what is hypothesis generating research

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

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Hypothesis generation is an early and critical step in any hypothesis-driven clinical research project. Because it is not yet a well-understood cognitive process, the need to improve the process goes unrecognized. Without an impactful hypothesis, the significance of any research project can be questionable, regardless of the rigor or diligence applied in other steps of the study, e.g., study design, data collection, and result analysis. In this perspective article, the authors provide a literature review on the following topics first: scientific thinking, reasoning, medical reasoning, literature-based discovery, and a field study to explore scientific thinking and discovery. Over the years, scientific thinking has shown excellent progress in cognitive science and its applied areas: education, medicine, and biomedical research. However, a review of the literature reveals the lack of original studies on hypothesis generation in clinical research. The authors then summarize their first human participant study exploring data-driven hypothesis generation by clinical researchers in a simulated setting. The results indicate that a secondary data analytical tool, VIADS—a visual interactive analytic tool for filtering, summarizing, and visualizing large health data sets coded with hierarchical terminologies, can shorten the time participants need, on average, to generate a hypothesis and also requires fewer cognitive events to generate each hypothesis. As a counterpoint, this exploration also indicates that the quality ratings of the hypotheses thus generated carry significantly lower ratings for feasibility when applying VIADS. Despite its small scale, the study confirmed the feasibility of conducting a human participant study directly to explore the hypothesis generation process in clinical research. This study provides supporting evidence to conduct a larger-scale study with a specifically designed tool to facilitate the hypothesis-generation process among inexperienced clinical researchers. A larger study could provide generalizable evidence, which in turn can potentially improve clinical research productivity and overall clinical research enterprise.

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The Research Hypothesis: Role and Construction

• First Online: 01 January 2012

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• Phyllis G. Supino EdD 3  

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A hypothesis is a logical construct, interposed between a problem and its solution, which represents a proposed answer to a research question. It gives direction to the investigator’s thinking about the problem and, therefore, facilitates a solution. There are three primary modes of inference by which hypotheses are developed: deduction (reasoning from a general propositions to specific instances), induction (reasoning from specific instances to a general proposition), and abduction (formulation/acceptance on probation of a hypothesis to explain a surprising observation).

A research hypothesis should reflect an inference about variables; be stated as a grammatically complete, declarative sentence; be expressed simply and unambiguously; provide an adequate answer to the research problem; and be testable. Hypotheses can be classified as conceptual versus operational, single versus bi- or multivariable, causal or not causal, mechanistic versus nonmechanistic, and null or alternative. Hypotheses most commonly entail statements about “variables” which, in turn, can be classified according to their level of measurement (scaling characteristics) or according to their role in the hypothesis (independent, dependent, moderator, control, or intervening).

A hypothesis is rendered operational when its broadly (conceptually) stated variables are replaced by operational definitions of those variables. Hypotheses stated in this manner are called operational hypotheses, specific hypotheses, or predictions and facilitate testing.

Wrong hypotheses, rightly worked from, have produced more results than unguided observation

—Augustus De Morgan, 1872[ 1 ]—

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Supino, P.G. (2012). The Research Hypothesis: Role and Construction. In: Supino, P., Borer, J. (eds) Principles of Research Methodology. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-3360-6_3

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Approaches to informed consent for hypothesis-testing and hypothesis-generating clinical genomics research

• Flavia M Facio 1 ,
• Julie C Sapp 1 ,
• Amy Linn 1 , 2 &
• Leslie G Biesecker 1  

BMC Medical Genomics volume  5 , Article number:  45 ( 2012 ) Cite this article

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Massively-parallel sequencing (MPS) technologies create challenges for informed consent of research participants given the enormous scale of the data and the wide range of potential results.

We propose that the consent process in these studies be based on whether they use MPS to test a hypothesis or to generate hypotheses. To demonstrate the differences in these approaches to informed consent, we describe the consent processes for two MPS studies. The purpose of our hypothesis-testing study is to elucidate the etiology of rare phenotypes using MPS. The purpose of our hypothesis-generating study is to test the feasibility of using MPS to generate clinical hypotheses, and to approach the return of results as an experimental manipulation. Issues to consider in both designs include: volume and nature of the potential results, primary versus secondary results, return of individual results, duty to warn, length of interaction, target population, and privacy and confidentiality.

The categorization of MPS studies as hypothesis-testing versus hypothesis-generating can help to clarify the issue of so-called incidental or secondary results for the consent process, and aid the communication of the research goals to study participants.

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Advances in DNA sequencing technologies and concomitant cost reductions have made the use of massively-parallel sequencing (MPS) in clinical research practicable for many researchers. Implementations of MPS include whole genome sequencing and whole exome sequencing, which we consider to be the same, for the purposes of informed consent. A challenge for researchers employing these technologies is to develop appropriate informed consent [ 1 , 2 ], given the enormous amount of information generated for each research participant, and the wide range of medically-relevant genetic results. Most of the informed consent challenges raised by MPS are not novel – what is novel is the scale and scope of genetic interrogation, and the opportunity to develop novel clinical research paradigms.

Massively-parallel sequencing has the capacity to detect nearly any disease-causing gene variant, including late-onset disorders, such as neurologic or cancer-susceptibility syndromes, subclinical disease or endo-phenotypes, such as impaired fasting glucose, and heterozygous carriers of traits inherited in a recessive pattern. Not only is the range of the disorders broad, but the variants have a wide range of relative risks from very high to nearly zero. This is a key distinction of MPS when compared to common SNP variant detection (using so-called gene chips). Because some variants discovered by MPS can be highly penetrant, the detection of such variants can have enormous medical and counseling impact. While many of these informed consent issues have been addressed previously [ 1 , 3 ], the use of MPS in clinical research combines these issues and is on a scale that is orders of magnitude greater than previous study designs.

The initial clinical research uses of MPS were a brute force approach to the identification of mutations for rare mendelian disorders [ 4 ]. This is a variation of positional cloning (also known as gene mapping) and thus a form of classical hypothesis-testing research. The hypothesis is that the phenotype under study is caused by a genetic variant and a suite of techniques is employed (in this case MPS) to identify that causative variant. The application of this technology in this setting is of great promise and will identify causative gene variants for numerous traits, with some predicting that the majority of Mendelian disorders will be elucidated in 5–10 years.

The second of these pathways to discovery is a more novel approach of generating and then sifting MPS results as the raw material to allow the generation of clinical hypotheses, which are in turn used to design clinical experiments to discover the phenotype that is associated with that genotype. This approach we term hypothesis-generating clinical genomics. These hypothesis-generating studies require a consent process that provides the participant with an understanding of scale and scope of the interrogation, which is based on a contextual understanding of the goal and overall organization of the research since specific risks and benefits can be difficult to delineate [ 5 , 6 ]. Importantly, participants need to understand the notion that the researcher is exploring their genomes in an open-ended fashion, that the goal of the experiment is not predictable at the outset, and that the participant will be presented with downstream situations that are not currently foreseeable.

We outline here our approaches to informed consent for our hypothesis-testing and hypothesis-generating MPS research studies. We propose that the consent process be tailored depending on which of these two designs is used, and whether the research aims include study of the return of results.

General issues regarding return of results

Participants in our protocols have the option to learn their potentially clinically relevant genetic variant results. The issue of return of results is controversial and the theoretical arguments for and against the return of results have been extensively debated [ 7 ]. Although an increasing body of literature describes the approaches taken by a few groups no clear consensus exists in either the clinical genomics or bioethics community [ 8 ]. At one end of the spectrum there are those who argue that no results should be returned [ 9 ], and at the other end others contend that the entire sequence should be presented to the research participant [ 10 – 12 ]. In between these extremes lies a qualified or intermediate disclosure policy [ 13 , 14 ]. We take the intermediate position in both of our protocols by giving research participants the choice to receive results, including variants deemed to be clinically actionable [ 3 , 15 ]. Additionally, both protocols are investigating participants’ intentions towards receiving different types of results in order to inform the disclosure policies within the projects and in the broader community [ 16 ]. Because one of our research goals is to study the issues surrounding return of results, it is appropriate and necessary to return results. Thus, the following discussion focuses on issues pertinent to studies that plan to return results.

Issues to consider

Issue #1: primary versus secondary variant results and the open-ended nature of clinical genomics.

In our hypothesis-testing study we distinguish variants as either primary or secondary variants, the distinction reflecting the purpose of the study. A primary variant is a mutation that causes the phenotype that is under study, i.e., the hypothesis that is being tested in the study. A secondary variant is any mutation result not related to the disorder under study, but discovered as part of the quest for the primary variant.

We prefer the term ‘secondary’ to ‘incidental’ because the latter is an adjective indicating chance occurrence, and the discovery of a disease causing mutation by MPS cannot be considered a chance occurrence. The word ‘incidental’ also suggests a lesser degree of importance or impact and it is important to recognize that secondary findings can be of greater medical or personal impact than primary findings.

The consent discussion about results potentially available from participation in a hypothesis-testing study is framed in terms of the study goal, and we assume a high degree of alignment between participants’ goals and the researchers’ aims with respect to primary variants. Participants are, in general, highly motivated to learn the primary variant result and we presume that this motivation contributed to their decision to enroll in the study, similar to motivations for those who have been involved in positional cloning studies. This motivation may not hold for secondary variants, but our approach is to offer them the opportunity to learn secondary and actionable variants that may substantially alter susceptibility to, or reproductive risk for, disease.

In the hypothesis-generating study design no categorical distinction (primary vs. secondary) is made among pathogenic variants, i.e., all variants are treated the same without the label of ‘primary’ or ‘secondary’. This is because we are not using MPS to uncover genetic variants for a specific disease, and any of the variants could potentially be used for hypothesis generation. We suggest that this is the most novel issue with respect to informed consent as the study is open-ended regarding its goals and downstream research activities. This is challenging for informed consent because it is impossible to know what types of hypotheses may be generated at the time of enrollment and consent.

Because the downstream research topics and activities are impossible to predict in hypothesis-generating research, subjects must be consented initially to the open-ended nature of the project. During the course of the study, they must be iteratively re-consented as hypothesis are generated from the genomic data and more specific follow-up studies are designed and proposed to test those newly generated hypotheses. These downstream, iterative consents will vary in their formality, and the degree to which they need to be reviewed and approved. Some general procedures can be approved in advance; for example it may be anticipated that segregation studies would be useful to determine causality for sequence variants or the investigator may simply wish to obtain some additional targeted medical or family history from the research subject. This could be approved prospectively by the IRB with the iterative consent with the subject comprising a verbal discussion of the nature of the trait for which the segregation analysis or additional information is being sought. More specific or more invasive or risky iterative analyses would necessitate review and approval by the IRB with written informed consent.

Informed consent approach

The informed consent process must reflect the fundamental study design distinction of hypothesis-testing versus hypothesis-generating clinical genomics research. For the latter, the challenge is to help the research subjects understand that they are enrolling in a study that could lead to innumerable downstream research activities and goals. The informed consent process must be, like the research, iterative, and involve ongoing communication and consent with respect to those downstream activities.

Issue #2: Volume and nature of information

Whole genome sequencing can elucidate an enormous number of variations for a given individual. A typical whole genome sequence yields ~4,000,000 sequence variations. A whole exome sequence limits the interrogation to the coding regions of genes (about 1–1.5% of the genome) and generates typically 30,000-50,000 gene variants. While most are benign or of unknown consequence, some are associated with a significant increased risk of disease for the individual and/or their family members. For example, the typical human is a carrier for three to five deleterious genetic variants or mutations that cause severe recessive diseases [ 17 , 18 ]. In addition, there are over 30 known cancer susceptibility syndromes, which in aggregate may affect more than 1/500 patients, and the sequence variants that cause these disorders can be readily detected with MPS. These variants can have extremely high relative risks. For some disorders, a rare variant can be associated with a relative risk of greater than 1,000. This is in contrast with common SNP typing which detects variants associated with small relative risks (typically on the order of 1.2-1.5). It is arguable whether the latter type of variant has any clinical utility as an individual test.

Conveying the full scope of genomic interrogation planned for each sample and the volume of information generated for a given participant is impossible. The goal and challenge in this instance is to give the participant as realistic a picture as possible of the likely amount of clinically actionable results the technology can generate. Our approach is two-fold: to give the subjects the clear message that the number and nature of the findings is enormous and literally impossible to describe in a comprehensive manner and to use illustrative examples of the spectrum of these results.

To provide examples, we bin genetic variants into broad categories, as follows: heterozygous carriers of genetic variants implicated in recessive conditions (e.g., CFTR p.Phe508del and cystic fibrosis); variants that cause a treatable disorder that may be present, but asymptomatic or undiagnosed (e.g., LDLR p.Trp87X, familial hypercholesterolemia); variants that predispose to later-onset conditions (e.g., BRCA2 c.5946delT (commonly known as c.6174delT), breast and ovarian cancer susceptibility); variants that predispose to late-onset but untreatable disorders (e.g., frontotemporal dementia MAPT p.Pro301Leu).

Additionally, the scale and scope of the results determines a near certainty that all participants will be found to harbor disease-causing mutations. This is because the interrogation of all genes brings to light the fact that the average human carries 3–5 recessive deleterious genes in addition to the risks for later onset or incompletely penetrant dominant disorders. This reality can be unsettling and surprising to research subjects and we believe it is important to address this early in the process, not downstream in the iterative phase. It is essential for the participants to choose whether MPS research is appropriate for them, taking into account their personal views and values.

Communicate to participants both the overwhelming scale and scope of genetic results they may opt to receive and provide them with specific disease examples that illustrate the kinds of decisions they may need to make as the results become available. These examples should also assist the research subjects in making a decision about whether to participate in the study and if so, the kinds of decisions they may need be making in the future as results become available.

Issue #3: Return of individual genotype results

The return of individual genotype results from MPS presents a new challenge in the clinical research environment, again because of the scale and breadth of the results. The genetic and medical counseling can be challenging because of the volume of results generated, participants’ expectations, the many different categories of results, and the length of time for the information to be available. We suggest that the most reasonable practice is to take a conservative approach and disclose only clinically actionable results. To this end, the absence of a deleterious gene variant (or a negative result) would not be disclosed to research participants. It is our understanding that it is mandatory to validate any individual results that are returned to research subjects in a CLIA-certified laboratory. Using current clinical practice as a standard or benchmark, we suggest that until other approaches are shown to be appropriate and effective, disclosure should take place during a face-to-face encounter involving a multidisciplinary team (geneticist, genetic counselor, and specialists on an ad-hoc basis based on the phenotype in question).

During the initial consent, participants are alerted to the fact that in the future the study team will contact them by telephone and their previously-stated preferences and impressions about receiving primary and secondary variant results will be reviewed. The logistics and details of this future conversation feature prominently in the initial informed consent session, as it is challenging to make and to receive such calls. Participants make a choice to learn or not learn a result each time a result becomes available. Once a participant makes the decision to learn a genotype result, the variant is confirmed in a CLIA lab, and a report is generated. The results are communicated to the participant during a face-to-face meeting with a geneticist and genetic counselor, and with the participation of other specialists depending on the case and the participant’s preferences. These phone discussions are seen as an extension of the initial informed consent process and as opportunities for the participants to make decisions in a more relevant and current context (compared to the original informed consent session). We see this as an iterative approach to consent, also known as circular consent [ 5 ]. Participants who opt not to learn a specific result can still be contacted later if other results become available, unless they choose not to be contacted by us any longer.

This approach to returning results is challenged by the hypothesis-generating genomics research approach. Participants in our hypothesis-testing protocol are not asked to make a decision about learning individual genotype results at the time of consent. This is because we cannot know the nature of the future potential finding at the time of the original consent. Rather, they are engaged in a discussion of what they currently imagine their preferences might be at some future date, again using exemplar disorders and hypothetical scenarios of hypothesis-generating studies.

In the hypothesis-generating study, we have distinct approaches for variants in known disease-causing genes versus variants in genes that are hypothesized to cause disease (the latter being the operative hypothesis generating activity). For the former, the results are handled in a manner quite similar to the hypothesis-testing study. In the latter case, the participant may be asked if they would be willing to return for further phenotyping to help us determine the nature of the variant of uncertain clinical significance (VUCS). The participant is typically informed that they have a sequence variant and that we would like to learn, through clinical research whether this variant has any phenotypic or clinical significance. It is emphasized that current knowledge does not show that the variant causes any phenotype and the chances are high that the variant is benign. However, neither the gene nor the sequence variant is disclosed and the research finding is not confirmed in a CLIA certified lab. This type of VUCS would only be communicated back to the participant if the clinical research showed that the variant was causative, and the return of the result was determined medically appropriate by our Mutation Advisory Committee, and following confirmation in a CLIA-certified laboratory.

For the return of mutations in known, disease causing genes, the initial consent cannot comprehensively inform subjects of the nature of the diseases, because of the scale and scope of the potential results. Instead, exemplars are given to elicit general preferences, which are then affirmed or refined at the time results are available. Hypothesis-generating studies require that subjects receive sufficient information to make an informed choice about participation in the specific follow-up study, with return of individual results only if the cause and effect relationship is established, with appropriate oversight.

Issue #4: Duty to warn

Given the breadth of MPS gene interrogation, it is reasonable to anticipate that occasional participants may have mutations that pose a likely severe negative consequence, which we classify as “panic” results. This models clinical and research practice for the return of results such as a pulmonary mass or high serum potassium level. In contrast to the above-mentioned autosomal recessive carrier states that are expected to be nearly universal, genetic panic results should be uncommon. However, they should not be considered as unanticipated – it is obvious that such variants will be detected and the informed consent process should anticipate these. Examples would be deleterious variants for malignant hyperthermia or Long QT Syndrome, either of which have a substantial risk of sudden death and the risk can be mitigated.

Both our hypothesis-testing and hypothesis-generating studies include mechanisms for the participants to indicate the types of results that they wish to have returned to them. In the hypothesis-testing mode of research this is primarily to respect the autonomy of the participants, but in addition, for the hypothesis-generating study we are assessing the motivations and interests of the subjects in various types of results and manipulating the return of results as an experimental aim. It is our clinical research experience that participants are challenged by making decisions regarding possible future results that are rare, but potentially severe. As well, the medical and social contexts of the subjects evolves over time and the consent that was obtained at enrollment may not be relevant or appropriate at a later time when such a result arises. This is particularly relevant for a research study that is ongoing for substantial periods of time (see also point #7, below).

To address these issues we have consented the subjects to the potential return of “panic” results, irrespective of their preferences at the initial consent session. In effect, the consent process is for some participants a consent to override their preference.

In both hypothesis-testing and hypothesis-generating research it is important to outline circumstances in which researchers’ duty-to-warn may result in a return of results that may be contrary to the preferences of the subject. It is essential that the subjects understand this approach to unusually severe mutation results. Subjects who are uncomfortable with this approach to return of results are encouraged to decline enrollment.

Issue #5: Length of researcher and participant interaction

Approaches to MPS data are evolving rapidly and it is anticipated that this ongoing research into the significance of DNA variants will continue for years or decades. The different purposes of the two study designs lead to different endpoints in terms of researcher’s responsibility to analyze results. In our hypothesis-testing research, discussion of the relationship of the participants to the researchers is framed in terms of the discovery of the primary variant. We ask participants to be willing to interact with us for a period of months or years as it is impossible for to set a specific timeline to determine the cause of the disorder under investigation (if it ever discovered). While attempts to elucidate the primary variant are underway, participants’ genomic data are periodically annotated using the most current bioinformatic methodologies available. We conceptualize our commitment to return re-annotated and updated results to participants as diminishing, but not disappearing, after this initial results’ disclosure. As the primary aim of the study has been accomplished, less attention will be directed to the characterization of ancillary genomic data, yet we believe we retain an obligation to share highly clinically actionable findings with participants should we obtain them.

In the hypothesis-generating study the researcher’s responsibility to annotate participants’ genomes/exomes is ongoing. This is ongoing because, as noted above, one of the experimental aims is to study the motivations and interests of the subjects in these types of results. Determining how this motivation and interest fares over time is an important research goal. During the informed consent discussion it is emphasized that the iterative nature of result interpretation will lead to multiple meetings for the disclosure of clinically actionable results, and that the participant may be contacted months or years after the date of enrollment. Additionally, it is outlined that the participant will make a choice about learning the result each time he/she is re-contacted about the availability of a research finding, and that finding will only be confirmed in a CLIA-certified laboratory if the participant opts to learn the information. Participants who return to discuss results are reminded that they will be contacted in the future if and when other results deemed to be clinically actionable are found for that individual.

Describe nature, mutual commitments, and duration of researcher-participant relationship to participants. For hypothesis-testing studies it is appropriate that the intensity of the clinical annotation of secondary variants may decline when the primary goal of the study is met. For hypothesis-generating studies, such interactions may continue for as long as there are variants to be further evaluated and as long as the subject retains an interest in the participation.

Issue #6: Target population

The informed consent process needs to take into account the target population in terms of their disease phenotype, age, and whether the goal is to enroll individual participants or families. These considerations represent the greatest divergence in approaches to informed consent when comparing hypothesis-testing and hypothesis-generating research. In our two studies, the hypothesis-testing study focuses on rare diseases and often family participation, whereas the hypothesis-generating study focuses on more common diseases and unrelated index cases. There are an infinite number of study designs and investigators may adapt our approaches to informed consent for their own designs.

Our hypothesis-testing protocol enrolls both individual participants and families (most commonly trios), the latter being more common. In hypothesis-testing research, many participants are either affected by a genetic disease or are a close relative (typically a parent) of a person with a genetic disease. The research participants must weigh their hope for, and personal meaning ascribed to, learning the genetic cause for their disorder against the possibility of being in a position to learn a significant amount of unanticipated information. Discussing and addressing the potential discrepancy of the participants’ expectations of the value of their results and what they may realistically stand to learn (both desired and undesired information) is a central component of the informed consent process.

In our hypothesis-testing protocol, when parents are consenting on behalf of a minor child, we review with them the issues surrounding genetic testing of children and discuss their attitudes regarding their child’s autonomy and their parental decision-making values. Because family trios (most often mother-father-child) are enrolled together, we discuss how one individual’s preferences regarding results may be disrupted or superseded by another family member’s choice and communication of that individual’s knowledge.

In contrast, our hypothesis-generating protocol enrolls as probands or primary participants older, unrelated individuals [ 19 ]. Most participants are self-selected in terms of their decision to enroll and are not enrolled because they or a relative have a rare disease. Participants in the hypothesis-generating protocol are consented for future exploration of any and all possible phenotypes. This is a key distinguishing feature of this hypothesis-generating approach to research, which is a different paradigm – going from genotype to phenotype. The participants may be invited for additional phenotyping. In fact, multiple satellite studies are ongoing to evaluate various subsets of participants for different phenotypes. The key with the consent for these subjects is to initially communicate to the subjects the general approach – that their genome will be explored, variations will be identified, and they may be re-contacted for a potential follow-up study to understand the potential relationship of that variant to their phenotype. These subsequent consents for follow-up studies are considered an iterative consent process, which is similar to the Informed Cohort concept [ 20 ].

Hypothesis-generating research is a novel approach to clinical research design and requires an ongoing, iterative approach to informed consent. For hypothesis-testing research a key informed consent issue is for the subjects to balance the desire for information on the primary disease causing mutation with the pros and cons of obtaining possibly undesired information on secondary variants.

Issue #7: Privacy and confidentiality

In MPS studies, privacy and confidentiality is a complex and multifaceted issue. Some potential challenges include: the deposition of genetic and phenotypic data in public databases, the placement of CLIA-validated results in the individual’s medical chart, and the discovery of secondary variants in relatives of affected probands in family-based (typically hypothesis-testing) research.

The field of genomics has a tradition of deposition of data in publicly accessible databases. Participants in our protocols are informed that the goal of sharing de-identified information in public databases is to advance research, and that there are methods in place maximize the privacy and confidentiality of personally identifiable information. However, the deposition of genomic-scale data for an individual participant, such as a MPS sequence, is far above the minimal amount of data to uniquely identify the sample [ 21 , 22 ]. Therefore, the participants should be made aware that the scale of the data could allow analysts to connect sequence data to individuals by matching variants in the deposited research data to other data from that person. As well, the public deposition of data in some cases is an irrevocable decision. Once the data are deposited and distributed, it may be impossible to remove the data from all computer servers, should the subject decide to withdraw from the study.

Additionally, participants are informed that once a result is CLIA-certified, that result is placed in the individual’s medical chart of the clinical research institution and may be accessible by third parties. Although there are state and federal laws to protect individuals against genetic discrimination, including GINA, this law has not yet been tested in the courts. This is explained to participants up front at the time of enrollment and a more detailed discussion takes place at the time of results disclosure. To offer additional protection in the event of a court subpoena, a Certificate of Confidentiality has been obtained in the hypothesis-testing and hypothesis-generating protocols. The discussion surrounding privacy and confidentiality is approached in a similar manner in both protocols.

The third issue regarding confidentiality is that MPS can generate many results in each individual and it is highly likely that some, if not all, of the variants detected in one research participant may be present in another research participant (e.g., a parent). This is again a consequence of the scale and breadth of MPS in that the large number of variants that can be detected in each participant makes it exceedingly likely that their relatives share many of these variants and that their genetic risks of rare diseases may be measurably altered. It is important to communicate to the participants that it is likely that such variants can be detected and that they may have implications for other members of the family, and that the consented individuals, or their parent may need to communicate those results to other members of the family.

The informed consent should include discussion of public deposition of data, the entry of CLIA-validated results into medical records, and the likely discovery of variants with implications for family members.

We describe an approach to the informed consent process as a mutual opportunity for researchers and participants to assess one another’s goals in MPS protocols that employ both hypothesis-generating and hypothesis-testing methodologies. The use of MPS in clinical research requires adaptation of established processes of human subjects protections. The potentially overwhelming scale of information generated by MPS necessitates that investigators and IRBs adapt traditional approaches to consent the subjects. Because nearly all subjects will have a clinically actionable result, investigators must implement thoughtful plan for consent regarding results disclosure, including setting a threshold for the types of information that should be disclosed to the participants.

While some of the informed consent issues for MPS are independent of the study design, others should be adapted based on whether the research study is employing MPS to test a hypothesis (i.e., find the cause of a rare condition in an affected cohort), or to generate hypotheses (i.e., find deleterious or potentially deleterious variants that warrant participant follow-up and further investigation). For example, the health-related attributes of the study cohort (healthy individuals versus disease patients) are likely to influence participants’ motivations and expectations of MPS, and in the case of a disease cohort create the need to dichotomize the genetic variants into primary and secondary. Conversely, issues inherent to MPS technology are central to the informed consent approach in both types of studies. The availability of MPS allows a paradigm shift in genetics research – no longer are investigators constrained to long-standing approaches of hypothesis-testing modes of research. The scale of MPS allows investigators to proceed from genotype to phenotype, and leads to new challenges for genetic and medical counseling. Research participants receiving results from MPS might not present with a personal and/or family history suggestive of conditions revealed by their genotypic variants, and consequently might not perceive their a priori risk to be elevated for those conditions.

Participants’ motivations to have whole genome/exome sequencing at this early stage are important to take into consideration in the informed consent process. Initial qualitative data suggest that individuals enroll in the hypothesis-generating study because of altruism in promoting research, and a desire to learn about genetic factors that contribute to their own health and disease risk [ 23 ]. Most participants expect that genomic information will improve the overall knowledge of disease causes and treatments. Moreover, data on research participants’ preferences to receive different types of genetic results suggest that they have strong intentions to receive all types of results [ 16 ]. However, they are able to discern between the types and quality of information they could learn, and demonstrate stronger attitudes to learn clinically actionable and carrier status results when compared to results that are uncertain or not clinically actionable. These findings provide initial insights into the value these early adopters place on information generated by high-throughput sequencing studies, and help us tailor the informed consent process to this group of individuals. However, more empirical data are needed to guide the informed consent process, including data on research participants’ ability to receive results for multiple disorders and traits.

Participants in both types of studies are engaged in a discussion of the complex and dynamic nature of genomic annotation so that they may make an informed decision about participation and may be aware of the need to revisit results learned at additional time points in the future. As well, we advocate a process whereby investigators retain some latitude with respect to the most serious, potentially life-threatening mutations. While it is mandatory to respect the autonomy of research subjects, this does not mean that investigators must accede to the research subject’s views of these “panic” results. In a paradoxical way, the research participant and the researcher can agree that the latter can maintain a small, but initially ambiguous degree of latitude with respect to these most serious variants. In the course of utilizing MPS technology for further elucidation of the genetic architecture of health and disease, it is imperative that research participants and researchers be engaged in a continuous discussion about the state of scientific knowledge and the types of information that could potentially be learned from MPS. Although resource-intensive, this “partnership model” [ 2 ] or informed cohort approach to informed consent promotes respect for participants, and allows evaluation of the benefits and harms of disclosure in a more timely and relevant manner.

We have here proposed a categorization of massively-parallel clinical genomics research studies as hypothesis-testing versus hypothesis-generating to help clarify the issue of so-called incidental or secondary results for the consent process, and aid the communication of the research goals to study participants. By using this categorization approach and considering seven important features of this kind of research (Primary versus secondary variant results and the open-ended nature of clinical genomics, Volume and nature of information, Return of individual genotype results, Duty to warn, Length of researcher and participant interaction, Target population, and Privacy and confidentiality) researchers can design an informed consent process that is open, transparent, and appropriately balances risks and benefits of this exciting approach to heritable disease research.

This study was supported by funding from the Intramural Research Program of the National Human Genome Research Institute. The authors have no conflicts to declare.

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Facio, F.M., Sapp, J.C., Linn, A. et al. Approaches to informed consent for hypothesis-testing and hypothesis-generating clinical genomics research. BMC Med Genomics 5 , 45 (2012). https://doi.org/10.1186/1755-8794-5-45

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2.4 Developing a Hypothesis

Learning objectives.

• Distinguish between a theory and a hypothesis.
• Discover how theories are used to generate hypotheses and how the results of studies can be used to further inform theories.
• Understand the characteristics of a good hypothesis.

Theories and Hypotheses

Before describing how to develop a hypothesis it is imporant to distinguish betwee a theory and a hypothesis. A  theory  is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes, functions, or organizing principles that have not been observed directly. Consider, for example, Zajonc’s theory of social facilitation and social inhibition. He proposed that being watched by others while performing a task creates a general state of physiological arousal, which increases the likelihood of the dominant (most likely) response. So for highly practiced tasks, being watched increases the tendency to make correct responses, but for relatively unpracticed tasks, being watched increases the tendency to make incorrect responses. Notice that this theory—which has come to be called drive theory—provides an explanation of both social facilitation and social inhibition that goes beyond the phenomena themselves by including concepts such as “arousal” and “dominant response,” along with processes such as the effect of arousal on the dominant response.

Outside of science, referring to an idea as a theory often implies that it is untested—perhaps no more than a wild guess. In science, however, the term theory has no such implication. A theory is simply an explanation or interpretation of a set of phenomena. It can be untested, but it can also be extensively tested, well supported, and accepted as an accurate description of the world by the scientific community. The theory of evolution by natural selection, for example, is a theory because it is an explanation of the diversity of life on earth—not because it is untested or unsupported by scientific research. On the contrary, the evidence for this theory is overwhelmingly positive and nearly all scientists accept its basic assumptions as accurate. Similarly, the “germ theory” of disease is a theory because it is an explanation of the origin of various diseases, not because there is any doubt that many diseases are caused by microorganisms that infect the body.

A  hypothesis , on the other hand, is a specific prediction about a new phenomenon that should be observed if a particular theory is accurate. It is an explanation that relies on just a few key concepts. Hypotheses are often specific predictions about what will happen in a particular study. They are developed by considering existing evidence and using reasoning to infer what will happen in the specific context of interest. Hypotheses are often but not always derived from theories. So a hypothesis is often a prediction based on a theory but some hypotheses are a-theoretical and only after a set of observations have been made, is a theory developed. This is because theories are broad in nature and they explain larger bodies of data. So if our research question is really original then we may need to collect some data and make some observation before we can develop a broader theory.

Theories and hypotheses always have this  if-then  relationship. “ If   drive theory is correct,  then  cockroaches should run through a straight runway faster, and a branching runway more slowly, when other cockroaches are present.” Although hypotheses are usually expressed as statements, they can always be rephrased as questions. “Do cockroaches run through a straight runway faster when other cockroaches are present?” Thus deriving hypotheses from theories is an excellent way of generating interesting research questions.

But how do researchers derive hypotheses from theories? One way is to generate a research question using the techniques discussed in this chapter  and then ask whether any theory implies an answer to that question. For example, you might wonder whether expressive writing about positive experiences improves health as much as expressive writing about traumatic experiences. Although this  question  is an interesting one  on its own, you might then ask whether the habituation theory—the idea that expressive writing causes people to habituate to negative thoughts and feelings—implies an answer. In this case, it seems clear that if the habituation theory is correct, then expressive writing about positive experiences should not be effective because it would not cause people to habituate to negative thoughts and feelings. A second way to derive hypotheses from theories is to focus on some component of the theory that has not yet been directly observed. For example, a researcher could focus on the process of habituation—perhaps hypothesizing that people should show fewer signs of emotional distress with each new writing session.

Among the very best hypotheses are those that distinguish between competing theories. For example, Norbert Schwarz and his colleagues considered two theories of how people make judgments about themselves, such as how assertive they are (Schwarz et al., 1991) [1] . Both theories held that such judgments are based on relevant examples that people bring to mind. However, one theory was that people base their judgments on the  number  of examples they bring to mind and the other was that people base their judgments on how  easily  they bring those examples to mind. To test these theories, the researchers asked people to recall either six times when they were assertive (which is easy for most people) or 12 times (which is difficult for most people). Then they asked them to judge their own assertiveness. Note that the number-of-examples theory implies that people who recalled 12 examples should judge themselves to be more assertive because they recalled more examples, but the ease-of-examples theory implies that participants who recalled six examples should judge themselves as more assertive because recalling the examples was easier. Thus the two theories made opposite predictions so that only one of the predictions could be confirmed. The surprising result was that participants who recalled fewer examples judged themselves to be more assertive—providing particularly convincing evidence in favor of the ease-of-retrieval theory over the number-of-examples theory.

Theory Testing

The primary way that scientific researchers use theories is sometimes called the hypothetico-deductive method  (although this term is much more likely to be used by philosophers of science than by scientists themselves). A researcher begins with a set of phenomena and either constructs a theory to explain or interpret them or chooses an existing theory to work with. He or she then makes a prediction about some new phenomenon that should be observed if the theory is correct. Again, this prediction is called a hypothesis. The researcher then conducts an empirical study to test the hypothesis. Finally, he or she reevaluates the theory in light of the new results and revises it if necessary. This process is usually conceptualized as a cycle because the researcher can then derive a new hypothesis from the revised theory, conduct a new empirical study to test the hypothesis, and so on. As  Figure 2.2  shows, this approach meshes nicely with the model of scientific research in psychology presented earlier in the textbook—creating a more detailed model of “theoretically motivated” or “theory-driven” research.

Figure 2.2 Hypothetico-Deductive Method Combined With the General Model of Scientific Research in Psychology Together they form a model of theoretically motivated research.

As an example, let us consider Zajonc’s research on social facilitation and inhibition. He started with a somewhat contradictory pattern of results from the research literature. He then constructed his drive theory, according to which being watched by others while performing a task causes physiological arousal, which increases an organism’s tendency to make the dominant response. This theory predicts social facilitation for well-learned tasks and social inhibition for poorly learned tasks. He now had a theory that organized previous results in a meaningful way—but he still needed to test it. He hypothesized that if his theory was correct, he should observe that the presence of others improves performance in a simple laboratory task but inhibits performance in a difficult version of the very same laboratory task. To test this hypothesis, one of the studies he conducted used cockroaches as subjects (Zajonc, Heingartner, & Herman, 1969) [2] . The cockroaches ran either down a straight runway (an easy task for a cockroach) or through a cross-shaped maze (a difficult task for a cockroach) to escape into a dark chamber when a light was shined on them. They did this either while alone or in the presence of other cockroaches in clear plastic “audience boxes.” Zajonc found that cockroaches in the straight runway reached their goal more quickly in the presence of other cockroaches, but cockroaches in the cross-shaped maze reached their goal more slowly when they were in the presence of other cockroaches. Thus he confirmed his hypothesis and provided support for his drive theory. (Zajonc also showed that drive theory existed in humans (Zajonc & Sales, 1966) [3] in many other studies afterward).

Incorporating Theory into Your Research

When you write your research report or plan your presentation, be aware that there are two basic ways that researchers usually include theory. The first is to raise a research question, answer that question by conducting a new study, and then offer one or more theories (usually more) to explain or interpret the results. This format works well for applied research questions and for research questions that existing theories do not address. The second way is to describe one or more existing theories, derive a hypothesis from one of those theories, test the hypothesis in a new study, and finally reevaluate the theory. This format works well when there is an existing theory that addresses the research question—especially if the resulting hypothesis is surprising or conflicts with a hypothesis derived from a different theory.

To use theories in your research will not only give you guidance in coming up with experiment ideas and possible projects, but it lends legitimacy to your work. Psychologists have been interested in a variety of human behaviors and have developed many theories along the way. Using established theories will help you break new ground as a researcher, not limit you from developing your own ideas.

Characteristics of a Good Hypothesis

There are three general characteristics of a good hypothesis. First, a good hypothesis must be testable and falsifiable . We must be able to test the hypothesis using the methods of science and if you’ll recall Popper’s falsifiability criterion, it must be possible to gather evidence that will disconfirm the hypothesis if it is indeed false. Second, a good hypothesis must be  logical. As described above, hypotheses are more than just a random guess. Hypotheses should be informed by previous theories or observations and logical reasoning. Typically, we begin with a broad and general theory and use  deductive reasoning to generate a more specific hypothesis to test based on that theory. Occasionally, however, when there is no theory to inform our hypothesis, we use  inductive reasoning  which involves using specific observations or research findings to form a more general hypothesis. Finally, the hypothesis should be  positive.  That is, the hypothesis should make a positive statement about the existence of a relationship or effect, rather than a statement that a relationship or effect does not exist. As scientists, we don’t set out to show that relationships do not exist or that effects do not occur so our hypotheses should not be worded in a way to suggest that an effect or relationship does not exist. The nature of science is to assume that something does not exist and then seek to find evidence to prove this wrong, to show that really it does exist. That may seem backward to you but that is the nature of the scientific method. The underlying reason for this is beyond the scope of this chapter but it has to do with statistical theory.

Key Takeaways

• A theory is broad in nature and explains larger bodies of data. A hypothesis is more specific and makes a prediction about the outcome of a particular study.
• Working with theories is not “icing on the cake.” It is a basic ingredient of psychological research.
• Like other scientists, psychologists use the hypothetico-deductive method. They construct theories to explain or interpret phenomena (or work with existing theories), derive hypotheses from their theories, test the hypotheses, and then reevaluate the theories in light of the new results.
• Practice: Find a recent empirical research report in a professional journal. Read the introduction and highlight in different colors descriptions of theories and hypotheses.
• Schwarz, N., Bless, H., Strack, F., Klumpp, G., Rittenauer-Schatka, H., & Simons, A. (1991). Ease of retrieval as information: Another look at the availability heuristic.  Journal of Personality and Social Psychology, 61 , 195–202. ↵
• Zajonc, R. B., Heingartner, A., & Herman, E. M. (1969). Social enhancement and impairment of performance in the cockroach.  Journal of Personality and Social Psychology, 13 , 83–92. ↵
• Zajonc, R.B. & Sales, S.M. (1966). Social facilitation of dominant and subordinate responses. Journal of Experimental Social Psychology, 2 , 160-168. ↵

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

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

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

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

Research Hypothesis 101

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

What is a hypothesis?

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

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

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

Hypothesis: sleep impacts academic performance.

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

But that’s not good enough…

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

What is a research hypothesis?

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

Let’s take a look at these more closely.

Need a helping hand?

Hypothesis Essential #1: Specificity & Clarity

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

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

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

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

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

Hypothesis Essential #2: Testability (Provability)

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

For example, consider the hypothesis we mentioned earlier:

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

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

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

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

Defining A Research Hypothesis

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

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

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

What about the null hypothesis?

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

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

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

And there you have it – hypotheses in a nutshell. 

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

Psst... there’s more!

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

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16 Comments

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

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

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

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

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

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

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

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

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

could you please elaborate it more

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

This is very helpful

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

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

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

this is very important note help me much more

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How to Write a Great Hypothesis

Hypothesis Definition, Format, Examples, and Tips

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

Amy Morin, LCSW, is a psychotherapist and international bestselling author. Her books, including "13 Things Mentally Strong People Don't Do," have been translated into more than 40 languages. Her TEDx talk,  "The Secret of Becoming Mentally Strong," is one of the most viewed talks of all time.

Verywell / Alex Dos Diaz

• The Scientific Method

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• Operationalization

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Hypotheses examples.

• Collecting Data

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

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

At a Glance

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

The Hypothesis in the Scientific Method

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

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

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

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

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

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

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

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

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

Elements of a Good Hypothesis

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

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

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

How to Formulate a Good Hypothesis

To form a hypothesis, you should take these steps:

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

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

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

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

The Importance of Operational Definitions

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

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

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

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

Replicability

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

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

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

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

Hypothesis Checklist

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

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

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

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

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

A few examples of simple hypotheses:

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

Examples of a complex hypothesis include:

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

Examples of a null hypothesis include:

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

Examples of an alternative hypothesis:

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

Collecting Data on Your Hypothesis

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

Descriptive Research Methods

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

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

Experimental Research Methods

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

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

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

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

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

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

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

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

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

Research Hypothesis In Psychology: Types, & Examples

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

A research hypothesis, in its plural form “hypotheses,” is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method .

Hypotheses connect theory to data and guide the research process towards expanding scientific understanding

Some key points about hypotheses:

• A hypothesis expresses an expected pattern or relationship. It connects the variables under investigation.
• It is stated in clear, precise terms before any data collection or analysis occurs. This makes the hypothesis testable.
• A hypothesis must be falsifiable. It should be possible, even if unlikely in practice, to collect data that disconfirms rather than supports the hypothesis.
• Hypotheses guide research. Scientists design studies to explicitly evaluate hypotheses about how nature works.
• For a hypothesis to be valid, it must be testable against empirical evidence. The evidence can then confirm or disprove the testable predictions.
• Hypotheses are informed by background knowledge and observation, but go beyond what is already known to propose an explanation of how or why something occurs.

Predictions typically arise from a thorough knowledge of the research literature, curiosity about real-world problems or implications, and integrating this to advance theory. They build on existing literature while providing new insight.

Types of Research Hypotheses

Alternative hypothesis.

The research hypothesis is often called the alternative or experimental hypothesis in experimental research.

It typically suggests a potential relationship between two key variables: the independent variable, which the researcher manipulates, and the dependent variable, which is measured based on those changes.

The alternative hypothesis states a relationship exists between the two variables being studied (one variable affects the other).

A hypothesis is a testable statement or prediction about the relationship between two or more variables. It is a key component of the scientific method. Some key points about hypotheses:

• Important hypotheses lead to predictions that can be tested empirically. The evidence can then confirm or disprove the testable predictions.

In summary, a hypothesis is a precise, testable statement of what researchers expect to happen in a study and why. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.

An experimental hypothesis predicts what change(s) will occur in the dependent variable when the independent variable is manipulated.

It states that the results are not due to chance and are significant in supporting the theory being investigated.

The alternative hypothesis can be directional, indicating a specific direction of the effect, or non-directional, suggesting a difference without specifying its nature. It’s what researchers aim to support or demonstrate through their study.

Null Hypothesis

The null hypothesis states no relationship exists between the two variables being studied (one variable does not affect the other). There will be no changes in the dependent variable due to manipulating the independent variable.

It states results are due to chance and are not significant in supporting the idea being investigated.

The null hypothesis, positing no effect or relationship, is a foundational contrast to the research hypothesis in scientific inquiry. It establishes a baseline for statistical testing, promoting objectivity by initiating research from a neutral stance.

Many statistical methods are tailored to test the null hypothesis, determining the likelihood of observed results if no true effect exists.

This dual-hypothesis approach provides clarity, ensuring that research intentions are explicit, and fosters consistency across scientific studies, enhancing the standardization and interpretability of research outcomes.

Nondirectional Hypothesis

A non-directional hypothesis, also known as a two-tailed hypothesis, predicts that there is a difference or relationship between two variables but does not specify the direction of this relationship.

It merely indicates that a change or effect will occur without predicting which group will have higher or lower values.

For example, “There is a difference in performance between Group A and Group B” is a non-directional hypothesis.

Directional Hypothesis

A directional (one-tailed) hypothesis predicts the nature of the effect of the independent variable on the dependent variable. It predicts in which direction the change will take place. (i.e., greater, smaller, less, more)

It specifies whether one variable is greater, lesser, or different from another, rather than just indicating that there’s a difference without specifying its nature.

For example, “Exercise increases weight loss” is a directional hypothesis.

Falsifiability

The Falsification Principle, proposed by Karl Popper , is a way of demarcating science from non-science. It suggests that for a theory or hypothesis to be considered scientific, it must be testable and irrefutable.

Falsifiability emphasizes that scientific claims shouldn’t just be confirmable but should also have the potential to be proven wrong.

It means that there should exist some potential evidence or experiment that could prove the proposition false.

However many confirming instances exist for a theory, it only takes one counter observation to falsify it. For example, the hypothesis that “all swans are white,” can be falsified by observing a black swan.

For Popper, science should attempt to disprove a theory rather than attempt to continually provide evidence to support a research hypothesis.

Can a Hypothesis be Proven?

Hypotheses make probabilistic predictions. They state the expected outcome if a particular relationship exists. However, a study result supporting a hypothesis does not definitively prove it is true.

All studies have limitations. There may be unknown confounding factors or issues that limit the certainty of conclusions. Additional studies may yield different results.

In science, hypotheses can realistically only be supported with some degree of confidence, not proven. The process of science is to incrementally accumulate evidence for and against hypothesized relationships in an ongoing pursuit of better models and explanations that best fit the empirical data. But hypotheses remain open to revision and rejection if that is where the evidence leads.

• Disproving a hypothesis is definitive. Solid disconfirmatory evidence will falsify a hypothesis and require altering or discarding it based on the evidence.
• However, confirming evidence is always open to revision. Other explanations may account for the same results, and additional or contradictory evidence may emerge over time.

We can never 100% prove the alternative hypothesis. Instead, we see if we can disprove, or reject the null hypothesis.

If we reject the null hypothesis, this doesn’t mean that our alternative hypothesis is correct but does support the alternative/experimental hypothesis.

Upon analysis of the results, an alternative hypothesis can be rejected or supported, but it can never be proven to be correct. We must avoid any reference to results proving a theory as this implies 100% certainty, and there is always a chance that evidence may exist which could refute a theory.

How to Write a Hypothesis

• Identify variables . The researcher manipulates the independent variable and the dependent variable is the measured outcome.
• Operationalized the variables being investigated . Operationalization of a hypothesis refers to the process of making the variables physically measurable or testable, e.g. if you are about to study aggression, you might count the number of punches given by participants.
• Decide on a direction for your prediction . If there is evidence in the literature to support a specific effect of the independent variable on the dependent variable, write a directional (one-tailed) hypothesis. If there are limited or ambiguous findings in the literature regarding the effect of the independent variable on the dependent variable, write a non-directional (two-tailed) hypothesis.
• Make it Testable : Ensure your hypothesis can be tested through experimentation or observation. It should be possible to prove it false (principle of falsifiability).
• Clear & concise language . A strong hypothesis is concise (typically one to two sentences long), and formulated using clear and straightforward language, ensuring it’s easily understood and testable.

Consider a hypothesis many teachers might subscribe to: students work better on Monday morning than on Friday afternoon (IV=Day, DV= Standard of work).

Now, if we decide to study this by giving the same group of students a lesson on a Monday morning and a Friday afternoon and then measuring their immediate recall of the material covered in each session, we would end up with the following:

• The alternative hypothesis states that students will recall significantly more information on a Monday morning than on a Friday afternoon.
• The null hypothesis states that there will be no significant difference in the amount recalled on a Monday morning compared to a Friday afternoon. Any difference will be due to chance or confounding factors.

More Examples

• Memory : Participants exposed to classical music during study sessions will recall more items from a list than those who studied in silence.
• Social Psychology : Individuals who frequently engage in social media use will report higher levels of perceived social isolation compared to those who use it infrequently.
• Developmental Psychology : Children who engage in regular imaginative play have better problem-solving skills than those who don’t.
• Clinical Psychology : Cognitive-behavioral therapy will be more effective in reducing symptoms of anxiety over a 6-month period compared to traditional talk therapy.
• Cognitive Psychology : Individuals who multitask between various electronic devices will have shorter attention spans on focused tasks than those who single-task.
• Health Psychology : Patients who practice mindfulness meditation will experience lower levels of chronic pain compared to those who don’t meditate.
• Organizational Psychology : Employees in open-plan offices will report higher levels of stress than those in private offices.
• Behavioral Psychology : Rats rewarded with food after pressing a lever will press it more frequently than rats who receive no reward.

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What Is A Passive Income And How Can You Earn It With A Full Time Job

Passive income may seem mysterious but there are many ways to achieve it.

According to American entrepreneur Grant Cordone, passive income is the key to building wealth. No matter how many hours you work, it may be impossible to reach your financial goals with a 9-5 job. Many people have their money work for them with passive income as a strategy. Passive income requires you to play the long game and is an investment over a period of time so if you are looking for a get rich quick scheme, this is not for you. Here are a few ways to earn a passive income with a full-time job with thoughts and real-life case studies of those who are putting in the passive income work.

There are quite a few ways to create passive income streams.

What is Passive Income?

Imagine waking up to notifications informing you of the money you made while you were asleep? That is the definition of passive income. It allows you to make money without actively working in real time. “Passive income, tends to be misunderstood by those who don't generate it, and is very real in an era dominated by automation and AI tools. Today, it's simpler than ever to create and distribute value using tools like open source software, free social networks, and affordable web hosting, which enable virtually anyone, from teenagers to seniors, to generate what I tend to call 'automated income.' Denying the existence of passive income fundamentally ignores the current reality where AI and an abundance of free and affordable tools give people unprecedented ability to produce value at scale,” says Roberto Blake, Founder of Awesome Creator Academy and Author of : Create Something Awesome: How Creators are Profiting from Their Passion in the Creator Economy.

Ideally, the minimal effort you put into a passive income is to create the business venture you use to generate it. If you want a life where the money you make is not determined by the hours you work, start building a passive income today.

Passive income often means nurturing a process for a while before it runs on its own.

“Passive income is a smart way to build diversification of your income streams. It enables you to stop relying on one single job and provides a safety net during economic downturns,” offers Brooke Sellas, CEO at B Squared Media. Conversely, Phil Pallen, a brand strategist and content creator, says, “There’s nothing “passive” about the process of building passive income streams—it can be long hours on top of an existing workload. However, moving away from the dependency on one income source can lead to a happier life with less pressure.” And Sol Orwell, co-founder at Examine, adds on “I've made a large chunk of passive income. To be honest, the work to get there was likely spent on doing work that would have made a non-passive income business heh. Plus, with Google's finickiness, it's much harder than before.”

There are many ways to earn a passive income digitally.

Do Your Research Into Passive Income Streams

There exist many ways to create your own passive income streams and success can be found in ways that are unexpected. Do your due diligence to make sure that you are picking the right opportunity for you and your financial goals. Ratana, a voice actor and performance coach, cautions “Passive income can be a misnomer. Most of us hear the phrase and think “set it and forget it.” It might be more like “set it and periodically reassess it.” I think even when you have products or services that you offer perennially, as a business owner you still have to review, service and adjust them, to make sure they are a fit for your customers and your business over the long term.” Shelly at Shelly Saves the Day goes further along this line of thought with “Passive income is always talked about as if it requires very little work to get massive returns. The truth is, while you can 'make money while you sleep,' what they fail to tell you is that it will take many sleepless nights to get to that point.”

Cynthia Griffiths adds on "Passive income can be a rewarding way to explore creativity in a manner that cultivates connection with special interest audiences - but despite the misleading buzz name, any passive income stream *does* require hard work. What I’ve learned designing stickers, t-shirts, and home goods, has led to additional income but also served as a thought exercise that became a force multiplier for career development in my day job marketing for video games.”

On the other hand, B. Dave Walters, writer and content creator, says, “I got my start in the insurance business, where passive income was the name of the game; those small residuals eventually built up into something significant. Now, I'm looking for some sort of back-end participation in any project I'm a part of.”

Passive income requires quite a bit of set-up before fruition.

And he’s not the only one. Christina Garnett, Founder and Fractional CCO at Pocket CCO, says “Passive income provides an opportunity for those who see a hole in the market and might struggle to scale their other work options. For creators and knowledge workers especially, passive income offers a way for them to share their knowledge through courses, books, and other materials that need to only be created once.”

“Passive income has never been my main source of revenue, but it’s such a powerful way to supplement your income when you’re freelancing or running your own business. As someone who’s largely sold physical goods, it’s been a fun challenge to dream up digital/passive income opportunities (like selling digital versions of designs, writing ebooks, maintaining virtual subscription platforms like Patreon, etc!),” enthuses Jordan Dené Ellis, founder of Jordandené and The Sartorial Geek.

There are thousands of passive income streams you can start, but here are a few of the most popular ways to create income on the side.

Properly set up, a passive income stream can pay for years to come.

Create Digital Assets

Use your skills and expertise to create digital products such software applications, online courses, and eBooks. Once created with the right kind of thoughtful marketing, you can create a very successful passive income stream. “Having digital comics and TTRPG adventures available for sale gives me the passive income that can often make or break a month. Honestly, I wish I had learned about it at a younger age or in art school because good business practices are art an within itself,” says Jen Vaughn, Cartoonist & Narrative Designer.

Andrea Casanova, creatorpreneur and viral marketer adds, “In today's ‘knowledge’ economy, the traditional 9-5 income model has evolved. Anyone can monetize their expertise through mini-courses, downloadable content, or building online communities. The possibilities are limitless.” Michelle Mitchell, Executive Producer at TEDx LongBeach Salon, focuses on how it can help a segment of people by offering “Increasingly, women entrepreneurs are turning to digital products for passive income, such as digital planners. Creating digital planners offers several advantages that make them appealing to creators and consumers alike. The pros are low overhead costs, scalability, ease of distribution, and automation.”

Passive income can rely on saving and reinvesting over time.

Digital assets seem to ring true as Audrey Boyle, CEO at Social Proxy says “As a creator, I prioritize reusable digital assets. The true time drain lies in constantly generating unique creative work – reusability is the key to unlocking passive income.” “Passive income has become an important part of my creative independence, allowing me to pursue my own projects and only take on client projects that I’m genuinely interested in. The freedom I’ve gained is invaluable, especially as I’m now needing to plan my own maternity leave. It takes time and effort to build, but it feels like a natural part of my work and what I already enjoy sharing,” says Mimi Chao, owner-illustrator of Mimochai.

“For creators, speakers, coaches, and knowledge entrepreneurs, passive income can exist in the form of selling digital products (think Kajabi), monetizing content (YouTube + TikTok), writing a book, and more. It allows for the opportunity to turn income from a linear, input-output relationship to one that may become exponential, even more so when you apply the principles of compounding,” offers Jerry Won, Founder, World Class Speakers.

Passive income can help bridge the gap when a full-time job may not cover all expenses.

Monetize Talents and Hobbies

What do you enjoy outside of your 9 to 5? Art, singing, photography? Capitalize on your talents and hobbies and use them to begin to make extra money. There’s a variety of platforms where you can create and list physical products or have a dedicated audience sponsor your journey. These sites include Etsy, Patreon, Upwork, Fiverr and more. “I sell 3D models online on the side. It’s a passion of mine and the passive income gives me a sense of some stability and peace of mind,” says Sallia, Project Director at Paper Triangles. Another 3D creative and influencer, Joe "joemag" Magdalena, offers “ I have found that maintaining a passive income relies on a lot of active steps. Whether it was shipping Etsy orders, making content, or updating my Patreon I still need to be involved in keeping the plates spinning. If you take a back seat it quickly falls apart.”

On the other side of the creative coin, there are many alternatives. “As a filmmaker, writer, actor, and creative wearing many hats, navigating the gig economy means building multiple streams of income. Passive income sources like residuals and backend points are essential, providing a financial cushion that sustains and fuels my creativity between active projects,” offers Giovannie Espiritu, Telly-winning Director and Writer. “Designing patterns for Spoonflower and setting up a print on demand shop using Big Cartel have been low lift ways for me to dip my toes into making passive income while pushing myself creatively. Yes, not everything will translate into sales, but it's a fun exercise in gaining extra income while pushing your artistic curiosity,” says Emily Corbin, community manager and creative.

Passive income can appear as a windfall out-of-the-blue to outsides but takes work to set up.

Real Estate Investing

Whether through residential or commercial properties, investing in real estate is a great way to generate cash flow while you sleep. “Diversified passive income not only ensures financial resilience but also optimizes earning potential. By leveraging multiple income sources, you reduce risk and significantly enhance your capacity to achieve true financial freedom,” says Derral Eves, Youtube strategist. “There are two keys to being successful at building passive income: Patience & Persistent. Patience to allow for your efforts to grow and compound, and Persistent referring to a focus on recurring revenue streams,” offers Mike Allton, Strategic Marketing Leader in AI and Data-Driven Solutions at The Social Media Hat.

Invest in Stocks

Instead of putting your money into a savings account, invest some of it into dividend paying stocks. There’s also many other ways to do modest investing such as CDs or high interest rate savings accounts. Savvy investors know how to turn their investments into a reliable source of income.

“Passive income is money made when you don’t have to work. A few years ago I started investing into ETF funds that paid a monthly dividend. I liked this because it felt like I was getting a monthly income — and I knew if I worked hard I could get to a place where the income matched my living expenses,” offers Thomas Ma, Co-founder of Sapphire Studios.

Passive income has a few downsides that can occasionally outweigh the positives.

Work After Work

The last thing most people want to do after returning home from a hard day’s work is to get back on the computer and keep working. However, if you can’t leave your job because you have too many bills to pay, so the only way to earn a passive income is to work after work. Think about it like this, would you rather work a 9-5 until you retire while living a more mediocre lifestyle, or put in the extra hours for a few years, retire early and live a more fulfilling life?

Motivational speaker Les Brown once said, “You must be willing to do things today others won’t do, in order to have the things tomorrow others won’t have.” In other words, how successful you become is entirely up to what you are prepared to do. Brandon Groce, Co-founder of newform.community, focuses on the community side and says “Passive income is much easier to achieve when you start by building a community. By focusing on the needs of my audience first, I’ve been able to create products/merch that truly meet them where they are & solve their problems, leading to passive income. It’s all about putting the audience first and letting the products follow.” Also on the community-focused side, Chantel Soumis, Head of Marketing at Steelhead Technologies says “Having a circle of incredible humans is amazing, especially when you sing their praises to everyone you know. I have had a very lucrative passive income just from referring my friends to folks in the industry... when word of mouth is the strongest form of marketing, referrals go a long ways! My bank account gets a boost and I don't have to lift a finger. How great is that?”

Passive income requires strategy over time.

Working full-time while building a passive income may seem daunting, but with the right mind-set and strategy, it is more than possible.

“Passive income doesn’t just happen overnight; it takes building a strong community and delivering high-quality content that provides value. As the founder of XYZ Media, I leverage my expertise in media and marketing to build community through educational digital products and evergreen lifestyle, beauty, and food content on channels like Yummmier, inspiring individuals to elevate everyday life,” says XiXi Yang, Founder and CEO of XYZ Media.

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Scientists are testing mRNA vaccines to protect cows and people against bird flu

FILE - Cows stand in the milking parlor of a dairy farm in New Vienna, Iowa, on Monday, July 24, 2023. The bird flu outbreak in U.S. dairy cows is prompting development of new, next-generation mRNA vaccines — akin to COVID-19 shots — that are being tested in both animals and people. In June 2024, the U.S. Agriculture Department is to begin testing a vaccine developed by University of Pennsylvania researchers by giving it to calves. (AP Photo/Charlie Neibergall, File)

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The bird flu outbreak in U.S. dairy cows is prompting development of new, next-generation mRNA vaccines — akin to COVID-19 shots — that are being tested in both animals and people.

Next month, the U.S. Agriculture Department is to begin testing a vaccine developed by University of Pennsylvania researchers by giving it to calves. The idea: If vaccinating cows protects dairy workers, that could mean fewer chances for the virus to jump into people and mutate in ways that could spur human-to-human spread.

Meanwhile. the U.S. Department of Health and Human Services has been talking to manufacturers about possible mRNA flu vaccines for people that, if needed, could supplement millions of bird flu vaccine doses already in government hands.

“If there’s a pandemic, there’s going to be a huge demand for vaccine,” said Richard Webby, a flu researcher at St. Jude Children’s Research Hospital in Memphis. “The more different (vaccine manufacturing) platforms that can respond to that, the better.”

The bird flu virus has been spreading among more animal species in scores of countries since 2020. It was detected in U.S. dairy herds in March, although investigators think it may have been in cows since December. This week, the USDA announced it had been found in alpacas for the first time.

At least three people — all workers at farms with infected cows — have been diagnosed with bird flu, although the illnesses were considered mild.

But earlier versions of the same H5N1 flu virus have been highly lethal to humans in other parts of the world. Officials are taking steps to be prepared if the virus mutates in a way to make it more deadly or enables it to spread more easily from person to person.

Traditionally, most flu vaccines are made via an egg-based manufacturing process that’s been used for more than 70 years. It involves injecting a candidate virus into fertilized chicken eggs, which are incubated for several days to allow the viruses to grow. Fluid is harvested from the eggs and is used as the basis for vaccines, with killed or weakened virus priming the body’s immune system.

Rather than eggs — also vulnerable to bird flu-caused supply constraints — some flu vaccine is made in giant vats of cells.

Officials say they already have two candidate vaccines for people that appear to be well-matched to the bird flu virus in U.S. dairy herds. The Centers for Disease Control and Prevention used the circulating bird flu virus as the seed strain for them.

The government has hundreds of thousands of vaccine doses in pre-filled syringes and vials that likely could go out in a matter of weeks, if needed, federal health officials say.

They also say they have bulk antigen that could generate nearly 10 million more doses that could be filled, finished and distributed in a matter of a few months. CSL Seqirus, which manufactures cell-based flu vaccine, this week announced that the government hired it to fill and finish about 4.8 million of those doses. The work could be done by late summer, U.S. health officials said this week.

But the production lines for flu vaccines are already working on this fall’s seasonal shots — work that would have to be interrupted to produce millions more doses of bird flu vaccine. So the government has been pursuing another, quicker approach: the mRNA technology used to produce the primary vaccines deployed against COVID-19.

These messenger RNA vaccines are made using a small section of genetic material from the virus. The genetic blueprint is designed to teach the body how to make a protein used to build immunity.

The pharmaceutical company Moderna already has a bird flu mRNA vaccine in very early-stage human testing. In a statement, Moderna confirmed that “we are in discussions with the U.S. government on advancing our pandemic flu candidate.”

Similar work has been going on at Pfizer. Company researchers in December gave human volunteers an mRNA vaccine against a bird flu strain that’s similar to — but not exactly the same as — the one in cows. Since then, researchers have performed a lab experiment exposing blood samples from those volunteers to the strain seen in dairy farms, and saw a “notable increases in antibody responses,” Pfizer said in a statement.

As for the vaccine for cows, Penn immunologist Scott Hensley worked with mRNA pioneer and Nobel laureate Drew Weissman to produce the experimental doses. Hensley said that vaccine is similar to the Moderna one for people.

In first-step testing, mice and ferrets produced high levels of bird flu virus-fighting antibodies after vaccination.

In another experiment, researchers vaccinated one group of ferrets and deliberately infected them, and then compared what happened to ferrets that hadn’t been vaccinated. All the vaccinated animals survived and the unvaccinated did not, Hensley said.

“The vaccine was really successful,” said Webby, whose lab did that work last year in collaboration with Hensley.

The cow study will be akin to the first-step testing initially done in smaller animals. The plan is for initially about 10 calves to be vaccinated, half with one dose and half with another. Then their blood will be drawn and examined to look for how much bird flu-fighting antibodies were produced.

The USDA study first will have to determine the right dose for such a large animal, Hensley said, before testing if it protects them like it did smaller animals.

What “scares me the most is the amount of interaction between cattle and humans,” Hensley said.

“We’re not talking about an animal that lives on a mountain top,” he said. “If this was a bobcat outbreak I’d feel bad for the bobcats, but that’s not a big human risk.”

If a vaccine reduces the amount of virus in the cow, “then ultimately we reduce the chance that a mutant virus that spreads in humans is going to emerge,” he said.

The Associated Press Health and Science Department receives support from the Howard Hughes Medical Institute’s Science and Educational Media Group. The AP is solely responsible for all content.

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Guest Essay

The Long-Overlooked Molecule That Will Define a Generation of Science

By Thomas Cech

Dr. Cech is a biochemist and the author of the forthcoming book “The Catalyst: RNA and the Quest to Unlock Life’s Deepest Secrets,” from which this essay is adapted.

From E=mc² to splitting the atom to the invention of the transistor, the first half of the 20th century was dominated by breakthroughs in physics.

Then, in the early 1950s, biology began to nudge physics out of the scientific spotlight — and when I say “biology,” what I really mean is DNA. The momentous discovery of the DNA double helix in 1953 more or less ushered in a new era in science that culminated in the Human Genome Project, completed in 2003, which decoded all of our DNA into a biological blueprint of humankind.

DNA has received an immense amount of attention. And while the double helix was certainly groundbreaking in its time, the current generation of scientific history will be defined by a different (and, until recently, lesser-known) molecule — one that I believe will play an even bigger role in furthering our understanding of human life: RNA.

You may remember learning about RNA (ribonucleic acid) back in your high school biology class as the messenger that carries information stored in DNA to instruct the formation of proteins. Such messenger RNA, mRNA for short, recently entered the mainstream conversation thanks to the role they played in the Covid-19 vaccines. But RNA is much more than a messenger, as critical as that function may be.

Other types of RNA, called “noncoding” RNAs, are a tiny biological powerhouse that can help to treat and cure deadly diseases, unlock the potential of the human genome and solve one of the most enduring mysteries of science: explaining the origins of all life on our planet.

Though it is a linchpin of every living thing on Earth, RNA was misunderstood and underappreciated for decades — often dismissed as nothing more than a biochemical backup singer, slaving away in obscurity in the shadows of the diva, DNA. I know that firsthand: I was slaving away in obscurity on its behalf.

In the early 1980s, when I was much younger and most of the promise of RNA was still unimagined, I set up my lab at the University of Colorado, Boulder. After two years of false leads and frustration, my research group discovered that the RNA we’d been studying had catalytic power. This means that the RNA could cut and join biochemical bonds all by itself — the sort of activity that had been thought to be the sole purview of protein enzymes. This gave us a tantalizing glimpse at our deepest origins: If RNA could both hold information and orchestrate the assembly of molecules, it was very likely that the first living things to spring out of the primordial ooze were RNA-based organisms.

That breakthrough at my lab — along with independent observations of RNA catalysis by Sidney Altman at Yale — was recognized with a Nobel Prize in 1989. The attention generated by the prize helped lead to an efflorescence of research that continued to expand our idea of what RNA could do.

In recent years, our understanding of RNA has begun to advance even more rapidly. Since 2000, RNA-related breakthroughs have led to 11 Nobel Prizes. In the same period, the number of scientific journal articles and patents generated annually by RNA research has quadrupled. There are more than 400 RNA-based drugs in development, beyond the ones that are already in use. And in 2022 alone, more than $1 billion in private equity funds was invested in biotechnology start-ups to explore frontiers in RNA research.

What’s driving the RNA age is this molecule’s dazzling versatility. Yes, RNA can store genetic information, just like DNA. As a case in point, many of the viruses (from influenza to Ebola to SARS-CoV-2) that plague us don’t bother with DNA at all; their genes are made of RNA, which suits them perfectly well. But storing information is only the first chapter in RNA’s playbook.

Unlike DNA, RNA plays numerous active roles in living cells. It acts as an enzyme, splicing and dicing other RNA molecules or assembling proteins — the stuff of which all life is built — from amino acid building blocks. It keeps stem cells active and forestalls aging by building out the DNA at the ends of our chromosomes.

RNA discoveries have led to new therapies, such as the use of antisense RNA to help treat children afflicted with the devastating disease spinal muscular atrophy. The mRNA vaccines, which saved millions of lives during the Covid pandemic, are being reformulated to attack other diseases, including some cancers . RNA research may also be helping us rewrite the future; the genetic scissors that give CRISPR its breathtaking power to edit genes are guided to their sites of action by RNAs.

Although most scientists now agree on RNA's bright promise, we are still only beginning to unlock its potential. Consider, for instance, that some 75 percent of the human genome consists of dark matter that is copied into RNAs of unknown function. While some researchers have dismissed this dark matter as junk or noise, I expect it will be the source of even more exciting breakthroughs.

We don’t know yet how many of these possibilities will prove true. But if the past 40 years of research have taught me anything, it is never to underestimate this little molecule. The age of RNA is just getting started.

Thomas Cech is a biochemist at the University of Colorado, Boulder; a recipient of the Nobel Prize in Chemistry in 1989 for his work with RNA; and the author of “The Catalyst: RNA and the Quest to Unlock Life’s Deepest Secrets,” from which this essay is adapted.

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Google research shows the fast rise of AI-generated misinformation

Artificial intelligence has become a source of misinformation with lightning speed.

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From fake images of war to celebrity hoaxes, artificial intelligence technology has spawned new forms of reality-warping misinformation online. New analysis co-authored by Google researchers shows just how quickly the problem has grown.

The research, co-authored by researchers from Google, Duke University and several fact-checking and media organizations, was published in a preprint last week. The paper introduces a massive new dataset of misinformation going back to 1995 that was fact-checked by websites like Snopes.

According to the researchers, the data reveals that AI-generated images have quickly risen in prominence, becoming nearly as popular as more traditional forms of manipulation.

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The work was first reported by 404 Media after being spotted by the Faked Up newsletter, and it clearly shows that "AI-generated images made up a minute proportion of content manipulations overall until early last year," the researchers wrote.

Last year saw the release of new AI image-generation tools by major players in tech, including OpenAI, Microsoft and Google itself. Now, AI-generated misinformation is "nearly as common as text and general content manipulations," the paper said.

The researchers note that the uptick in fact-checking AI images coincided with a general wave of AI hype, which may have led websites to focus on the technology. The dataset shows that fact-checking AI has slowed down in recent months, with traditional text and image manipulation seeing an increase.

The study looked at other forms of media, too, and found that video hoaxes now make up roughly 60 per cent of all fact-checked claims that include media.

That doesn't mean AI-generated misinformation has slowed down, said Sasha Luccioni, a leading AI ethics researcher at machine learning platform Hugging Face.

"Personally, I feel like this is because there are so many [examples of AI misinformation] that it's hard to keep track!" Luccioni said in an email. "I see them regularly myself, even outside of social media, in advertising, for instance."

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AI has been used to generate fake images of real people, with concerning effects. For example, fake nude images of Taylor Swift circulated earlier this year. 404 Media reported that the tool used to create the images was Microsoft's AI-generation software, which it licenses from ChatGPT maker OpenAI — prompting the tech giant to close a loophole allowing the images to be generated.

The technology has also fooled people in more innocuous ways. Recent fake photos showing Katy Perry attending the Met Gala in New York — in reality, she never did —  fooled observers on social media and even the star's own parents.

The rise of AI has caused headaches for social media companies and Google itself. Fake celebrity images have been featured prominently in Google image search results in the past, thanks to SEO-driven content farms. Using AI to manipulate search results is against Google's policies.

Taylor Swift deepfakes taken offline. It’s not so easy for regular people

Google spokespeople were not immediately available for comment. Previously, a spokesperson told technology news outlet Motherboard that "when we find instances where low-quality content is ranking highly, we build scalable solutions that improve the results not for just one search, but for a range of queries."

To deal with the problem of AI fakes, Google has launched such initiatives as digital waterma rking , which flags AI-generated images as fake with a mark that is invisible to the human eye. The company, along with Microsoft, Intel and Adobe, is also exploring giving creators the option to add a visible watermark to AI-generated images.

"I think if Big Tech companies collaborated on a standard of AI watermarks, that would definitely help the field as a whole at this point," Luccioni said.

ABOUT THE AUTHOR

Jordan Pearson is a Toronto-based journalist and the former executive editor of Motherboard.

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