’s no effect of different farming practices on the rates of soil erosion.
Comparing soil erosion rates in areas with different farming practices over a considerable timeframe could help test this hypothesis.
If, statistically, the farming practices do not lead to differences in soil erosion rates, the null hypothesis is accepted.
However, if marked variation appears, the null hypothesis is rejected, meaning farming practices do influence soil erosion rates. It would be crucial to control for external factors like weather, soil type, and natural vegetation.
The variety of hypotheses mentioned above underscores the diversity of research constructs inherent in different fields, each with its unique purpose and way of testing.
While researchers may develop hypotheses primarily as tools to define and narrow the focus of the study, these hypotheses also serve as valuable guiding forces for the data collection and analysis procedures, making the research process more efficient and direction-focused.
Hypotheses serve as a compass for any form of academic research. The diverse examples provided, from Psychology to Educational Studies, Environmental Science to Gerontology, clearly demonstrate how certain hypotheses suit specific fields more aptly than others.
It is important to underline that although these varied hypotheses differ in their structure and methods of testing, each endorses the fundamental value of empiricism in research. Evidence-based decision making remains at the heart of scholarly inquiry, regardless of the research field, thus aligning all hypotheses to the core purpose of scientific investigation.
Testing hypotheses is an essential part of the scientific method . By doing so, researchers can either confirm their predictions, giving further validity to an existing theory, or they might uncover new insights that could potentially shift the field’s understanding of a particular phenomenon. In either case, hypotheses serve as the stepping stones for scientific exploration and discovery.
Atkinson, P., Delamont, S., Cernat, A., Sakshaug, J. W., & Williams, R. A. (2021). SAGE research methods foundations . SAGE Publications Ltd.
Curcio, G., Ferrara, M., & De Gennaro, L. (2006). Sleep loss, learning capacity and academic performance. Sleep medicine reviews , 10 (5), 323-337.
Kim, J. H. (2022). Regular physical exercise and its association with depression: A population-based study short title: Exercise and depression. Psychiatry Research , 309 , 114406.
King, D. E. (2005). Dietary fiber, inflammation, and cardiovascular disease. Molecular nutrition & food research , 49 (6), 594-600.
Marian, V., & Shook, A. (2012, September). The cognitive benefits of being bilingual. In Cerebrum: the Dana forum on brain science (Vol. 2012). Dana Foundation.
Tan, W. C. K. (2022). Research Methods: A Practical Guide For Students And Researchers (Second Edition) . World Scientific Publishing Company.
Waller, N. A., Zhang, N., Cocci, A. H., D’Agostino, C., Wesolek‐Greenson, S., Wheelock, K., … & Resnicow, K. (2021). Screen time use impacts low‐income preschool children’s sleep quality, tiredness, and ability to fall asleep. Child: care, health and development, 47 (5), 618-626.
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Learning objectives.
By the end of this section, you will be able to do the following:
What is biology? In simple terms, biology is the study of life. This is a very broad definition because the scope of biology is vast. Biologists may study anything from the microscopic or submicroscopic view of a cell to ecosystems and the whole living planet ( Figure 1.2 ). Listening to the daily news, you will quickly realize how many aspects of biology we discuss every day. For example, recent news topics include Escherichia coli ( Figure 1.3 ) outbreaks in spinach and Salmonella contamination in peanut butter. Other subjects include efforts toward finding a cure for AIDS, Alzheimer’s disease, and cancer. On a global scale, many researchers are committed to finding ways to protect the planet, solve environmental issues, and reduce the effects of climate change. All of these diverse endeavors are related to different facets of the discipline of biology.
Biology is a science, but what exactly is science? What does the study of biology share with other scientific disciplines? We can define science (from the Latin scientia , meaning “knowledge”) as knowledge that covers general truths or the operation of general laws, especially when acquired and tested by the scientific method. It becomes clear from this definition that applying scientific method plays a major role in science. The scientific method is a method of research with defined steps that include experiments and careful observation.
We will examine scientific method steps in detail later, but one of the most important aspects of this method is the testing of hypotheses by means of repeatable experiments. A hypothesis is a suggested explanation for an event, which one can test. Although using the scientific method is inherent to science, it is inadequate in determining what science is. This is because it is relatively easy to apply the scientific method to disciplines such as physics and chemistry, but when it comes to disciplines like archaeology, psychology, and geology, the scientific method becomes less applicable as repeating experiments becomes more difficult.
These areas of study are still sciences, however. Consider archaeology—even though one cannot perform repeatable experiments, hypotheses may still be supported. For instance, archaeologists can hypothesize that an ancient culture existed based on finding a piece of pottery. They could make further hypotheses about various characteristics of this culture, which could be correct or false through continued support or contradictions from other findings. A hypothesis may become a verified theory. A theory is a tested and confirmed explanation for observations or phenomena. Therefore, we may be better off to define science as fields of study that attempt to comprehend the nature of the universe.
What would you expect to see in a museum of natural sciences? Frogs? Plants? Dinosaur skeletons? Exhibits about how the brain functions? A planetarium? Gems and minerals? Maybe all of the above? Science includes such diverse fields as astronomy, biology, computer sciences, geology, logic, physics, chemistry, and mathematics ( Figure 1.4 ). However, scientists consider those fields of science related to the physical world and its phenomena and processes natural sciences . Thus, a museum of natural sciences might contain any of the items listed above.
There is no complete agreement when it comes to defining what the natural sciences include, however. For some experts, the natural sciences are astronomy, biology, chemistry, earth science, and physics. Other scholars choose to divide natural sciences into life sciences , which study living things and include biology, and physical sciences , which study nonliving matter and include astronomy, geology, physics, and chemistry. Some disciplines such as biophysics and biochemistry build on both life and physical sciences and are interdisciplinary. Some refer to natural sciences as “hard science” because they rely on the use of quantitative data. Social sciences that study society and human behavior are more likely to use qualitative assessments to drive investigations and findings.
Not surprisingly, the natural science of biology has many branches or subdisciplines. Cell biologists study cell structure and function, while biologists who study anatomy investigate the structure of an entire organism. Those biologists studying physiology, however, focus on the internal functioning of an organism. Some areas of biology focus on only particular types of living things. For example, botanists explore plants, while zoologists specialize in animals.
One thing is common to all forms of science: an ultimate goal “to know.” Curiosity and inquiry are the driving forces for the development of science. Scientists seek to understand the world and the way it operates. To do this, they use two methods of logical thinking: inductive reasoning and deductive reasoning.
Inductive reasoning is a form of logical thinking that uses related observations to arrive at a general conclusion. This type of reasoning is common in descriptive science. A life scientist such as a biologist makes observations and records them. These data can be qualitative or quantitative, and one can supplement the raw data with drawings, pictures, photos, or videos. From many observations, the scientist can infer conclusions (inductions) based on evidence. Inductive reasoning involves formulating generalizations inferred from careful observation and analyzing a large amount of data. Brain studies provide an example. In this type of research, scientists observe many live brains while people are engaged in a specific activity, such as viewing images of food. The scientist then predicts the part of the brain that “lights up” during this activity to be the part controlling the response to the selected stimulus, in this case, images of food. Excess absorption of radioactive sugar derivatives by active areas of the brain causes the various areas to "light up". Scientists use a scanner to observe the resultant increase in radioactivity. Then, researchers can stimulate that part of the brain to see if similar responses result.
Deductive reasoning or deduction is the type of logic used in hypothesis-based science. In deductive reasoning, the pattern of thinking moves in the opposite direction as compared to inductive reasoning. Deductive reasoning is a form of logical thinking that uses a general principle or law to predict specific results. From those general principles, a scientist can deduce and predict the specific results that would be valid as long as the general principles are valid. Studies in climate change can illustrate this type of reasoning. For example, scientists may predict that if the climate becomes warmer in a particular region, then the distribution of plants and animals should change.
Both types of logical thinking are related to the two main pathways of scientific study: descriptive science and hypothesis-based science. Descriptive (or discovery) science , which is usually inductive, aims to observe, explore, and discover, while hypothesis-based science , which is usually deductive, begins with a specific question or problem and a potential answer or solution that one can test. The boundary between these two forms of study is often blurred, and most scientific endeavors combine both approaches. The fuzzy boundary becomes apparent when thinking about how easily observation can lead to specific questions. For example, a gentleman in the 1940s observed that the burr seeds that stuck to his clothes and his dog’s fur had a tiny hook structure. On closer inspection, he discovered that the burrs’ gripping device was more reliable than a zipper. He eventually experimented to find the best material that acted similarly, and produced the hook-and-loop fastener popularly known today as Velcro. Descriptive science and hypothesis-based science are in continuous dialogue.
Biologists study the living world by posing questions about it and seeking science-based responses. Known as scientific method, this approach is common to other sciences as well. The scientific method was used even in ancient times, but England’s Sir Francis Bacon (1561–1626) first documented it ( Figure 1.5 ). He set up inductive methods for scientific inquiry. The scientific method is not used only by biologists; researchers from almost all fields of study can apply it as a logical, rational problem-solving method.
The scientific process typically starts with an observation (often a problem to solve) that leads to a question. Let’s think about a simple problem that starts with an observation and apply the scientific method to solve the problem. One Monday morning, a student arrives at class and quickly discovers that the classroom is too warm. That is an observation that also describes a problem: the classroom is too warm. The student then asks a question: “Why is the classroom so warm?”
Recall that a hypothesis is a suggested explanation that one can test. To solve a problem, one can propose several hypotheses. For example, one hypothesis might be, “The classroom is warm because no one turned on the air conditioning.” However, there could be other responses to the question, and therefore one may propose other hypotheses. A second hypothesis might be, “The classroom is warm because there is a power failure, and so the air conditioning doesn’t work.”
Once one has selected a hypothesis, the student can make a prediction. A prediction is similar to a hypothesis but it typically has the format “If . . . then . . . .” For example, the prediction for the first hypothesis might be, “ If the student turns on the air conditioning, then the classroom will no longer be too warm.”
A valid hypothesis must be testable. It should also be falsifiable , meaning that experimental results can disprove it. Importantly, science does not claim to “prove” anything because scientific understandings are always subject to modification with further information. This step—openness to disproving ideas—is what distinguishes sciences from non-sciences. The presence of the supernatural, for instance, is neither testable nor falsifiable. To test a hypothesis, a researcher will conduct one or more experiments designed to eliminate one or more of the hypotheses. Each experiment will have one or more variables and one or more controls. A variable is any part of the experiment that can vary or change during the experiment. The control group contains every feature of the experimental group except it is not given the manipulation that the researcher hypothesizes. Therefore, if the experimental group's results differ from the control group, the difference must be due to the hypothesized manipulation, rather than some outside factor. Look for the variables and controls in the examples that follow. To test the first hypothesis, the student would find out if the air conditioning is on. If the air conditioning is turned on but does not work, there should be another reason, and the student should reject this hypothesis. To test the second hypothesis, the student could check if the lights in the classroom are functional. If so, there is no power failure and the student should reject this hypothesis. The students should test each hypothesis by carrying out appropriate experiments. Be aware that rejecting one hypothesis does not determine whether or not one can accept the other hypotheses. It simply eliminates one hypothesis that is not valid ( Figure 1.6 ). Using the scientific method, the student rejects the hypotheses that are inconsistent with experimental data.
While this “warm classroom” example is based on observational results, other hypotheses and experiments might have clearer controls. For instance, a student might attend class on Monday and realize she had difficulty concentrating on the lecture. One observation to explain this occurrence might be, “When I eat breakfast before class, I am better able to pay attention.” The student could then design an experiment with a control to test this hypothesis.
In hypothesis-based science, researchers predict specific results from a general premise. We call this type of reasoning deductive reasoning: deduction proceeds from the general to the particular. However, the reverse of the process is also possible: sometimes, scientists reach a general conclusion from a number of specific observations. We call this type of reasoning inductive reasoning, and it proceeds from the particular to the general. Researchers often use inductive and deductive reasoning in tandem to advance scientific knowledge ( Figure 1.7 ). In recent years a new approach of testing hypotheses has developed as a result of an exponential growth of data deposited in various databases. Using computer algorithms and statistical analyses of data in databases, a new field of so-called "data research" (also referred to as "in silico" research) provides new methods of data analyses and their interpretation. This will increase the demand for specialists in both biology and computer science, a promising career opportunity.
In the example below, the scientific method is used to solve an everyday problem. Match the scientific method steps (numbered items) with the process of solving the everyday problem (lettered items). Based on the results of the experiment, is the hypothesis correct? If it is incorrect, propose some alternative hypotheses.
1. Observation | a. There is something wrong with the electrical outlet. |
2. Question | b. If something is wrong with the outlet, my coffeemaker also won’t work when plugged into it. |
3. Hypothesis (answer) | c. My toaster doesn’t toast my bread. |
4. Prediction | d. I plug my coffee maker into the outlet. |
5. Experiment | e. My coffeemaker works. |
6. Result | f. Why doesn’t my toaster work? |
Decide if each of the following is an example of inductive or deductive reasoning.
The scientific method may seem too rigid and structured. It is important to keep in mind that, although scientists often follow this sequence, there is flexibility. Sometimes an experiment leads to conclusions that favor a change in approach. Often, an experiment brings entirely new scientific questions to the puzzle. Many times, science does not operate in a linear fashion. Instead, scientists continually draw inferences and make generalizations, finding patterns as their research proceeds. Scientific reasoning is more complex than the scientific method alone suggests. Notice, too, that we can apply the scientific method to solving problems that aren’t necessarily scientific in nature.
The scientific community has been debating for the last few decades about the value of different types of science. Is it valuable to pursue science for the sake of simply gaining knowledge, or does scientific knowledge only have worth if we can apply it to solving a specific problem or to bettering our lives? This question focuses on the differences between two types of science: basic science and applied science.
Basic science or “pure” science seeks to expand knowledge regardless of the short-term application of that knowledge. It is not focused on developing a product or a service of immediate public or commercial value. The immediate goal of basic science is knowledge for knowledge’s sake, although this does not mean that, in the end, it may not result in a practical application.
In contrast, applied science or “technology,” aims to use science to solve real-world problems, making it possible, for example, to improve a crop yield, find a cure for a particular disease, or save animals threatened by a natural disaster ( Figure 1.8 ). In applied science, the problem is usually defined for the researcher.
Some individuals may perceive applied science as “useful” and basic science as “useless.” A question these people might pose to a scientist advocating knowledge acquisition would be, “What for?” However, a careful look at the history of science reveals that basic knowledge has resulted in many remarkable applications of great value. Many scientists think that a basic understanding of science is necessary before researchers develop an application, therefore, applied science relies on the results that researchers generate through basic science. Other scientists think that it is time to move on from basic science in order to find solutions to actual problems. Both approaches are valid. It is true that there are problems that demand immediate attention; however, scientists would find few solutions without the help of the wide knowledge foundation that basic science generates.
One example of how basic and applied science can work together to solve practical problems occurred after the discovery of DNA structure led to an understanding of the molecular mechanisms governing DNA replication. DNA strands, unique in every human, are in our cells, where they provide the instructions necessary for life. When DNA replicates, it produces new copies of itself, shortly before a cell divides. Understanding DNA replication mechanisms enabled scientists to develop laboratory techniques that researchers now use to identify genetic diseases, pinpoint individuals who were at a crime scene, and determine paternity. Without basic science, it is unlikely that applied science could exist.
Another example of the link between basic and applied research is the Human Genome Project, a study in which researchers analyzed and mapped each human chromosome to determine the precise sequence of DNA subunits and each gene's exact location. (The gene is the basic unit of heredity represented by a specific DNA segment that codes for a functional molecule. An individual’s complete collection of genes is their genome.) Researchers have studied other less complex organisms as part of this project in order to gain a better understanding of human chromosomes. The Human Genome Project ( Figure 1.9 ) relied on basic research with simple organisms and, later, with the human genome. An important end goal eventually became using the data for applied research, seeking cures and early diagnoses for genetically related diseases.
While scientists usually carefully plan research efforts in both basic science and applied science, note that some discoveries are made by serendipity , that is, by means of a fortunate accident or a lucky surprise. Scottish biologist Alexander Fleming discovered penicillin when he accidentally left a petri dish of Staphylococcus bacteria open. An unwanted mold grew on the dish, killing the bacteria. Fleming's curiosity to investigate the reason behind the bacterial death, followed by his experiments, led to the discovery of the antibiotic penicillin, which is produced by the fungus Penicillium . Even in the highly organized world of science, luck—when combined with an observant, curious mind—can lead to unexpected breakthroughs.
Whether scientific research is basic science or applied science, scientists must share their findings in order for other researchers to expand and build upon their discoveries. Collaboration with other scientists—when planning, conducting, and analyzing results—is important for scientific research. For this reason, important aspects of a scientist’s work are communicating with peers and disseminating results to peers. Scientists can share results by presenting them at a scientific meeting or conference, but this approach can reach only the select few who are present. Instead, most scientists present their results in peer-reviewed manuscripts that are published in scientific journals. Peer-reviewed manuscripts are scientific papers that a scientist’s colleagues or peers review. These colleagues are qualified individuals, often experts in the same research area, who judge whether or not the scientist’s work is suitable for publication. The process of peer review helps to ensure that the research in a scientific paper or grant proposal is original, significant, logical, and thorough. Grant proposals, which are requests for research funding, are also subject to peer review. Scientists publish their work so other scientists can reproduce their experiments under similar or different conditions to expand on the findings.
A scientific paper is very different from creative writing. Although creativity is required to design experiments, there are fixed guidelines when it comes to presenting scientific results. First, scientific writing must be brief, concise, and accurate. A scientific paper needs to be succinct but detailed enough to allow peers to reproduce the experiments.
The scientific paper consists of several specific sections—introduction, materials and methods, results, and discussion. This structure is sometimes called the “IMRaD” format. There are usually acknowledgment and reference sections as well as an abstract (a concise summary) at the beginning of the paper. There might be additional sections depending on the type of paper and the journal where it will be published. For example, some review papers require an outline.
The introduction starts with brief, but broad, background information about what is known in the field. A good introduction also gives the rationale of the work. It justifies the work carried out and also briefly mentions the end of the paper, where the researcher will present the hypothesis or research question driving the research. The introduction refers to the published scientific work of others and therefore requires citations following the style of the journal. Using the work or ideas of others without proper citation is plagiarism .
The materials and methods section includes a complete and accurate description of the substances the researchers use, and the method and techniques they use to gather data. The description should be thorough enough to allow another researcher to repeat the experiment and obtain similar results, but it does not have to be verbose. This section will also include information on how the researchers made measurements and the types of calculations and statistical analyses they used to examine raw data. Although the materials and methods section gives an accurate description of the experiments, it does not discuss them.
Some journals require a results section followed by a discussion section, but it is more common to combine both. If the journal does not allow combining both sections, the results section simply narrates the findings without any further interpretation. The researchers present results with tables or graphs, but they do not present duplicate information. In the discussion section, the researchers will interpret the results, describe how variables may be related, and attempt to explain the observations. It is indispensable to conduct an extensive literature search to put the results in the context of previously published scientific research. Therefore, researchers include proper citations in this section as well.
Finally, the conclusion section summarizes the importance of the experimental findings. While the scientific paper almost certainly answers one or more scientific questions that the researchers stated, any good research should lead to more questions. Therefore, a well-done scientific paper allows the researchers and others to continue and expand on the findings.
Review articles do not follow the IMRAD format because they do not present original scientific findings, or primary literature. Instead, they summarize and comment on findings that were published as primary literature and typically include extensive reference sections.
Ai generator.
Delve into the fascinating world of biology with our definitive guide on crafting impeccable hypothesis thesis statements . As the foundation of any impactful biological research, a well-formed hypothesis paves the way for groundbreaking discoveries and insights. Whether you’re examining cellular behavior or large-scale ecosystems, mastering the art of the thesis statement is crucial. Embark on this enlightening journey with us, as we provide stellar examples and invaluable writing advice tailored for budding biologists.
A good hypothesis in biology is a statement that offers a tentative explanation for a biological phenomenon, based on prior knowledge or observation. It should be:
Example: “If a plant is given a higher concentration of carbon dioxide, then it will undergo photosynthesis at an increased rate compared to a plant given a standard concentration of carbon dioxide.”
In this example:
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Biology, as the study of life and living organisms, is vast and diverse. Crafting a good thesis statement in this field requires a clear understanding of the topic at hand, capturing the essence of the research aim. From genetics to ecology, from cell biology to animal behavior, the following examples will give you a comprehensive idea about forming succinct biology thesis statements.
Genetics: Understanding the role of the BRCA1 gene in breast cancer susceptibility can lead to targeted treatments.
2. Evolution: The finch populations of the Galápagos Islands provide evidence of natural selection through beak variations in response to food availability.
3. Cell Biology: Mitochondrial dysfunction is a central factor in the onset of age-related neurodegenerative diseases.
4. Ecology: Deforestation in the Amazon directly impacts global carbon dioxide levels, influencing climate change.
5. Human Anatomy: Regular exercise enhances cardiovascular health by improving heart muscle function and reducing arterial plaque.
6. Marine Biology: Coral bleaching events in the Great Barrier Reef correlate strongly with rising sea temperatures.
7. Zoology: Migration patterns of Monarch butterflies are influenced by seasonal changes and available food sources.
8. Botany: The symbiotic relationship between mycorrhizal fungi and plant roots enhances nutrient absorption in poor soil conditions.
9. Microbiology: The overuse of antibiotics in healthcare has accelerated the evolution of antibiotic-resistant bacterial strains.
10. Physiology: High altitude adaptation in certain human populations has led to increased hemoglobin production.
11. Immunology: The role of T-cells in the human immune response is critical in developing effective vaccines against viral diseases.
12. Behavioral Biology: Birdsong variations in sparrows can be attributed to both genetic factors and environmental influences.
13. Developmental Biology: The presence of certain hormones during fetal development dictates the differentiation of sex organs in mammals.
14. Conservation Biology: The rapid decline of bee populations worldwide is directly linked to the use of certain pesticides in agriculture.
15. Molecular Biology: The CRISPR-Cas9 system has revolutionized gene editing techniques, offering potential cures for genetic diseases.
16. Virology: The mutation rate of the influenza virus necessitates annual updates in vaccine formulations.
17. Neurobiology: Neural plasticity in the adult brain can be enhanced through consistent learning and cognitive challenges.
18. Ethology: Elephant herds exhibit complex social structures and matriarchal leadership.
19. Biotechnology: Genetically modified crops can improve yield and resistance but also pose ecological challenges.
20. Environmental Biology: Industrial pollution in freshwater systems disrupts aquatic life and can lead to loss of biodiversity.
21. Neurodegenerative Diseases: Amyloid-beta protein accumulation in the brain is a key marker for Alzheimer’s disease progression.
22. Endocrinology: The disruption of thyroid hormone balance leads to metabolic disorders and weight fluctuations.
23. Bioinformatics: Machine learning algorithms can predict protein structures with high accuracy, advancing drug design.
24. Plant Physiology: The stomatal closure mechanism in plants helps prevent water loss and maintain turgor pressure.
25. Parasitology: The lifecycle of the malaria parasite involves complex interactions between humans and mosquitoes.
26. Molecular Genetics: Epigenetic modifications play a crucial role in gene expression regulation and cell differentiation.
27. Evolutionary Psychology: Human preference for symmetrical faces is a result of evolutionarily advantageous traits.
28. Ecosystem Dynamics: The reintroduction of apex predators in ecosystems restores ecological balance and biodiversity.
29. Epigenetics: Maternal dietary choices during pregnancy can influence the epigenetic profiles of offspring.
30. Biochemistry: Enzyme kinetics in metabolic pathways reveal insights into cellular energy production.
31. Bioluminescence: The role of bioluminescence in deep-sea organisms serves as camouflage and communication.
32. Genetics of Disease: Mutations in the CFTR gene cause cystic fibrosis, leading to severe respiratory and digestive issues.
33. Reproductive Biology: The influence of pheromones on mate selection is a critical aspect of reproductive success in many species.
34. Plant-Microbe Interactions: Rhizobium bacteria facilitate nitrogen fixation in leguminous plants, benefiting both organisms.
35. Comparative Anatomy: Homologous structures in different species provide evidence of shared evolutionary ancestry.
36. Stem Cell Research: Induced pluripotent stem cells hold immense potential for regenerative medicine and disease modeling.
37. Bioethics: Balancing the use of genetic modification in humans with ethical considerations is a complex challenge.
38. Molecular Evolution: The study of orthologous and paralogous genes offers insights into evolutionary relationships.
39. Bioenergetics: ATP synthesis through oxidative phosphorylation is a fundamental process driving cellular energy production.
40. Population Genetics: The Hardy-Weinberg equilibrium model helps predict allele frequencies in populations over time.
41. Animal Communication: The complex vocalizations of whales serve both social bonding and long-distance communication purposes.
42. Biogeography: The distribution of marsupials in Australia and their absence elsewhere highlights the impact of geographical isolation on evolution.
43. Aquatic Ecology: The phenomenon of eutrophication in lakes is driven by excessive nutrient runoff and results in harmful algal blooms.
44. Insect Behavior: The waggle dance of honeybees conveys precise information about the location of food sources to other members of the hive.
45. Microbial Ecology: The gut microbiome’s composition influences host health, metabolism, and immune system development.
46. Evolution of Sex: The Red Queen hypothesis explains the evolution of sexual reproduction as a defense against rapidly evolving parasites.
47. Immunotherapy: Manipulating the immune response to target cancer cells shows promise as an effective cancer treatment strategy.
48. Epigenetic Inheritance: Epigenetic modifications can be passed down through generations, impacting traits and disease susceptibility.
49. Comparative Genomics: Comparing the genomes of different species sheds light on genetic adaptations and evolutionary divergence.
50. Neurotransmission: The dopamine reward pathway in the brain is implicated in addiction and motivation-related behaviors.
51. Microbial Biotechnology: Genetically engineered bacteria can produce valuable compounds like insulin, revolutionizing pharmaceutical production.
52. Bioinformatics: DNA sequence analysis reveals evolutionary relationships between species and uncovers hidden genetic information.
53. Animal Migration: The navigational abilities of migratory birds are influenced by magnetic fields and celestial cues.
54. Human Evolution: The discovery of ancient hominin fossils provides insights into the evolutionary timeline of our species.
55. Cancer Genetics: Mutations in tumor suppressor genes contribute to the uncontrolled growth and division of cancer cells.
56. Aquatic Biomes: Coral reefs, rainforests of the sea, host incredible biodiversity and face threats from climate change and pollution.
57. Genomic Medicine: Personalized treatments based on an individual’s genetic makeup hold promise for more effective healthcare.
58. Molecular Pharmacology: Understanding receptor-ligand interactions aids in the development of targeted drugs for specific diseases.
59. Biodiversity Conservation: Preserving habitat diversity is crucial to maintaining ecosystems and preventing species extinction.
60. Evolutionary Developmental Biology: Comparing embryonic development across species reveals shared genetic pathways and evolutionary constraints.
61. Plant Reproductive Strategies: Understanding the trade-offs between asexual and sexual reproduction in plants sheds light on their evolutionary success.
62. Parasite-Host Interactions: The coevolution of parasites and their hosts drives adaptations and counter-adaptations over time.
63. Genomic Diversity: Exploring genetic variations within populations helps uncover disease susceptibilities and evolutionary history.
64. Ecological Succession: Studying the process of ecosystem recovery after disturbances provides insights into resilience and stability.
65. Conservation Genetics: Genetic diversity assessment aids in formulating effective conservation strategies for endangered species.
66. Neuroplasticity and Learning: Investigating how the brain adapts through synaptic changes improves our understanding of memory and learning.
67. Synthetic Biology: Designing and engineering biological systems offers innovative solutions for medical, environmental, and industrial challenges.
68. Ethnobotany: Documenting the traditional uses of plants by indigenous communities informs both conservation and pharmaceutical research.
69. Ecological Niche Theory: Exploring how species adapt to specific ecological niches enhances our grasp of biodiversity patterns.
70. Ecosystem Services: Quantifying the benefits provided by ecosystems, like pollination and carbon sequestration, supports conservation efforts.
71. Fungal Biology: Investigating mycorrhizal relationships between fungi and plants illuminates nutrient exchange mechanisms.
72. Molecular Clock Hypothesis: Genetic mutations accumulate over time, providing a method to estimate evolutionary divergence dates.
73. Developmental Disorders: Unraveling the genetic and environmental factors contributing to developmental disorders informs therapeutic approaches.
74. Epigenetics and Disease: Epigenetic modifications contribute to the development of diseases like cancer, diabetes, and neurodegenerative disorders.
75. Animal Cognition: Studying cognitive abilities in animals unveils their problem-solving skills, social dynamics, and sensory perceptions.
76. Microbiota-Brain Axis: The gut-brain connection suggests a bidirectional communication pathway influencing mental health and behavior.
77. Neurological Disorders: Neurodegenerative diseases like Parkinson’s and Alzheimer’s have genetic and environmental components that drive their progression.
78. Plant Defense Mechanisms: Investigating how plants ward off pests and pathogens informs sustainable agricultural practices.
79. Conservation Genomics: Genetic data aids in identifying distinct populations and prioritizing conservation efforts for at-risk species.
80. Reproductive Strategies: Comparing reproductive methods in different species provides insights into evolutionary trade-offs and reproductive success.
81. Epigenetics in Aging: Exploring epigenetic changes in the aging process offers insights into longevity and age-related diseases.
82. Antimicrobial Resistance: Understanding the genetic mechanisms behind bacterial resistance to antibiotics informs strategies to combat the global health threat.
83. Plant-Animal Interactions: Investigating mutualistic relationships between plants and pollinators showcases the delicate balance of ecosystems.
84. Adaptations to Extreme Environments: Studying extremophiles reveals the remarkable ways organisms thrive in extreme conditions like deep-sea hydrothermal vents.
85. Genetic Disorders: Genetic mutations underlie numerous disorders like cystic fibrosis, sickle cell anemia, and muscular dystrophy.
86. Conservation Behavior: Analyzing the behavioral ecology of endangered species informs habitat preservation and restoration efforts.
87. Neuroplasticity in Rehabilitation: Harnessing the brain’s ability to rewire itself offers promising avenues for post-injury or post-stroke rehabilitation.
88. Disease Vectors: Understanding how mosquitoes transmit diseases like malaria and Zika virus is critical for disease prevention strategies.
89. Biochemical Pathways: Mapping metabolic pathways in cells provides insights into disease development and potential therapeutic targets.
90. Invasive Species Impact: Examining the effects of invasive species on native ecosystems guides management strategies to mitigate their impact.
91. Molecular Immunology: Studying the intricate immune response mechanisms aids in the development of vaccines and immunotherapies.
92. Plant-Microbe Symbiosis: Investigating how plants form partnerships with beneficial microbes enhances crop productivity and sustainability.
93. Cancer Immunotherapy: Harnessing the immune system to target and eliminate cancer cells offers new avenues for cancer treatment.
94. Evolution of Flight: Analyzing the adaptations leading to the development of flight in birds and insects sheds light on evolutionary innovation.
95. Genomic Diversity in Human Populations: Exploring genetic variations among different human populations informs ancestry, migration, and susceptibility to diseases.
96. Hormonal Regulation: Understanding the role of hormones in growth, reproduction, and homeostasis provides insights into physiological processes.
97. Conservation Genetics in Plant Conservation: Genetic diversity assessment helps guide efforts to conserve rare and endangered plant species.
98. Neuronal Communication: Investigating neurotransmitter systems and synaptic transmission enhances our comprehension of brain function.
99. Microbial Biogeography: Mapping the distribution of microorganisms across ecosystems aids in understanding their ecological roles and interactions.
100. Gene Therapy: Developing methods to replace or repair defective genes offers potential treatments for genetic disorders.
This section offers diverse examples of scientific hypothesis statements that cover a range of biological topics. Each example briefly describes the subject matter and the potential implications of the hypothesis.
Testability hypothesis is a critical aspect of a hypothesis. These examples are formulated in a way that allows them to be tested through experiments or observations. They focus on cause-and-effect relationships that can be verified or refuted.
This section emphasizes hypotheses that are part of broader scientific investigations. They involve studying complex interactions or phenomena and often contribute to our understanding of larger biological systems.
These examples are tailored for research hypothesis studies. They highlight hypotheses that drive focused research questions, often leading to specific experimental designs and data collection methods.
Predictive simple hypothesis involve making educated guesses about how variables might interact or behave under specific conditions. These examples showcase hypotheses that anticipate outcomes based on existing knowledge.
A hypothesis in biology is a critical component of scientific research that proposes an explanation for a specific biological phenomenon. Writing a well-formulated hypothesis sets the foundation for conducting experiments, making observations, and drawing meaningful conclusions. Follow this step-by-step guide to create a strong biology hypothesis:
1. Identify the Phenomenon: Clearly define the biological phenomenon you intend to study. This could be a question, a pattern, an observation, or a problem in the field of biology.
2. Conduct Background Research: Before formulating a hypothesis, gather relevant information from scientific literature. Understand the existing knowledge about the topic to ensure your hypothesis builds upon previous research.
3. State the Independent and Dependent Variables: Identify the variables involved in the phenomenon. The independent variable is what you manipulate or change, while the dependent variable is what you measure as a result of the changes.
4. Formulate a Testable Question: Based on your background research, create a specific and testable question that addresses the relationship between the variables. This question will guide the formulation of your hypothesis.
5. Craft the Hypothesis: A hypothesis should be a clear and concise statement that predicts the outcome of your experiment or observation. It should propose a cause-and-effect relationship between the independent and dependent variables.
6. Use the “If-Then” Structure: Formulate your hypothesis using the “if-then” structure. The “if” part states the independent variable and the condition you’re manipulating, while the “then” part predicts the outcome for the dependent variable.
7. Make it Falsifiable: A good hypothesis should be testable and capable of being proven false. There should be a way to gather data that either supports or contradicts the hypothesis.
8. Be Specific and Precise: Avoid vague language and ensure that your hypothesis is specific and precise. Clearly define the variables and the expected relationship between them.
9. Revise and Refine: Once you’ve formulated your hypothesis, review it to ensure it accurately reflects your research question and variables. Revise as needed to make it more concise and focused.
10. Seek Feedback: Share your hypothesis with peers, mentors, or colleagues to get feedback. Constructive input can help you refine your hypothesis further.
Writing a biology alternative hypothesis statement requires precision and clarity to ensure that your research is well-structured and testable. Here are some valuable tips to help you create effective and scientifically sound hypothesis statements:
1. Be Clear and Concise: Your hypothesis statement should convey your idea succinctly. Avoid unnecessary jargon or complex language that might confuse your audience.
2. Address Cause and Effect: A hypothesis suggests a cause-and-effect relationship between variables. Clearly state how changes in the independent variable are expected to affect the dependent variable.
3. Use Specific Language: Define your variables precisely. Use specific terms to describe the independent and dependent variables, as well as any conditions or measurements.
4. Follow the “If-Then” Structure: Use the classic “if-then” structure to frame your hypothesis. State the independent variable (if) and the expected outcome (then). This format clarifies the relationship you’re investigating.
5. Make it Testable: Your hypothesis must be capable of being tested through experimentation or observation. Ensure that there is a measurable and observable way to determine if it’s true or false.
6. Avoid Ambiguity: Eliminate vague terms that can be interpreted in multiple ways. Be precise in your language to avoid confusion.
7. Base it on Existing Knowledge: Ground your hypothesis in prior research or existing scientific theories. It should build upon established knowledge and contribute new insights.
8. Predict a Direction: Your hypothesis should predict a specific outcome. Whether you anticipate an increase, decrease, or a difference, your hypothesis should make a clear prediction.
9. Be Focused: Keep your hypothesis statement focused on one specific idea or relationship. Avoid trying to address too many variables or concepts in a single statement.
10. Consider Alternative Explanations: Acknowledge alternative explanations for your observations or outcomes. This demonstrates critical thinking and a thorough understanding of your field.
11. Avoid Value Judgments: Refrain from including value judgments or opinions in your hypothesis. Stick to objective and measurable factors.
12. Be Realistic: Ensure that your hypothesis is plausible and feasible. It should align with what is known about the topic and be achievable within the scope of your research.
13. Refine and Revise: Draft multiple versions of your hypothesis statement and refine them. Discuss and seek feedback from mentors, peers, or advisors to enhance its clarity and precision.
14. Align with Research Goals: Your hypothesis should align with the overall goals of your research project. Make sure it addresses the specific question or problem you’re investigating.
15. Be Open to Revision: As you conduct research and gather data, be open to revising your hypothesis if the evidence suggests a different outcome than initially predicted.
Remember, a well-crafted biology science hypothesis statement serves as the foundation of your research and guides your experimental design and data analysis. It’s essential to invest time and effort in formulating a clear, focused, and testable hypothesis that contributes to the advancement of scientific knowledge.
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Coho salmon ( Oncorhynchus kisutch ) are amazing fish. Indigenous to the Pacific Northwest, they begin their lives in freshwater streams and then relocate to the open ocean. But when a Coho salmon reaches breeding age, it'll return to the waterway of its birth , sometimes traveling 400 miles (644 kilometers) to get there.
Enter the late Arthur Davis Hasler. While an ecologist and biologist at the University of Wisconsin, he was intrigued by the question of how these creatures find their home streams. And in 1960, he used a Hypothesis-Presentation.pdf">basic tenet of science — the hypothesis — to find out.
So what is a hypothesis? A hypothesis is a tentative, testable explanation for an observed phenomenon in nature. Hypotheses are narrow in scope — unlike theories , which cover a broad range of observable phenomena and draw from many different lines of evidence. Meanwhile, a prediction is a result you'd expect to get if your hypothesis or theory is accurate.
So back to 1960 and Hasler and those salmon. One unverified idea was that Coho salmon used eyesight to locate their home streams. Hasler set out to test this notion (or hypothesis). First, he rounded up several fish who'd already returned to their native streams. Next, he blindfolded some of the captives — but not all of them — before dumping his salmon into a faraway stretch of water. If the eyesight hypothesis was correct, then Hasler could expect fewer of the blindfolded fish to return to their home streams.
Things didn't work out that way. The fish without blindfolds came back at the same rate as their blindfolded counterparts. (Other experiments demonstrated that smell, and not sight, is the key to the species' homing ability.)
Although Hasler's blindfold hypothesis was disproven, others have fared better. Today, we're looking at three of the best-known experiments in history — and the hypotheses they tested.
Isaac newton's radiant prisms (1665), robert paine's revealing starfish (1963-1969).
The Hypothesis : If dogs are susceptible to conditioned responses (drooling), then a dog who is regularly exposed to the same neutral stimulus (metronome/bell) before it receives food will associate this neutral stimulus with the act of eating. Eventually, the dog should begin to drool at a predictable rate when it encounters said stimulus — even before any actual food is offered.
The Experiment : A Nobel Prize-winner and outspoken critic of Soviet communism, Ivan Pavlov is synonymous with man's best friend . In 1903, the Russian-born scientist kicked off a decades-long series of experiments involving dogs and conditioned responses .
Offer a plate of food to a hungry dog and it'll salivate. In this context, the stimulus (the food) will automatically trigger a particular response (the drooling). The latter is an innate, unlearned reaction to the former.
By contrast, the rhythmic sound of a metronome or bell is a neutral stimulus. To a dog, the noise has no inherent meaning and if the animal has never heard it before, the sound won't provoke an instinctive reaction. But the sight of food sure will .
So when Pavlov and his lab assistants played the sound of the metronome/bell before feeding sessions, the researchers conditioned test dogs to mentally link metronomes/bells with mealtime. Due to repeated exposure, the noise alone started to make the dogs' mouths water before they were given food.
According to " Ivan Pavlov: A Russian Life in Science " by biographer Daniel P. Todes, Pavlov's big innovation here was his discovery that he could quantify the reaction of each pooch by measuring the amount of saliva it generated. Every canine predictably drooled at its own consistent rate when he or she encountered a personalized (and artificial) food-related cue.
Pavlov and his assistants used conditioned responses to look at other hypotheses about animal physiology, as well. In one notable experiment, a dog was tested on its ability to tell time . This particular pooch always received food when it heard a metronome click at the rate of 60 strokes per minute. But it never got any food after listening to a slower, 40-strokes-per-minute beat. Lo and behold, Pavlov's animal began to salivate in response to the faster rhythm — but not the slower one . So clearly, it could tell the two rhythmic beats apart.
The Verdict : With the right conditioning — and lots of patience — you can make a hungry dog respond to neutral stimuli by salivating on cue in a way that's both predictable and scientifically quantifiable.
The Hypothesis : If white sunlight is a mixture of all the colors in the visible spectrum — and these travel at varying wavelengths — then each color will refract at a different angle when a beam of sunlight passes through a glass prism.
The Experiments : Color was a scientific mystery before Isaac Newton came along. During the summer of 1665, he started experimenting with glass prisms from the safety of a darkened room in Cambridge, England.
He cut a quarter-inch (0.63-centimeter) circular hole into one of the window shutters, allowing a single beam of sunlight to enter the place. When Newton held up a prism to this ray, an oblong patch of multicolored light was projected onto the opposite wall.
This contained segregated layers of red, orange, yellow, green, blue, indigo and violet light. From top to bottom, this patch measured 13.5 inches (33.65 centimeters) tall, yet it was only 2.6 inches (6.6 centimeters) across.
Newton deduced that these vibrant colors had been hiding within the sunlight itself, but the prism bent (or "refracted") them at different angles, which separated the colors out.
Still, he wasn't 100 percent sure. So Newton replicated the experiment with one small change. This time, he took a second prism and had it intercept the rainbow-like patch of light. Once the refracted colors entered the new prism, they recombined into a circular white sunbeam. In other words, Newton took a ray of white light, broke it apart into a bunch of different colors and then reassembled it. What a neat party trick!
The Verdict : Sunlight really is a blend of all the colors in the rainbow — and yes, these can be individually separated via light refraction.
The Hypothesis : If predators limit the populations of the organisms they attack, then we'd expect the prey species to become more common after the eradication of a major predator.
The Experiment : Meet Pisaster ochraceus , also known as the purple sea star (or the purple starfish if you prefer).
Using an extendable stomach , the creature feeds on mussels, limpets, barnacles, snails and other hapless victims. On some seaside rocks (and tidal pools) along the coast of Washington state, this starfish is the apex predator.
The animal made Robert Paine a scientific celebrity. An ecologist by trade, Paine was fascinated by the environmental roles of top predators. In June 1963, he kicked off an ambitious experiment along Washington state's Mukkaw Bay. For years on end, Paine kept a rocky section of this shoreline completely starfish-free.
It was hard work. Paine had to regularly pry wayward sea stars off "his" outcrop — sometimes with a crowbar. Then he'd chuck them into the ocean.
Before the experiment, Paine observed 15 different species of animals and algae inhabiting the area he decided to test. By June 1964 — one year after his starfish purge started — that number had dropped to eight .
Unchecked by purple sea stars, the barnacle population skyrocketed. Subsequently, these were replaced by California mussels , which came to dominate the terrain. By anchoring themselves to rocks in great numbers, the mussels edged out other life-forms. That made the outcrop uninhabitable to most former residents: Even sponges, anemones and algae — organisms that Pisaster ochraceus doesn't eat — were largely evicted.
All those species continued to thrive on another piece of shoreline that Paine left untouched. Later experiments convinced him that Pisaster ochraceus is a " keystone species ," a creature who exerts disproportionate influence over its environment. Eliminate the keystone and the whole system gets disheveled.
The Verdict : Apex predators don't just affect the animals that they hunt. Removing a top predator sets off a chain reaction that can fundamentally transform an entire ecosystem.
Contrary to popular belief, Pavlov almost never used bells in his dog experiments. Instead, he preferred metronomes, buzzers, harmoniums and electric shocks.
How can a hypothesis become a theory, what's the difference between a hypothesis and a prediction.
Please copy/paste the following text to properly cite this HowStuffWorks.com article:
It's the initial building block in the scientific method.
What makes a hypothesis testable.
Bibliography.
A scientific hypothesis is a tentative, testable explanation for a phenomenon in the natural world. It's the initial building block in the scientific method . Many describe it as an "educated guess" based on prior knowledge and observation. While this is true, a hypothesis is more informed than a guess. While an "educated guess" suggests a random prediction based on a person's expertise, developing a hypothesis requires active observation and background research.
The basic idea of a hypothesis is that there is no predetermined outcome. For a solution to be termed a scientific hypothesis, it has to be an idea that can be supported or refuted through carefully crafted experimentation or observation. This concept, called falsifiability and testability, was advanced in the mid-20th century by Austrian-British philosopher Karl Popper in his famous book "The Logic of Scientific Discovery" (Routledge, 1959).
A key function of a hypothesis is to derive predictions about the results of future experiments and then perform those experiments to see whether they support the predictions.
A hypothesis is usually written in the form of an if-then statement, which gives a possibility (if) and explains what may happen because of the possibility (then). The statement could also include "may," according to California State University, Bakersfield .
Here are some examples of hypothesis statements:
A useful hypothesis should be testable and falsifiable. That means that it should be possible to prove it wrong. A theory that can't be proved wrong is nonscientific, according to Karl Popper's 1963 book " Conjectures and Refutations ."
An example of an untestable statement is, "Dogs are better than cats." That's because the definition of "better" is vague and subjective. However, an untestable statement can be reworded to make it testable. For example, the previous statement could be changed to this: "Owning a dog is associated with higher levels of physical fitness than owning a cat." With this statement, the researcher can take measures of physical fitness from dog and cat owners and compare the two.
In an experiment, researchers generally state their hypotheses in two ways. The null hypothesis predicts that there will be no relationship between the variables tested, or no difference between the experimental groups. The alternative hypothesis predicts the opposite: that there will be a difference between the experimental groups. This is usually the hypothesis scientists are most interested in, according to the University of Miami .
For example, a null hypothesis might state, "There will be no difference in the rate of muscle growth between people who take a protein supplement and people who don't." The alternative hypothesis would state, "There will be a difference in the rate of muscle growth between people who take a protein supplement and people who don't."
If the results of the experiment show a relationship between the variables, then the null hypothesis has been rejected in favor of the alternative hypothesis, according to the book " Research Methods in Psychology " (BCcampus, 2015).
There are other ways to describe an alternative hypothesis. The alternative hypothesis above does not specify a direction of the effect, only that there will be a difference between the two groups. That type of prediction is called a two-tailed hypothesis. If a hypothesis specifies a certain direction — for example, that people who take a protein supplement will gain more muscle than people who don't — it is called a one-tailed hypothesis, according to William M. K. Trochim , a professor of Policy Analysis and Management at Cornell University.
Sometimes, errors take place during an experiment. These errors can happen in one of two ways. A type I error is when the null hypothesis is rejected when it is true. This is also known as a false positive. A type II error occurs when the null hypothesis is not rejected when it is false. This is also known as a false negative, according to the University of California, Berkeley .
A hypothesis can be rejected or modified, but it can never be proved correct 100% of the time. For example, a scientist can form a hypothesis stating that if a certain type of tomato has a gene for red pigment, that type of tomato will be red. During research, the scientist then finds that each tomato of this type is red. Though the findings confirm the hypothesis, there may be a tomato of that type somewhere in the world that isn't red. Thus, the hypothesis is true, but it may not be true 100% of the time.
The best hypotheses are simple. They deal with a relatively narrow set of phenomena. But theories are broader; they generally combine multiple hypotheses into a general explanation for a wide range of phenomena, according to the University of California, Berkeley . For example, a hypothesis might state, "If animals adapt to suit their environments, then birds that live on islands with lots of seeds to eat will have differently shaped beaks than birds that live on islands with lots of insects to eat." After testing many hypotheses like these, Charles Darwin formulated an overarching theory: the theory of evolution by natural selection.
"Theories are the ways that we make sense of what we observe in the natural world," Tanner said. "Theories are structures of ideas that explain and interpret facts."
Encyclopedia Britannica. Scientific Hypothesis. Jan. 13, 2022. https://www.britannica.com/science/scientific-hypothesis
Karl Popper, "The Logic of Scientific Discovery," Routledge, 1959.
California State University, Bakersfield, "Formatting a testable hypothesis." https://www.csub.edu/~ddodenhoff/Bio100/Bio100sp04/formattingahypothesis.htm
Karl Popper, "Conjectures and Refutations," Routledge, 1963.
Price, P., Jhangiani, R., & Chiang, I., "Research Methods of Psychology — 2nd Canadian Edition," BCcampus, 2015.
University of Miami, "The Scientific Method" http://www.bio.miami.edu/dana/161/evolution/161app1_scimethod.pdf
William M.K. Trochim, "Research Methods Knowledge Base," https://conjointly.com/kb/hypotheses-explained/
University of California, Berkeley, "Multiple Hypothesis Testing and False Discovery Rate" https://www.stat.berkeley.edu/~hhuang/STAT141/Lecture-FDR.pdf
University of California, Berkeley, "Science at multiple levels" https://undsci.berkeley.edu/article/0_0_0/howscienceworks_19
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A hypothesis proposes a relationship between the independent and dependent variable. A hypothesis is a prediction of the outcome of a test. It forms the basis for designing an experiment in the scientific method.A good hypothesis is testable, meaning it makes a prediction you can check with observation or experimentation.
Updated on January 12, 2019. A hypothesis is a tentative answer to a scientific question. A testable hypothesis is a hypothesis that can be proved or disproved as a result of testing, data collection, or experience. Only testable hypotheses can be used to conceive and perform an experiment using the scientific method.
When conducting scientific experiments, researchers develop hypotheses to guide experimental design. A hypothesis is a suggested explanation that is both testable and falsifiable. You must be able to test your hypothesis through observations and research, and it must be possible to prove your hypothesis false. For example, Michael observes that ...
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.
If you're seeing this message, it means we're having trouble loading external resources on our website. If you're behind a web filter, please make sure that the domains *.kastatic.org and *.kasandbox.org are unblocked.
A hypothesis is NOT just a guess (not even an educated one), although it can be based on your prior experience (such as in the example where the light won't turn on). In general, hypotheses in biology should be based on a credible, referenced source of information. A hypothesis must be testable to ensure that it is valid. For example, a ...
5. Phrase your hypothesis in three ways. To identify the variables, you can write a simple prediction in if…then form. The first part of the sentence states the independent variable and the second part states the dependent variable. If a first-year student starts attending more lectures, then their exam scores will improve.
Testable by ethical studies. The hypothesis should be testable by experiments that are ethically acceptable.9 For example, a hypothesis that parachutes reduce mortality from falls from an airplane cannot be tested using a randomized controlled trial.10 This is because it is obvious that all those jumping from a flying plane without a parachute ...
A hypothesis is a tentative statement that proposes a possible explanation to some phenomenon or event. A useful hypothesis is a testable statement, which may include a prediction. A hypothesis should not be confused with a theory. Theories are general explanations based on a large amount of data. For example, the theory of evolution applies to ...
The hypothesis is often written using the words "IF" and "THEN." For example, "If I do not study, then I will fail the test." The "if' and "then" statements reflect your independent and dependent variables. The hypothesis should relate back to your original question and must be testable.
hypothesis. science. scientific hypothesis, an idea that proposes a tentative explanation about a phenomenon or a narrow set of phenomena observed in the natural world. The two primary features of a scientific hypothesis are falsifiability and testability, which are reflected in an "If…then" statement summarizing the idea and in the ...
15 Hypothesis Examples. A hypothesis is defined as a testable prediction, and is used primarily in scientific experiments as a potential or predicted outcome that scientists attempt to prove or disprove (Atkinson et al., 2021; Tan, 2022). In my types of hypothesis article, I outlined 13 different hypotheses, including the directional hypothesis ...
For example, when studying biology, you must remember living organisms obey the laws of thermodynamics while using free energy and matter from the environment to carry out life processes that are explored in later chapters, such as metabolism and reproduction. ... Ask if this hypothesis can be modified to become a testable hypothesis. Stress ...
A prediction is similar to a hypothesis but it typically has the format "If . . . then . . . ." For example, the prediction for the first hypothesis might be, "If the student turns on the air conditioning, then the classroom will no longer be too warm." Testing a Hypothesis. A valid hypothesis must be testable.
The hypothesis is an educated, testable prediction about what will happen. Make it clear. A good hypothesis is written in clear and simple language. Reading your hypothesis should tell a teacher or judge exactly what you thought was going to happen when you started your project. Keep the variables in mind.
Writing a well-formulated hypothesis sets the foundation for conducting experiments, making observations, and drawing meaningful conclusions. Follow this step-by-step guide to create a strong biology hypothesis: 1. Identify the Phenomenon: Clearly define the biological phenomenon you intend to study.
A hypothesis is a tentative, testable explanation for an observed phenomenon in nature. Hypotheses are narrow in scope — unlike theories, which cover a broad range of observable phenomena and draw from many different lines of evidence. Meanwhile, a prediction is a result you'd expect to get if your hypothesis or theory is accurate.
A scientific hypothesis is a tentative, testable explanation for a phenomenon in the natural world. It's the initial building block in the scientific method. Many describe it as an "educated guess ...