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• How to Write a Strong Hypothesis | Steps & Examples

How to Write a Strong Hypothesis | Steps & Examples

Published on May 6, 2022 by Shona McCombes . Revised on November 20, 2023.

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

Example: Hypothesis

Daily apple consumption leads to fewer doctor’s visits.

Table of contents

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

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

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

Variables in hypotheses

Hypotheses propose a relationship between two or more types of variables .

• An independent variable is something the researcher changes or controls.
• A dependent variable is something the researcher observes and measures.

If there are any control variables , extraneous variables , or confounding variables , be sure to jot those down as you go to minimize the chances that research bias  will affect your results.

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

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Step 1. Ask a question

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

Step 2. Do some preliminary research

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

At this stage, you might construct a conceptual framework to ensure that you’re embarking on a relevant topic . This can also help you identify which variables you will study and what you think the relationships are between them. Sometimes, you’ll have to operationalize more complex constructs.

Step 3. Formulate your hypothesis

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

4. Refine your hypothesis

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

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

5. Phrase your hypothesis in three ways

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

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

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

6. Write a null hypothesis

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

• H 0 : The number of lectures attended by first-year students has no effect on their final exam scores.
• H 1 : The number of lectures attended by first-year students has a positive effect on their final exam scores.

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

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

Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.

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

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McCombes, S. (2023, November 20). How to Write a Strong Hypothesis | Steps & Examples. Scribbr. Retrieved September 4, 2024, from https://www.scribbr.com/methodology/hypothesis/

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Formulating Hypotheses for Different Study Designs

Durga prasanna misra.

1 Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, India.

Armen Yuri Gasparyan

2 Departments of Rheumatology and Research and Development, Dudley Group NHS Foundation Trust (Teaching Trust of the University of Birmingham, UK), Russells Hall Hospital, Dudley, UK.

Olena Zimba

3 Department of Internal Medicine #2, Danylo Halytsky Lviv National Medical University, Lviv, Ukraine.

Marlen Yessirkepov

4 Department of Biology and Biochemistry, South Kazakhstan Medical Academy, Shymkent, Kazakhstan.

Vikas Agarwal

George d. kitas.

5 Centre for Epidemiology versus Arthritis, University of Manchester, Manchester, UK.

Generating a testable working hypothesis is the first step towards conducting original research. Such research may prove or disprove the proposed hypothesis. Case reports, case series, online surveys and other observational studies, clinical trials, and narrative reviews help to generate hypotheses. Observational and interventional studies help to test hypotheses. A good hypothesis is usually based on previous evidence-based reports. Hypotheses without evidence-based justification and a priori ideas are not received favourably by the scientific community. Original research to test a hypothesis should be carefully planned to ensure appropriate methodology and adequate statistical power. While hypotheses can challenge conventional thinking and may be controversial, they should not be destructive. A hypothesis should be tested by ethically sound experiments with meaningful ethical and clinical implications. The coronavirus disease 2019 pandemic has brought into sharp focus numerous hypotheses, some of which were proven (e.g. effectiveness of corticosteroids in those with hypoxia) while others were disproven (e.g. ineffectiveness of hydroxychloroquine and ivermectin).

Graphical Abstract

DEFINING WORKING AND STANDALONE SCIENTIFIC HYPOTHESES

Science is the systematized description of natural truths and facts. Routine observations of existing life phenomena lead to the creative thinking and generation of ideas about mechanisms of such phenomena and related human interventions. Such ideas presented in a structured format can be viewed as hypotheses. After generating a hypothesis, it is necessary to test it to prove its validity. Thus, hypothesis can be defined as a proposed mechanism of a naturally occurring event or a proposed outcome of an intervention. 1 , 2

Hypothesis testing requires choosing the most appropriate methodology and adequately powering statistically the study to be able to “prove” or “disprove” it within predetermined and widely accepted levels of certainty. This entails sample size calculation that often takes into account previously published observations and pilot studies. 2 , 3 In the era of digitization, hypothesis generation and testing may benefit from the availability of numerous platforms for data dissemination, social networking, and expert validation. Related expert evaluations may reveal strengths and limitations of proposed ideas at early stages of post-publication promotion, preventing the implementation of unsupported controversial points. 4

Thus, hypothesis generation is an important initial step in the research workflow, reflecting accumulating evidence and experts' stance. In this article, we overview the genesis and importance of scientific hypotheses and their relevance in the era of the coronavirus disease 2019 (COVID-19) pandemic.

DO WE NEED HYPOTHESES FOR ALL STUDY DESIGNS?

Broadly, research can be categorized as primary or secondary. In the context of medicine, primary research may include real-life observations of disease presentations and outcomes. Single case descriptions, which often lead to new ideas and hypotheses, serve as important starting points or justifications for case series and cohort studies. The importance of case descriptions is particularly evident in the context of the COVID-19 pandemic when unique, educational case reports have heralded a new era in clinical medicine. 5

Case series serve similar purpose to single case reports, but are based on a slightly larger quantum of information. Observational studies, including online surveys, describe the existing phenomena at a larger scale, often involving various control groups. Observational studies include variable-scale epidemiological investigations at different time points. Interventional studies detail the results of therapeutic interventions.

Secondary research is based on already published literature and does not directly involve human or animal subjects. Review articles are generated by secondary research. These could be systematic reviews which follow methods akin to primary research but with the unit of study being published papers rather than humans or animals. Systematic reviews have a rigid structure with a mandatory search strategy encompassing multiple databases, systematic screening of search results against pre-defined inclusion and exclusion criteria, critical appraisal of study quality and an optional component of collating results across studies quantitatively to derive summary estimates (meta-analysis). 6 Narrative reviews, on the other hand, have a more flexible structure. Systematic literature searches to minimise bias in selection of articles are highly recommended but not mandatory. 7 Narrative reviews are influenced by the authors' viewpoint who may preferentially analyse selected sets of articles. 8

In relation to primary research, case studies and case series are generally not driven by a working hypothesis. Rather, they serve as a basis to generate a hypothesis. Observational or interventional studies should have a hypothesis for choosing research design and sample size. The results of observational and interventional studies further lead to the generation of new hypotheses, testing of which forms the basis of future studies. Review articles, on the other hand, may not be hypothesis-driven, but form fertile ground to generate future hypotheses for evaluation. Fig. 1 summarizes which type of studies are hypothesis-driven and which lead on to hypothesis generation.

STANDARDS OF WORKING AND SCIENTIFIC HYPOTHESES

A review of the published literature did not enable the identification of clearly defined standards for working and scientific hypotheses. It is essential to distinguish influential versus not influential hypotheses, evidence-based hypotheses versus a priori statements and ideas, ethical versus unethical, or potentially harmful ideas. The following points are proposed for consideration while generating working and scientific hypotheses. 1 , 2 Table 1 summarizes these points.

Evidence-based data

A scientific hypothesis should have a sound basis on previously published literature as well as the scientist's observations. Randomly generated (a priori) hypotheses are unlikely to be proven. A thorough literature search should form the basis of a hypothesis based on published evidence. 7

Unless a scientific hypothesis can be tested, it can neither be proven nor be disproven. Therefore, a scientific hypothesis should be amenable to testing with the available technologies and the present understanding of science.

Supported by pilot studies

If a hypothesis is based purely on a novel observation by the scientist in question, it should be grounded on some preliminary studies to support it. For example, if a drug that targets a specific cell population is hypothesized to be useful in a particular disease setting, then there must be some preliminary evidence that the specific cell population plays a role in driving that disease process.

Testable by ethical studies

The hypothesis should be testable by experiments that are ethically acceptable. 9 For example, a hypothesis that parachutes reduce mortality from falls from an airplane cannot be tested using a randomized controlled trial. 10 This is because it is obvious that all those jumping from a flying plane without a parachute would likely die. Similarly, the hypothesis that smoking tobacco causes lung cancer cannot be tested by a clinical trial that makes people take up smoking (since there is considerable evidence for the health hazards associated with smoking). Instead, long-term observational studies comparing outcomes in those who smoke and those who do not, as was performed in the landmark epidemiological case control study by Doll and Hill, 11 are more ethical and practical.

Balance between scientific temper and controversy

Novel findings, including novel hypotheses, particularly those that challenge established norms, are bound to face resistance for their wider acceptance. Such resistance is inevitable until the time such findings are proven with appropriate scientific rigor. However, hypotheses that generate controversy are generally unwelcome. For example, at the time the pandemic of human immunodeficiency virus (HIV) and AIDS was taking foot, there were numerous deniers that refused to believe that HIV caused AIDS. 12 , 13 Similarly, at a time when climate change is causing catastrophic changes to weather patterns worldwide, denial that climate change is occurring and consequent attempts to block climate change are certainly unwelcome. 14 The denialism and misinformation during the COVID-19 pandemic, including unfortunate examples of vaccine hesitancy, are more recent examples of controversial hypotheses not backed by science. 15 , 16 An example of a controversial hypothesis that was a revolutionary scientific breakthrough was the hypothesis put forth by Warren and Marshall that Helicobacter pylori causes peptic ulcers. Initially, the hypothesis that a microorganism could cause gastritis and gastric ulcers faced immense resistance. When the scientists that proposed the hypothesis themselves ingested H. pylori to induce gastritis in themselves, only then could they convince the wider world about their hypothesis. Such was the impact of the hypothesis was that Barry Marshall and Robin Warren were awarded the Nobel Prize in Physiology or Medicine in 2005 for this discovery. 17 , 18

DISTINGUISHING THE MOST INFLUENTIAL HYPOTHESES

Influential hypotheses are those that have stood the test of time. An archetype of an influential hypothesis is that proposed by Edward Jenner in the eighteenth century that cowpox infection protects against smallpox. While this observation had been reported for nearly a century before this time, it had not been suitably tested and publicised until Jenner conducted his experiments on a young boy by demonstrating protection against smallpox after inoculation with cowpox. 19 These experiments were the basis for widespread smallpox immunization strategies worldwide in the 20th century which resulted in the elimination of smallpox as a human disease today. 20

Other influential hypotheses are those which have been read and cited widely. An example of this is the hygiene hypothesis proposing an inverse relationship between infections in early life and allergies or autoimmunity in adulthood. An analysis reported that this hypothesis had been cited more than 3,000 times on Scopus. 1

LESSONS LEARNED FROM HYPOTHESES AMIDST THE COVID-19 PANDEMIC

The COVID-19 pandemic devastated the world like no other in recent memory. During this period, various hypotheses emerged, understandably so considering the public health emergency situation with innumerable deaths and suffering for humanity. Within weeks of the first reports of COVID-19, aberrant immune system activation was identified as a key driver of organ dysfunction and mortality in this disease. 21 Consequently, numerous drugs that suppress the immune system or abrogate the activation of the immune system were hypothesized to have a role in COVID-19. 22 One of the earliest drugs hypothesized to have a benefit was hydroxychloroquine. Hydroxychloroquine was proposed to interfere with Toll-like receptor activation and consequently ameliorate the aberrant immune system activation leading to pathology in COVID-19. 22 The drug was also hypothesized to have a prophylactic role in preventing infection or disease severity in COVID-19. It was also touted as a wonder drug for the disease by many prominent international figures. However, later studies which were well-designed randomized controlled trials failed to demonstrate any benefit of hydroxychloroquine in COVID-19. 23 , 24 , 25 , 26 Subsequently, azithromycin 27 , 28 and ivermectin 29 were hypothesized as potential therapies for COVID-19, but were not supported by evidence from randomized controlled trials. The role of vitamin D in preventing disease severity was also proposed, but has not been proven definitively until now. 30 , 31 On the other hand, randomized controlled trials identified the evidence supporting dexamethasone 32 and interleukin-6 pathway blockade with tocilizumab as effective therapies for COVID-19 in specific situations such as at the onset of hypoxia. 33 , 34 Clues towards the apparent effectiveness of various drugs against severe acute respiratory syndrome coronavirus 2 in vitro but their ineffectiveness in vivo have recently been identified. Many of these drugs are weak, lipophilic bases and some others induce phospholipidosis which results in apparent in vitro effectiveness due to non-specific off-target effects that are not replicated inside living systems. 35 , 36

Another hypothesis proposed was the association of the routine policy of vaccination with Bacillus Calmette-Guerin (BCG) with lower deaths due to COVID-19. This hypothesis emerged in the middle of 2020 when COVID-19 was still taking foot in many parts of the world. 37 , 38 Subsequently, many countries which had lower deaths at that time point went on to have higher numbers of mortality, comparable to other areas of the world. Furthermore, the hypothesis that BCG vaccination reduced COVID-19 mortality was a classic example of ecological fallacy. Associations between population level events (ecological studies; in this case, BCG vaccination and COVID-19 mortality) cannot be directly extrapolated to the individual level. Furthermore, such associations cannot per se be attributed as causal in nature, and can only serve to generate hypotheses that need to be tested at the individual level. 39

IS TRADITIONAL PEER REVIEW EFFICIENT FOR EVALUATION OF WORKING AND SCIENTIFIC HYPOTHESES?

Traditionally, publication after peer review has been considered the gold standard before any new idea finds acceptability amongst the scientific community. Getting a work (including a working or scientific hypothesis) reviewed by experts in the field before experiments are conducted to prove or disprove it helps to refine the idea further as well as improve the experiments planned to test the hypothesis. 40 A route towards this has been the emergence of journals dedicated to publishing hypotheses such as the Central Asian Journal of Medical Hypotheses and Ethics. 41 Another means of publishing hypotheses is through registered research protocols detailing the background, hypothesis, and methodology of a particular study. If such protocols are published after peer review, then the journal commits to publishing the completed study irrespective of whether the study hypothesis is proven or disproven. 42 In the post-pandemic world, online research methods such as online surveys powered via social media channels such as Twitter and Instagram might serve as critical tools to generate as well as to preliminarily test the appropriateness of hypotheses for further evaluation. 43 , 44

Some radical hypotheses might be difficult to publish after traditional peer review. These hypotheses might only be acceptable by the scientific community after they are tested in research studies. Preprints might be a way to disseminate such controversial and ground-breaking hypotheses. 45 However, scientists might prefer to keep their hypotheses confidential for the fear of plagiarism of ideas, avoiding online posting and publishing until they have tested the hypotheses.

SUGGESTIONS ON GENERATING AND PUBLISHING HYPOTHESES

Publication of hypotheses is important, however, a balance is required between scientific temper and controversy. Journal editors and reviewers might keep in mind these specific points, summarized in Table 2 and detailed hereafter, while judging the merit of hypotheses for publication. Keeping in mind the ethical principle of primum non nocere, a hypothesis should be published only if it is testable in a manner that is ethically appropriate. 46 Such hypotheses should be grounded in reality and lend themselves to further testing to either prove or disprove them. It must be considered that subsequent experiments to prove or disprove a hypothesis have an equal chance of failing or succeeding, akin to tossing a coin. A pre-conceived belief that a hypothesis is unlikely to be proven correct should not form the basis of rejection of such a hypothesis for publication. In this context, hypotheses generated after a thorough literature search to identify knowledge gaps or based on concrete clinical observations on a considerable number of patients (as opposed to random observations on a few patients) are more likely to be acceptable for publication by peer-reviewed journals. Also, hypotheses should be considered for publication or rejection based on their implications for science at large rather than whether the subsequent experiments to test them end up with results in favour of or against the original hypothesis.

Hypotheses form an important part of the scientific literature. The COVID-19 pandemic has reiterated the importance and relevance of hypotheses for dealing with public health emergencies and highlighted the need for evidence-based and ethical hypotheses. A good hypothesis is testable in a relevant study design, backed by preliminary evidence, and has positive ethical and clinical implications. General medical journals might consider publishing hypotheses as a specific article type to enable more rapid advancement of science.

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

Author Contributions:

• Data curation: Gasparyan AY, Misra DP, Zimba O, Yessirkepov M, Agarwal V, Kitas GD.

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Step-by-Step Guide: How to Craft a Strong Research Hypothesis

• 4 minute read

Table of Contents

A research hypothesis is a concise statement about the expected result of an experiment or project. In many ways, a research hypothesis represents the starting point for a scientific endeavor, as it establishes a tentative assumption that is eventually substantiated or falsified, ultimately improving our certainty about the subject investigated.   

To help you with this and ease the process, in this article, we discuss the purpose of research hypotheses and list the most essential qualities of a compelling hypothesis. Let’s find out!  

How to Craft a Research Hypothesis  

Crafting a research hypothesis begins with a comprehensive literature review to identify a knowledge gap in your field. Once you find a question or problem, come up with a possible answer or explanation, which becomes your hypothesis. Now think about the specific methods of experimentation that can prove or disprove the hypothesis, which ultimately lead to the results of the study.   

Enlisted below are some standard formats in which you can formulate a hypothesis¹ :  

• A hypothesis can use the if/then format when it seeks to explore the correlation between two variables in a study primarily.  

Example: If administered drug X, then patients will experience reduced fatigue from cancer treatment.  

• A hypothesis can adopt when X/then Y format when it primarily aims to expose a connection between two variables  

Example: When workers spend a significant portion of their waking hours in sedentary work , then they experience a greater frequency of digestive problems.  

• A hypothesis can also take the form of a direct statement.  

Example: Drug X and drug Y reduce the risk of cognitive decline through the same chemical pathways  

What are the Features of an Effective Hypothesis?  

Hypotheses in research need to satisfy specific criteria to be considered scientifically rigorous. Here are the most notable qualities of a strong hypothesis:  

• Testability: Ensure the hypothesis allows you to work towards observable and testable results.  
• Brevity and objectivity: Present your hypothesis as a brief statement and avoid wordiness.  
• Clarity and Relevance: The hypothesis should reflect a clear idea of what we know and what we expect to find out about a phenomenon and address the significant knowledge gap relevant to a field of study.   

Understanding Null and Alternative Hypotheses in Research  

There are two types of hypotheses used commonly in research that aid statistical analyses. These are known as the null hypothesis and the alternative hypothesis . A null hypothesis is a statement assumed to be factual in the initial phase of the study.   

For example, if a researcher is testing the efficacy of a new drug, then the null hypothesis will posit that the drug has no benefits compared to an inactive control or placebo . Suppose the data collected through a drug trial leads a researcher to reject the null hypothesis. In that case, it is considered to substantiate the alternative hypothesis in the above example, that the new drug provides benefits compared to the placebo.  

Let’s take a closer look at the null hypothesis and alternative hypothesis with two more examples:  

Null Hypothesis:  

The rate of decline in the number of species in habitat X in the last year is the same as in the last 100 years when controlled for all factors except the recent wildfires.  

In the next experiment, the researcher will experimentally reject this null hypothesis in order to confirm the following alternative hypothesis :  

The rate of decline in the number of species in habitat X in the last year is different from the rate of decline in the last 100 years when controlled for all factors other than the recent wildfires.  

In the pair of null and alternative hypotheses stated above, a statistical comparison of the rate of species decline over a century and the preceding year will help the research experimentally test the null hypothesis, helping to draw scientifically valid conclusions about two factors—wildfires and species decline.   

We also recommend that researchers pay attention to contextual echoes and connections when writing research hypotheses. Research hypotheses are often closely linked to the introduction ² , such as the context of the study, and can similarly influence the reader’s judgment of the relevance and validity of the research hypothesis.  

Seasoned experts, such as professionals at Elsevier Language Services, guide authors on how to best embed a hypothesis within an article so that it communicates relevance and credibility. Contact us if you want help in ensuring readers find your hypothesis robust and unbiased.  

References  

• Hypotheses – The University Writing Center. (n.d.). https://writingcenter.tamu.edu/writing-speaking-guides/hypotheses  
• Shaping the research question and hypothesis. (n.d.). Students. https://students.unimelb.edu.au/academic-skills/graduate-research-services/writing-thesis-sections-part-2/shaping-the-research-question-and-hypothesis  

Systematic Literature Review or Literature Review?

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• Knowledge Base
• Methodology
• How to Write a Strong Hypothesis | Guide & Examples

How to Write a Strong Hypothesis | Guide & Examples

Published on 6 May 2022 by Shona McCombes .

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

Table of contents

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

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

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

Variables in hypotheses

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

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

Prevent plagiarism, run a free check.

Step 1: ask a question.

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

Step 2: Do some preliminary research

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

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

Step 3: Formulate your hypothesis

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

Step 4: Refine your hypothesis

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

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

Step 5: Phrase your hypothesis in three ways

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

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

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

Step 6. Write a null hypothesis

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

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

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

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

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

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McCombes, S. (2022, May 06). How to Write a Strong Hypothesis | Guide & Examples. Scribbr. Retrieved 3 September 2024, from https://www.scribbr.co.uk/research-methods/hypothesis-writing/

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The Craft of Writing a Strong Hypothesis

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

• Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
• Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

• A research hypothesis has to be simple yet clear to look justifiable enough.
• It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
• It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
• A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
• If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
• A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

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What is a Research Hypothesis: How to Write it, Types, and Examples

Any research begins with a research question and a research hypothesis . A research question alone may not suffice to design the experiment(s) needed to answer it. A hypothesis is central to the scientific method. But what is a hypothesis ? A hypothesis is a testable statement that proposes a possible explanation to a phenomenon, and it may include a prediction. Next, you may ask what is a research hypothesis ? Simply put, a research hypothesis is a prediction or educated guess about the relationship between the variables that you want to investigate.  

It is important to be thorough when developing your research hypothesis. Shortcomings in the framing of a hypothesis can affect the study design and the results. A better understanding of the research hypothesis definition and characteristics of a good hypothesis will make it easier for you to develop your own hypothesis for your research. Let’s dive in to know more about the types of research hypothesis , how to write a research hypothesis , and some research hypothesis examples .  

Table of Contents

What is a hypothesis ?  

A hypothesis is based on the existing body of knowledge in a study area. Framed before the data are collected, a hypothesis states the tentative relationship between independent and dependent variables, along with a prediction of the outcome.  

What is a research hypothesis ?  

Young researchers starting out their journey are usually brimming with questions like “ What is a hypothesis ?” “ What is a research hypothesis ?” “How can I write a good research hypothesis ?”   

A research hypothesis is a statement that proposes a possible explanation for an observable phenomenon or pattern. It guides the direction of a study and predicts the outcome of the investigation. A research hypothesis is testable, i.e., it can be supported or disproven through experimentation or observation.     

Characteristics of a good hypothesis  

Here are the characteristics of a good hypothesis :  

• Clearly formulated and free of language errors and ambiguity  
• Concise and not unnecessarily verbose  
• Has clearly defined variables  
• Testable and stated in a way that allows for it to be disproven  
• Can be tested using a research design that is feasible, ethical, and practical   
• Specific and relevant to the research problem  
• Rooted in a thorough literature search  
• Can generate new knowledge or understanding.  

How to create an effective research hypothesis  

A study begins with the formulation of a research question. A researcher then performs background research. This background information forms the basis for building a good research hypothesis . The researcher then performs experiments, collects, and analyzes the data, interprets the findings, and ultimately, determines if the findings support or negate the original hypothesis.  

Let’s look at each step for creating an effective, testable, and good research hypothesis :  

• Identify a research problem or question: Start by identifying a specific research problem.   
• Review the literature: Conduct an in-depth review of the existing literature related to the research problem to grasp the current knowledge and gaps in the field.   
• Formulate a clear and testable hypothesis : Based on the research question, use existing knowledge to form a clear and testable hypothesis . The hypothesis should state a predicted relationship between two or more variables that can be measured and manipulated. Improve the original draft till it is clear and meaningful.  
• State the null hypothesis: The null hypothesis is a statement that there is no relationship between the variables you are studying.   
• Define the population and sample: Clearly define the population you are studying and the sample you will be using for your research.  
• Select appropriate methods for testing the hypothesis: Select appropriate research methods, such as experiments, surveys, or observational studies, which will allow you to test your research hypothesis .  

Remember that creating a research hypothesis is an iterative process, i.e., you might have to revise it based on the data you collect. You may need to test and reject several hypotheses before answering the research problem.  

How to write a research hypothesis  

When you start writing a research hypothesis , you use an “if–then” statement format, which states the predicted relationship between two or more variables. Clearly identify the independent variables (the variables being changed) and the dependent variables (the variables being measured), as well as the population you are studying. Review and revise your hypothesis as needed.  

An example of a research hypothesis in this format is as follows:  

“ If [athletes] follow [cold water showers daily], then their [endurance] increases.”  

Population: athletes  

Independent variable: daily cold water showers  

Dependent variable: endurance  

You may have understood the characteristics of a good hypothesis . But note that a research hypothesis is not always confirmed; a researcher should be prepared to accept or reject the hypothesis based on the study findings.  

Research hypothesis checklist  

Following from above, here is a 10-point checklist for a good research hypothesis :  

• Testable: A research hypothesis should be able to be tested via experimentation or observation.  
• Specific: A research hypothesis should clearly state the relationship between the variables being studied.  
• Based on prior research: A research hypothesis should be based on existing knowledge and previous research in the field.  
• Falsifiable: A research hypothesis should be able to be disproven through testing.  
• Clear and concise: A research hypothesis should be stated in a clear and concise manner.  
• Logical: A research hypothesis should be logical and consistent with current understanding of the subject.  
• Relevant: A research hypothesis should be relevant to the research question and objectives.  
• Feasible: A research hypothesis should be feasible to test within the scope of the study.  
• Reflects the population: A research hypothesis should consider the population or sample being studied.  
• Uncomplicated: A good research hypothesis is written in a way that is easy for the target audience to understand.  

By following this research hypothesis checklist , you will be able to create a research hypothesis that is strong, well-constructed, and more likely to yield meaningful results.  

Types of research hypothesis  

Different types of research hypothesis are used in scientific research:  

1. Null hypothesis:

A null hypothesis states that there is no change in the dependent variable due to changes to the independent variable. This means that the results are due to chance and are not significant. A null hypothesis is denoted as H0 and is stated as the opposite of what the alternative hypothesis states.   

Example: “ The newly identified virus is not zoonotic .”  

2. Alternative hypothesis:

This states that there is a significant difference or relationship between the variables being studied. It is denoted as H1 or Ha and is usually accepted or rejected in favor of the null hypothesis.  

Example: “ The newly identified virus is zoonotic .”  

3. Directional hypothesis :

This specifies the direction of the relationship or difference between variables; therefore, it tends to use terms like increase, decrease, positive, negative, more, or less.   

Example: “ The inclusion of intervention X decreases infant mortality compared to the original treatment .”   

4. Non-directional hypothesis:

While it does not predict the exact direction or nature of the relationship between the two variables, a non-directional hypothesis states the existence of a relationship or difference between variables but not the direction, nature, or magnitude of the relationship. A non-directional hypothesis may be used when there is no underlying theory or when findings contradict previous research.  

Example, “ Cats and dogs differ in the amount of affection they express .”  

5. Simple hypothesis :

A simple hypothesis only predicts the relationship between one independent and another independent variable.  

Example: “ Applying sunscreen every day slows skin aging .”  

6 . Complex hypothesis :

A complex hypothesis states the relationship or difference between two or more independent and dependent variables.   

Example: “ Applying sunscreen every day slows skin aging, reduces sun burn, and reduces the chances of skin cancer .” (Here, the three dependent variables are slowing skin aging, reducing sun burn, and reducing the chances of skin cancer.)  

7. Associative hypothesis:  

An associative hypothesis states that a change in one variable results in the change of the other variable. The associative hypothesis defines interdependency between variables.  

Example: “ There is a positive association between physical activity levels and overall health .”  

8 . Causal hypothesis:

A causal hypothesis proposes a cause-and-effect interaction between variables.  

Example: “ Long-term alcohol use causes liver damage .”  

Note that some of the types of research hypothesis mentioned above might overlap. The types of hypothesis chosen will depend on the research question and the objective of the study.  

Research hypothesis examples  

Here are some good research hypothesis examples :  

“The use of a specific type of therapy will lead to a reduction in symptoms of depression in individuals with a history of major depressive disorder.”  

“Providing educational interventions on healthy eating habits will result in weight loss in overweight individuals.”  

“Plants that are exposed to certain types of music will grow taller than those that are not exposed to music.”  

“The use of the plant growth regulator X will lead to an increase in the number of flowers produced by plants.”  

Characteristics that make a research hypothesis weak are unclear variables, unoriginality, being too general or too vague, and being untestable. A weak hypothesis leads to weak research and improper methods.   

Some bad research hypothesis examples (and the reasons why they are “bad”) are as follows:  

“This study will show that treatment X is better than any other treatment . ” (This statement is not testable, too broad, and does not consider other treatments that may be effective.)  

“This study will prove that this type of therapy is effective for all mental disorders . ” (This statement is too broad and not testable as mental disorders are complex and different disorders may respond differently to different types of therapy.)  

“Plants can communicate with each other through telepathy . ” (This statement is not testable and lacks a scientific basis.)  

Importance of testable hypothesis  

If a research hypothesis is not testable, the results will not prove or disprove anything meaningful. The conclusions will be vague at best. A testable hypothesis helps a researcher focus on the study outcome and understand the implication of the question and the different variables involved. A testable hypothesis helps a researcher make precise predictions based on prior research.  

To be considered testable, there must be a way to prove that the hypothesis is true or false; further, the results of the hypothesis must be reproducible.  

Frequently Asked Questions (FAQs) on research hypothesis  

1. What is the difference between research question and research hypothesis ?  

A research question defines the problem and helps outline the study objective(s). It is an open-ended statement that is exploratory or probing in nature. Therefore, it does not make predictions or assumptions. It helps a researcher identify what information to collect. A research hypothesis , however, is a specific, testable prediction about the relationship between variables. Accordingly, it guides the study design and data analysis approach.

2. When to reject null hypothesis ?

A null hypothesis should be rejected when the evidence from a statistical test shows that it is unlikely to be true. This happens when the test statistic (e.g., p -value) is less than the defined significance level (e.g., 0.05). Rejecting the null hypothesis does not necessarily mean that the alternative hypothesis is true; it simply means that the evidence found is not compatible with the null hypothesis.  

3. How can I be sure my hypothesis is testable?  

A testable hypothesis should be specific and measurable, and it should state a clear relationship between variables that can be tested with data. To ensure that your hypothesis is testable, consider the following:  

• Clearly define the key variables in your hypothesis. You should be able to measure and manipulate these variables in a way that allows you to test the hypothesis.  
• The hypothesis should predict a specific outcome or relationship between variables that can be measured or quantified.   
• You should be able to collect the necessary data within the constraints of your study.  
• It should be possible for other researchers to replicate your study, using the same methods and variables.   
• Your hypothesis should be testable by using appropriate statistical analysis techniques, so you can draw conclusions, and make inferences about the population from the sample data.  
• The hypothesis should be able to be disproven or rejected through the collection of data.  

4. How do I revise my research hypothesis if my data does not support it?  

If your data does not support your research hypothesis , you will need to revise it or develop a new one. You should examine your data carefully and identify any patterns or anomalies, re-examine your research question, and/or revisit your theory to look for any alternative explanations for your results. Based on your review of the data, literature, and theories, modify your research hypothesis to better align it with the results you obtained. Use your revised hypothesis to guide your research design and data collection. It is important to remain objective throughout the process.  

5. I am performing exploratory research. Do I need to formulate a research hypothesis?  

As opposed to “confirmatory” research, where a researcher has some idea about the relationship between the variables under investigation, exploratory research (or hypothesis-generating research) looks into a completely new topic about which limited information is available. Therefore, the researcher will not have any prior hypotheses. In such cases, a researcher will need to develop a post-hoc hypothesis. A post-hoc research hypothesis is generated after these results are known.  

6. How is a research hypothesis different from a research question?

A research question is an inquiry about a specific topic or phenomenon, typically expressed as a question. It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis.

7. Can a research hypothesis change during the research process?

Yes, research hypotheses can change during the research process. As researchers collect and analyze data, new insights and information may emerge that require modification or refinement of the initial hypotheses. This can be due to unexpected findings, limitations in the original hypotheses, or the need to explore additional dimensions of the research topic. Flexibility is crucial in research, allowing for adaptation and adjustment of hypotheses to align with the evolving understanding of the subject matter.

8. How many hypotheses should be included in a research study?

The number of research hypotheses in a research study varies depending on the nature and scope of the research. It is not necessary to have multiple hypotheses in every study. Some studies may have only one primary hypothesis, while others may have several related hypotheses. The number of hypotheses should be determined based on the research objectives, research questions, and the complexity of the research topic. It is important to ensure that the hypotheses are focused, testable, and directly related to the research aims.

9. Can research hypotheses be used in qualitative research?

Yes, research hypotheses can be used in qualitative research, although they are more commonly associated with quantitative research. In qualitative research, hypotheses may be formulated as tentative or exploratory statements that guide the investigation. Instead of testing hypotheses through statistical analysis, qualitative researchers may use the hypotheses to guide data collection and analysis, seeking to uncover patterns, themes, or relationships within the qualitative data. The emphasis in qualitative research is often on generating insights and understanding rather than confirming or rejecting specific research hypotheses through statistical testing.

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How to Write a Research Hypothesis: Good & Bad Examples

What is a research hypothesis?

A research hypothesis is an attempt at explaining a phenomenon or the relationships between phenomena/variables in the real world. Hypotheses are sometimes called “educated guesses”, but they are in fact (or let’s say they should be) based on previous observations, existing theories, scientific evidence, and logic. A research hypothesis is also not a prediction—rather, predictions are ( should be) based on clearly formulated hypotheses. For example, “We tested the hypothesis that KLF2 knockout mice would show deficiencies in heart development” is an assumption or prediction, not a hypothesis. 

The research hypothesis at the basis of this prediction is “the product of the KLF2 gene is involved in the development of the cardiovascular system in mice”—and this hypothesis is probably (hopefully) based on a clear observation, such as that mice with low levels of Kruppel-like factor 2 (which KLF2 codes for) seem to have heart problems. From this hypothesis, you can derive the idea that a mouse in which this particular gene does not function cannot develop a normal cardiovascular system, and then make the prediction that we started with. 

What is the difference between a hypothesis and a prediction?

You might think that these are very subtle differences, and you will certainly come across many publications that do not contain an actual hypothesis or do not make these distinctions correctly. But considering that the formulation and testing of hypotheses is an integral part of the scientific method, it is good to be aware of the concepts underlying this approach. The two hallmarks of a scientific hypothesis are falsifiability (an evaluation standard that was introduced by the philosopher of science Karl Popper in 1934) and testability —if you cannot use experiments or data to decide whether an idea is true or false, then it is not a hypothesis (or at least a very bad one).

So, in a nutshell, you (1) look at existing evidence/theories, (2) come up with a hypothesis, (3) make a prediction that allows you to (4) design an experiment or data analysis to test it, and (5) come to a conclusion. Of course, not all studies have hypotheses (there is also exploratory or hypothesis-generating research), and you do not necessarily have to state your hypothesis as such in your paper. 

But for the sake of understanding the principles of the scientific method, let’s first take a closer look at the different types of hypotheses that research articles refer to and then give you a step-by-step guide for how to formulate a strong hypothesis for your own paper.

Types of Research Hypotheses

Hypotheses can be simple , which means they describe the relationship between one single independent variable (the one you observe variations in or plan to manipulate) and one single dependent variable (the one you expect to be affected by the variations/manipulation). If there are more variables on either side, you are dealing with a complex hypothesis. You can also distinguish hypotheses according to the kind of relationship between the variables you are interested in (e.g., causal or associative ). But apart from these variations, we are usually interested in what is called the “alternative hypothesis” and, in contrast to that, the “null hypothesis”. If you think these two should be listed the other way round, then you are right, logically speaking—the alternative should surely come second. However, since this is the hypothesis we (as researchers) are usually interested in, let’s start from there.

Alternative Hypothesis

If you predict a relationship between two variables in your study, then the research hypothesis that you formulate to describe that relationship is your alternative hypothesis (usually H1 in statistical terms). The goal of your hypothesis testing is thus to demonstrate that there is sufficient evidence that supports the alternative hypothesis, rather than evidence for the possibility that there is no such relationship. The alternative hypothesis is usually the research hypothesis of a study and is based on the literature, previous observations, and widely known theories. 

Null Hypothesis

The hypothesis that describes the other possible outcome, that is, that your variables are not related, is the null hypothesis ( H0 ). Based on your findings, you choose between the two hypotheses—usually that means that if your prediction was correct, you reject the null hypothesis and accept the alternative. Make sure, however, that you are not getting lost at this step of the thinking process: If your prediction is that there will be no difference or change, then you are trying to find support for the null hypothesis and reject H1. 

Directional Hypothesis

While the null hypothesis is obviously “static”, the alternative hypothesis can specify a direction for the observed relationship between variables—for example, that mice with higher expression levels of a certain protein are more active than those with lower levels. This is then called a one-tailed hypothesis. 

Another example for a directional one-tailed alternative hypothesis would be that 

H1: Attending private classes before important exams has a positive effect on performance. 

Your null hypothesis would then be that

H0: Attending private classes before important exams has no/a negative effect on performance.

Nondirectional Hypothesis

A nondirectional hypothesis does not specify the direction of the potentially observed effect, only that there is a relationship between the studied variables—this is called a two-tailed hypothesis. For instance, if you are studying a new drug that has shown some effects on pathways involved in a certain condition (e.g., anxiety) in vitro in the lab, but you can’t say for sure whether it will have the same effects in an animal model or maybe induce other/side effects that you can’t predict and potentially increase anxiety levels instead, you could state the two hypotheses like this:

H1: The only lab-tested drug (somehow) affects anxiety levels in an anxiety mouse model.

You then test this nondirectional alternative hypothesis against the null hypothesis:

H0: The only lab-tested drug has no effect on anxiety levels in an anxiety mouse model.

How to Write a Hypothesis for a Research Paper

Now that we understand the important distinctions between different kinds of research hypotheses, let’s look at a simple process of how to write a hypothesis.

Writing a Hypothesis Step:1

Ask a question, based on earlier research. Research always starts with a question, but one that takes into account what is already known about a topic or phenomenon. For example, if you are interested in whether people who have pets are happier than those who don’t, do a literature search and find out what has already been demonstrated. You will probably realize that yes, there is quite a bit of research that shows a relationship between happiness and owning a pet—and even studies that show that owning a dog is more beneficial than owning a cat ! Let’s say you are so intrigued by this finding that you wonder: 

What is it that makes dog owners even happier than cat owners? 

Let’s move on to Step 2 and find an answer to that question.

Writing a Hypothesis Step 2:

Formulate a strong hypothesis by answering your own question. Again, you don’t want to make things up, take unicorns into account, or repeat/ignore what has already been done. Looking at the dog-vs-cat papers your literature search returned, you see that most studies are based on self-report questionnaires on personality traits, mental health, and life satisfaction. What you don’t find is any data on actual (mental or physical) health measures, and no experiments. You therefore decide to make a bold claim come up with the carefully thought-through hypothesis that it’s maybe the lifestyle of the dog owners, which includes walking their dog several times per day, engaging in fun and healthy activities such as agility competitions, and taking them on trips, that gives them that extra boost in happiness. You could therefore answer your question in the following way:

Dog owners are happier than cat owners because of the dog-related activities they engage in.

Now you have to verify that your hypothesis fulfills the two requirements we introduced at the beginning of this resource article: falsifiability and testability . If it can’t be wrong and can’t be tested, it’s not a hypothesis. We are lucky, however, because yes, we can test whether owning a dog but not engaging in any of those activities leads to lower levels of happiness or well-being than owning a dog and playing and running around with them or taking them on trips.  

Writing a Hypothesis Step 3:

Make your predictions and define your variables. We have verified that we can test our hypothesis, but now we have to define all the relevant variables, design our experiment or data analysis, and make precise predictions. You could, for example, decide to study dog owners (not surprising at this point), let them fill in questionnaires about their lifestyle as well as their life satisfaction (as other studies did), and then compare two groups of active and inactive dog owners. Alternatively, if you want to go beyond the data that earlier studies produced and analyzed and directly manipulate the activity level of your dog owners to study the effect of that manipulation, you could invite them to your lab, select groups of participants with similar lifestyles, make them change their lifestyle (e.g., couch potato dog owners start agility classes, very active ones have to refrain from any fun activities for a certain period of time) and assess their happiness levels before and after the intervention. In both cases, your independent variable would be “ level of engagement in fun activities with dog” and your dependent variable would be happiness or well-being . 

Examples of a Good and Bad Hypothesis

Let’s look at a few examples of good and bad hypotheses to get you started.

Good Hypothesis Examples

Bad Hypothesis Examples

Tips for Writing a Research Hypothesis

If you understood the distinction between a hypothesis and a prediction we made at the beginning of this article, then you will have no problem formulating your hypotheses and predictions correctly. To refresh your memory: We have to (1) look at existing evidence, (2) come up with a hypothesis, (3) make a prediction, and (4) design an experiment. For example, you could summarize your dog/happiness study like this:

(1) While research suggests that dog owners are happier than cat owners, there are no reports on what factors drive this difference. (2) We hypothesized that it is the fun activities that many dog owners (but very few cat owners) engage in with their pets that increases their happiness levels. (3) We thus predicted that preventing very active dog owners from engaging in such activities for some time and making very inactive dog owners take up such activities would lead to an increase and decrease in their overall self-ratings of happiness, respectively. (4) To test this, we invited dog owners into our lab, assessed their mental and emotional well-being through questionnaires, and then assigned them to an “active” and an “inactive” group, depending on… 

Note that you use “we hypothesize” only for your hypothesis, not for your experimental prediction, and “would” or “if – then” only for your prediction, not your hypothesis. A hypothesis that states that something “would” affect something else sounds as if you don’t have enough confidence to make a clear statement—in which case you can’t expect your readers to believe in your research either. Write in the present tense, don’t use modal verbs that express varying degrees of certainty (such as may, might, or could ), and remember that you are not drawing a conclusion while trying not to exaggerate but making a clear statement that you then, in a way, try to disprove . And if that happens, that is not something to fear but an important part of the scientific process.

Similarly, don’t use “we hypothesize” when you explain the implications of your research or make predictions in the conclusion section of your manuscript, since these are clearly not hypotheses in the true sense of the word. As we said earlier, you will find that many authors of academic articles do not seem to care too much about these rather subtle distinctions, but thinking very clearly about your own research will not only help you write better but also ensure that even that infamous Reviewer 2 will find fewer reasons to nitpick about your manuscript. 

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Now that you know how to write a strong research hypothesis for your research paper, you might be interested in our free AI Proofreader , Wordvice AI, which finds and fixes errors in grammar, punctuation, and word choice in academic texts. Or if you are interested in human proofreading , check out our English editing services , including research paper editing and manuscript editing .

On the Wordvice academic resources website , you can also find many more articles and other resources that can help you with writing the other parts of your research paper , with making a research paper outline before you put everything together, or with writing an effective cover letter once you are ready to submit.

5 Characteristics of a Good Hypothesis: A Guide for Researchers

• by Brian Thomas
• October 10, 2023

Are you a curious soul, always seeking answers to the whys and hows of the world? As a researcher, formulating a hypothesis is a crucial first step towards unraveling the mysteries of your study. A well-crafted hypothesis not only guides your research but also lays the foundation for drawing valid conclusions. But what exactly makes a hypothesis a good one? In this blog post, we will explore the five key characteristics of a good hypothesis that every researcher should know.

Here, we will delve into the world of hypotheses, covering everything from their types in research to understanding if they can be proven true. Whether you’re a seasoned researcher or just starting out, this blog post will provide valuable insights on how to craft a sound hypothesis for your study. So let’s dive in and uncover the secrets to formulating a hypothesis that stands strong amidst the scientific rigor!

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5 Characteristics of a Good Hypothesis

Clear and specific.

A good hypothesis is like a GPS that guides you to the right destination. It needs to be clear and specific so that you know exactly what you’re testing. Avoid vague statements or general ideas. Instead, focus on crafting a hypothesis that clearly states the relationship between variables and the expected outcome. Clarity is key, my friend!

Testable and Falsifiable

A hypothesis might sound great in theory, but if you can’t test it or prove it wrong, then it’s like chasing unicorns. A good hypothesis should be testable and falsifiable – meaning there should be a way to gather evidence to support or refute it. Don’t be afraid to challenge your hypothesis and put it to the test. Only when it can be proven false can it truly be considered a good hypothesis.

Based on Existing Knowledge

Imagine trying to build a Lego tower without any Lego bricks. That’s what it’s like to come up with a hypothesis that has no basis in existing knowledge. A good hypothesis is grounded in previous research, theories, or observations. It shows that you’ve done your homework and understand the current state of knowledge in your field. So, put on your research hat and gather those building blocks for a solid hypothesis!

Specific Predictions

No, we’re not talking about crystal ball predictions or psychic abilities here. A good hypothesis includes specific predictions about what you expect to happen. It’s like making an educated guess based on your understanding of the variables involved. These predictions help guide your research and give you something concrete to look for. So, put on those prediction goggles, my friend, and let’s get specific!

Relevant to the Research Question

A hypothesis is a road sign that points you in the right direction. But if it’s not relevant to your research question, then you might end up in a never-ending detour. A good hypothesis aligns with your research question and addresses the specific problem or phenomenon you’re investigating. Keep your focus on the main topic and avoid getting sidetracked by shiny distractions. Stay relevant, my friend, and you’ll find the answers you seek!

And there you have it: the five characteristics of a good hypothesis. Remember, a good hypothesis is clear, testable, based on existing knowledge, makes specific predictions, and is relevant to your research question. So go forth, my friend, and hypothesize your way to scientific discovery!

FAQs: Characteristics of a Good Hypothesis

In the realm of scientific research, a hypothesis plays a crucial role in formulating and testing ideas. A good hypothesis serves as the foundation for an experiment or study, guiding the researcher towards meaningful results. In this FAQ-style subsection, we’ll explore the characteristics of a good hypothesis, their types, formulation, and more. So let’s dive in and unravel the mysteries of hypothesis-making!

What Are Two Important Characteristics of a Good Hypothesis

A good hypothesis possesses two important characteristics:

Testability : A hypothesis must be testable to determine its validity. It should be formulated in a way that allows researchers to design and conduct experiments or gather data for analysis. For example, if we hypothesize that “drinking herbal tea reduces stress,” we can easily test it by conducting a study with a control group and a group drinking herbal tea.

Falsifiability : Falsifiability refers to the potential for a hypothesis to be proven wrong. A good hypothesis should make specific predictions that can be refuted or supported by evidence. This characteristic ensures that hypotheses are based on empirical observations rather than personal opinions. For instance, the hypothesis “all swans are white” can be falsified by discovering a single black swan.

What Are the Types of Hypothesis in Research

In research, there are three main types of hypotheses:

Null Hypothesis (H0) : The null hypothesis is a statement of no effect or relationship. It assumes that there is no significant difference between variables or no effect of a treatment. Researchers aim to reject the null hypothesis in favor of an alternative hypothesis.

Alternative Hypothesis (HA or H1) : The alternative hypothesis is the opposite of the null hypothesis. It asserts that there is a significant difference between variables or an effect of a treatment. Researchers seek evidence to support the alternative hypothesis.

Directional Hypothesis : A directional hypothesis predicts the specific direction of the relationship or difference between variables. For example, “increasing exercise duration will lead to greater weight loss.”

Can a Hypothesis Be Proven True

In scientific research, hypotheses are not proven true; they are supported or rejected based on empirical evidence . Even if a hypothesis is supported by multiple studies, new evidence could arise that contradicts it. Scientific knowledge is always subject to revision and refinement. Therefore, the goal is to gather enough evidence to either support or reject a hypothesis, rather than proving it absolutely true.

What Are the Six Parts of a Hypothesis

A hypothesis typically consists of six essential parts:

Research Question : A clear and concise question that the hypothesis seeks to answer.

Variables : Identification of the independent (manipulated) and dependent (measured) variables involved in the hypothesis.

Population : The specific group or individuals the hypothesis is concerned with.

Relationship or Comparison : The expected relationship or difference between variables, often indicated by directional terms like “more,” “less,” “higher,” or “lower.”

Predictability : A statement of the predicted outcome or result based on the relationship between variables.

Testability : The ability to design an experiment or gather data to support or reject the hypothesis.

How Do You Start a Hypothesis Sentence

When starting a hypothesis sentence, it is essential to use clear and concise language to express your ideas. A common approach is to use the phrase “If…then…” to establish the conditional relationship between variables. For example:

• If [independent variable], then [dependent variable] because [explanation of expected relationship].

This structure allows for a straightforward and logical formulation of the hypothesis.

What Are Examples of Hypotheses

Here are a few examples of well-formulated hypotheses:

If exposure to sunlight increases, then plants will grow taller because sunlight is necessary for photosynthesis.

If students receive praise for good grades, then their motivation to excel will increase because they seek recognition and approval.

If the dose of a painkiller is increased, then the relief from pain will last longer because a higher dosage has a prolonged effect.

What Are the Five Key Elements to a Good Hypothesis

A good hypothesis should include the following five key elements:

Clarity : The hypothesis should be clear and specific, leaving no room for interpretation.

Testability : It should be possible to test the hypothesis through experimentation or data collection.

Relevance : The hypothesis should be directly tied to the research question or problem being investigated.

Specificity : It must clearly state the relationship or difference between variables being studied.

Falsifiability : The hypothesis should make predictions that can be refuted or supported by empirical evidence.

What Makes a Good Hypothesis in a Research Paper

In a research paper, a good hypothesis should have the following characteristics:

Relevance : It must directly relate to the research topic and address the objectives of the study.

Clarity : The hypothesis should be concise and precisely worded to avoid confusion.

Unambiguous : It must leave no room for multiple interpretations or ambiguity.

Logic : The hypothesis should be based on rational and logical reasoning, considering existing theories and observations.

Empirical Support : Ideally, the hypothesis should be supported by prior empirical evidence or strong theoretical justifications.

Is a Hypothesis Always a Question

No, a hypothesis is not always in the form of a question. While some hypotheses can take the form of a question, others may be statements asserting a relationship or difference between variables. The form of a hypothesis depends on the research question being addressed and the researcher’s preferred style of expression.

What Are the Three Things Needed for a Good Hypothesis

For a hypothesis to be considered good, it must fulfill the following three criteria:

Testability : The hypothesis should be formulated in a way that allows for empirical testing through experimentation or data collection.

Falsifiability : It must make specific predictions that can be potentially refuted or supported by evidence.

Relevance : The hypothesis should directly address the research question or problem being investigated.

What Are the Four Components to a Good Hypothesis

A good hypothesis typically consists of four components:

Independent Variable : The variable being manipulated or controlled by the researcher.

Dependent Variable : The variable being measured or observed to determine the effect of the independent variable.

Directionality : The predicted relationship or difference between the independent and dependent variables.

Population : The specific group or individuals to which the hypothesis applies.

How Do You Formulate a Hypothesis

To formulate a hypothesis, follow these steps:

Identify the Research Topic : Clearly define the area or phenomenon you want to study.

Conduct Background Research : Review existing literature and research to gain knowledge about the topic.

Formulate a Research Question : Ask a clear and focused question that you want to answer through your hypothesis.

State the Null and Alternative Hypotheses : Develop a null hypothesis to assume no effect or relationship, and an alternative hypothesis to propose a significant effect or relationship.

Decide on Variables and Relationships : Determine the independent and dependent variables and the predicted relationship between them.

Refine and Test : Refine your hypothesis, ensuring it is clear, testable, and falsifiable. Then, design experiments or gather data to support or reject it.

What Is a Characteristic of a Hypothesis MCQ

Multiple-choice questions (MCQ) regarding the characteristics of a hypothesis often assess knowledge on the testability and falsifiability of hypotheses. They may ask about the criteria that distinguish a good hypothesis from a poor one or the importance of making specific predictions. Remember to choose answers that emphasize the empirical and testable nature of hypotheses.

What Five Criteria Must Be Satisfied for a Hypothesis to Be Scientific

For a hypothesis to be considered scientific, it must satisfy the following five criteria:

Testability : The hypothesis must be formulated in a way that allows it to be tested through experimentation or data collection.

Falsifiability : It should make specific predictions that can be potentially refuted or supported by empirical evidence.

Empirical Basis : The hypothesis should be based on empirical observations or existing theories and knowledge.

Relevance : It must directly address the research question or problem being investigated.

Objective : A scientific hypothesis should be free from personal biases or subjective opinions, focusing on objective observations and analysis.

What Are the Steps of Theory Development in Scientific Methods

In scientific methods, theory development typically involves the following steps:

Observation : Identifying a phenomenon or pattern worthy of investigation through observation or empirical data.

Formulation of a Hypothesis : Constructing a hypothesis that explains the observed phenomena or predicts a relationship between variables.

Data Collection : Gathering relevant data through experiments, surveys, observations, or other research methods.

Analysis : Analyzing the collected data to evaluate the hypothesis’s predictions and determine their validity.

Revision and Refinement : Based on the analysis, refining the hypothesis, modifying the theory, or formulating new hypotheses for further investigation.

Which of the Following Makes a Good Hypothesis

A good hypothesis is characterized by:

Testability : The ability to form experiments or gather data to support or refute the hypothesis.

Falsifiability : The potential for the hypothesis’s predictions to be proven wrong based on empirical evidence.

Clarity : A clear and concise statement or question that leaves no room for ambiguity.

Relevancy : Directly addressing the research question or problem at hand.

Remember, it is important to select the option that encompasses all these characteristics.

What Are the Characteristics of a Good Hypothesis

A good hypothesis possesses several characteristics, such as:

Testability : It should allow for empirical testing through experiments or data collection.

Falsifiability : The hypothesis should make specific predictions that can be potentially refuted or supported by evidence.

Clarity : It must be clearly and precisely formulated, leaving no room for ambiguity or multiple interpretations.

Relevance : The hypothesis should directly relate to the research question or problem being investigated.

What Is the Five-Step p-value Approach to Hypothesis Testing

The five-step p-value approach is a commonly used framework for hypothesis testing:

Step 1: Formulating the Hypotheses : The null hypothesis (H0) assumes no effect or relationship, while the alternative hypothesis (HA) proposes a significant effect or relationship.

Step 2: Setting the Significance Level : Decide on the level of significance (α), which represents the probability of rejecting the null hypothesis when it is true. The commonly used level is 0.05 (5%).

Step 3: Collecting Data and Performing the Test : Acquire and analyze the data, calculating the test statistic and the corresponding p-value.

Step 4: Comparing the p-value with the Significance Level : If the p-value is less than the significance level (α), reject the null hypothesis. Otherwise, fail to reject the null hypothesis.

Step 5: Drawing Conclusions : Based on the comparison in Step 4, interpret the results and draw conclusions about the hypothesis.

What Are the Stages of Hypothesis

The stages of hypothesis generally include:

Observation : Identifying a pattern, phenomenon, or research question that warrants investigation.

Formulation : Developing a hypothesis that explains or predicts the relationship or difference between variables.

Testing : Collecting data, designing experiments, or conducting studies to gather evidence supporting or refuting the hypothesis.

Analysis : Assessing the collected data to determine whether the results support or reject the hypothesis.

Conclusion : Drawing conclusions based on the analysis and making further iterations, refinements, or new hypotheses for future research.

What Is a Characteristic of a Good Hypothesis

A characteristic of a good hypothesis is its ability to make specific predictions about the relationship or difference between variables. Good hypotheses avoid vague statements and clearly articulate the expected outcomes. By doing so, researchers can design experiments or gather data that directly test the predictions, leading to meaningful results.

How Do You Write a Good Hypothesis Example

To write a good hypothesis example, follow these guidelines:

If possible, use the “If…then…” format to express a conditional relationship between variables.

Be clear and concise in stating the variables involved, the predicted relationship, and the expected outcome.

Ensure the hypothesis is testable, meaning it can be evaluated through experiments or data collection.

For instance, consider the following example:

If students study for longer periods of time, then their test scores will improve because increased study time allows for better retention of information and increased proficiency.

What Is the Difference Between Hypothesis and Hypotheses

The main difference between a hypothesis and hypotheses lies in their grammatical number. A hypothesis refers to a single statement or proposition that is formulated to explain or predict the relationship between variables. On the other hand, hypotheses is the plural form of the term hypothesis, commonly used when multiple statements or propositions are proposed and tested simultaneously.

What Is a Good Hypothesis Statement

A good hypothesis statement exhibits the following qualities:

Clarity : It is written in clear and concise language, leaving no room for confusion or ambiguity.

Testability : The hypothesis should be formulated in a way that enables testing through experiments or data collection.

Specificity : It must clearly state the predicted relationship or difference between variables.

By adhering to these criteria, a good hypothesis statement guides research efforts effectively.

What Is Not a Characteristic of a Good Hypothesis

A characteristic that does not align with a good hypothesis is subjectivity . A hypothesis should be objective, based on empirical observations or existing theories, and free from personal bias. While personal interpretations and opinions can inspire the formulation of a hypothesis, it must ultimately rely on objective observations and be open to empirical testing.

By now, you’ve gained insights into the characteristics of a good hypothesis, including testability, falsifiability, clarity,

• characteristics
• falsifiable
• good hypothesis
• hypothesis testing
• null hypothesis
• observations
• scientific rigor

Brian Thomas

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What Are the Elements of a Good Hypothesis?

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A hypothesis is an educated guess or prediction of what will happen. In science, a hypothesis proposes a relationship between factors called variables. A good hypothesis relates an independent variable and a dependent variable. The effect on the dependent variable depends on or is determined by what happens when you change the independent variable . While you could consider any prediction of an outcome to be a type of hypothesis, a good hypothesis is one you can test using the scientific method. In other words, you want to propose a hypothesis to use as the basis for an experiment.

Cause and Effect or 'If, Then' Relationships

A good experimental hypothesis can be written as an if, then statement to establish cause and effect on the variables. If you make a change to the independent variable, then the dependent variable will respond. Here's an example of a hypothesis:

If you increase the duration of light, (then) corn plants will grow more each day.

The hypothesis establishes two variables, length of light exposure, and the rate of plant growth. An experiment could be designed to test whether the rate of growth depends on the duration of light. The duration of light is the independent variable, which you can control in an experiment . The rate of plant growth is the dependent variable, which you can measure and record as data in an experiment.

Key Points of Hypothesis

When you have an idea for a hypothesis, it may help to write it out in several different ways. Review your choices and select a hypothesis that accurately describes what you are testing.

• Does the hypothesis relate an independent and dependent variable? Can you identify the variables?
• Can you test the hypothesis? In other words, could you design an experiment that would allow you to establish or disprove a relationship between the variables?
• Would your experiment be safe and ethical?
• Is there a simpler or more precise way to state the hypothesis? If so, rewrite it.

What If the Hypothesis Is Incorrect?

It's not wrong or bad if the hypothesis is not supported or is incorrect. Actually, this outcome may tell you more about a relationship between the variables than if the hypothesis is supported. You may intentionally write your hypothesis as a null hypothesis or no-difference hypothesis to establish a relationship between the variables.

For example, the hypothesis:

The rate of corn plant growth does not depend on the duration of light.

This can be tested by exposing corn plants to different length "days" and measuring the rate of plant growth. A statistical test can be applied to measure how well the data support the hypothesis. If the hypothesis is not supported, then you have evidence of a relationship between the variables. It's easier to establish cause and effect by testing whether "no effect" is found. Alternatively, if the null hypothesis is supported, then you have shown the variables are not related. Either way, your experiment is a success.

Need more examples of how to write a hypothesis ? Here you go:

• If you turn out all the lights, you will fall asleep faster. (Think: How would you test it?)
• If you drop different objects, they will fall at the same rate.
• If you eat only fast food, then you will gain weight.
• If you use cruise control, then your car will get better gas mileage.
• If you apply a top coat, then your manicure will last longer.
• If you turn the lights on and off rapidly, then the bulb will burn out faster.
• What Is a Testable Hypothesis?
• What Are Examples of a Hypothesis?
• What Is a Hypothesis? (Science)
• Scientific Hypothesis Examples
• Six Steps of the Scientific Method
• Scientific Method Flow Chart
• Null Hypothesis Examples
• Understanding Simple vs Controlled Experiments
• Scientific Method Vocabulary Terms
• Scientific Variable
• What Is an Experimental Constant?
• What Is a Controlled Experiment?
• What Is the Difference Between a Control Variable and Control Group?
• DRY MIX Experiment Variables Acronym
• Random Error vs. Systematic Error
• The Role of a Controlled Variable in an Experiment

How to Write a Hypothesis? Types and Examples 

All research studies involve the use of the scientific method, which is a mathematical and experimental technique used to conduct experiments by developing and testing a hypothesis or a prediction about an outcome. Simply put, a hypothesis is a suggested solution to a problem. It includes elements that are expressed in terms of relationships with each other to explain a condition or an assumption that hasn’t been verified using facts. 1 The typical steps in a scientific method include developing such a hypothesis, testing it through various methods, and then modifying it based on the outcomes of the experiments.  

A research hypothesis can be defined as a specific, testable prediction about the anticipated results of a study. 2 Hypotheses help guide the research process and supplement the aim of the study. After several rounds of testing, hypotheses can help develop scientific theories. 3 Hypotheses are often written as if-then statements. 

Here are two hypothesis examples: 

Dandelions growing in nitrogen-rich soils for two weeks develop larger leaves than those in nitrogen-poor soils because nitrogen stimulates vegetative growth. 4  

If a company offers flexible work hours, then their employees will be happier at work. 5  

Table of Contents

• What is a hypothesis? 
• Types of hypotheses 
• Characteristics of a hypothesis 
• Functions of a hypothesis 
• How to write a hypothesis 
• Hypothesis examples 
• Frequently asked questions 

What is a hypothesis?

A hypothesis expresses an expected relationship between variables in a study and is developed before conducting any research. Hypotheses are not opinions but rather are expected relationships based on facts and observations. They help support scientific research and expand existing knowledge. An incorrectly formulated hypothesis can affect the entire experiment leading to errors in the results so it’s important to know how to formulate a hypothesis and develop it carefully.

A few sources of a hypothesis include observations from prior studies, current research and experiences, competitors, scientific theories, and general conditions that can influence people. Figure 1 depicts the different steps in a research design and shows where exactly in the process a hypothesis is developed. 4  

There are seven different types of hypotheses—simple, complex, directional, nondirectional, associative and causal, null, and alternative. 

Types of hypotheses

The seven types of hypotheses are listed below: 5 , 6,7  

• Simple : Predicts the relationship between a single dependent variable and a single independent variable. 

Example: Exercising in the morning every day will increase your productivity.  

• Complex : Predicts the relationship between two or more variables. 

Example: Spending three hours or more on social media daily will negatively affect children’s mental health and productivity, more than that of adults.  

• Directional : Specifies the expected direction to be followed and uses terms like increase, decrease, positive, negative, more, or less. 

Example: The inclusion of intervention X decreases infant mortality compared to the original treatment.  

• Non-directional : Does not predict the exact direction, nature, or magnitude of the relationship between two variables but rather states the existence of a relationship. This hypothesis may be used when there is no underlying theory or if findings contradict prior research. 

Example: Cats and dogs differ in the amount of affection they express.  

• Associative and causal : An associative hypothesis suggests an interdependency between variables, that is, how a change in one variable changes the other.  

Example: There is a positive association between physical activity levels and overall health.  

A causal hypothesis, on the other hand, expresses a cause-and-effect association between variables. 

Example: Long-term alcohol use causes liver damage.  

• Null : Claims that the original hypothesis is false by showing that there is no relationship between the variables. 

Example: Sleep duration does not have any effect on productivity.  

• Alternative : States the opposite of the null hypothesis, that is, a relationship exists between two variables. 

Example: Sleep duration affects productivity.  

Characteristics of a hypothesis

So, what makes a good hypothesis? Here are some important characteristics of a hypothesis. 8,9  

• Testable : You must be able to test the hypothesis using scientific methods to either accept or reject the prediction. 
• Falsifiable : It should be possible to collect data that reject rather than support the hypothesis. 
• Logical : Hypotheses shouldn’t be a random guess but rather should be based on previous theories, observations, prior research, and logical reasoning. 
• Positive : The hypothesis statement about the existence of an association should be positive, that is, it should not suggest that an association does not exist. Therefore, the language used and knowing how to phrase a hypothesis is very important. 
• Clear and accurate : The language used should be easily comprehensible and use correct terminology. 
• Relevant : The hypothesis should be relevant and specific to the research question. 
• Structure : Should include all the elements that make a good hypothesis: variables, relationship, and outcome. 

Functions of a hypothesis

The following list mentions some important functions of a hypothesis: 1  

• Maintains the direction and progress of the research. 
• Expresses the important assumptions underlying the proposition in a single statement. 
• Establishes a suitable context for researchers to begin their investigation and for readers who are referring to the final report. 
• Provides an explanation for the occurrence of a specific phenomenon. 
• Ensures selection of appropriate and accurate facts necessary and relevant to the research subject. 

To summarize, a hypothesis provides the conceptual elements that complete the known data, conceptual relationships that systematize unordered elements, and conceptual meanings and interpretations that explain the unknown phenomena. 1  

How to write a hypothesis

Listed below are the main steps explaining how to write a hypothesis. 2,4,5  

• Make an observation and identify variables : Observe the subject in question and try to recognize a pattern or a relationship between the variables involved. This step provides essential background information to begin your research.  

For example, if you notice that an office’s vending machine frequently runs out of a specific snack, you may predict that more people in the office choose that snack over another. 

• Identify the main research question : After identifying a subject and recognizing a pattern, the next step is to ask a question that your hypothesis will answer.  

For example, after observing employees’ break times at work, you could ask “why do more employees take breaks in the morning rather than in the afternoon?” 

• Conduct some preliminary research to ensure originality and novelty : Your initial answer, which is your hypothesis, to the question is based on some pre-existing information about the subject. However, to ensure that your hypothesis has not been asked before or that it has been asked but rejected by other researchers you would need to gather additional information.  

For example, based on your observations you might state a hypothesis that employees work more efficiently when the air conditioning in the office is set at a lower temperature. However, during your preliminary research you find that this hypothesis was proven incorrect by a prior study. 

• Develop a general statement : After your preliminary research has confirmed the originality of your proposed answer, draft a general statement that includes all variables, subjects, and predicted outcome. The statement could be if/then or declarative.  
• Finalize the hypothesis statement : Use the PICOT model, which clarifies how to word a hypothesis effectively, when finalizing the statement. This model lists the important components required to write a hypothesis. 

P opulation: The specific group or individual who is the main subject of the research 

I nterest: The main concern of the study/research question 

C omparison: The main alternative group 

O utcome: The expected results  

T ime: Duration of the experiment 

Once you’ve finalized your hypothesis statement you would need to conduct experiments to test whether the hypothesis is true or false. 

Hypothesis examples

The following table provides examples of different types of hypotheses. 10 ,11  

Key takeaways  

Here’s a summary of all the key points discussed in this article about how to write a hypothesis. 

• A hypothesis is an assumption about an association between variables made based on limited evidence, which should be tested. 
• A hypothesis has four parts—the research question, independent variable, dependent variable, and the proposed relationship between the variables.   
• The statement should be clear, concise, testable, logical, and falsifiable. 
• There are seven types of hypotheses—simple, complex, directional, non-directional, associative and causal, null, and alternative. 
• A hypothesis provides a focus and direction for the research to progress. 
• A hypothesis plays an important role in the scientific method by helping to create an appropriate experimental design. 

Frequently asked questions

Hypotheses and research questions have different objectives and structure. The following table lists some major differences between the two. 9  

Here are a few examples to differentiate between a research question and hypothesis. 

Yes, here’s a simple checklist to help you gauge the effectiveness of your hypothesis. 9   1. When writing a hypothesis statement, check if it:  2. Predicts the relationship between the stated variables and the expected outcome.  3. Uses simple and concise language and is not wordy.  4. Does not assume readers’ knowledge about the subject.  5. Has observable, falsifiable, and testable results. 

As mentioned earlier in this article, a hypothesis is an assumption or prediction about an association between variables based on observations and simple evidence. These statements are usually generic. Research objectives, on the other hand, are more specific and dictated by hypotheses. The same hypothesis can be tested using different methods and the research objectives could be different in each case.     For example, Louis Pasteur observed that food lasts longer at higher altitudes, reasoned that it could be because the air at higher altitudes is cleaner (with fewer or no germs), and tested the hypothesis by exposing food to air cleaned in the laboratory. 12 Thus, a hypothesis is predictive—if the reasoning is correct, X will lead to Y—and research objectives are developed to test these predictions. 

Null hypothesis testing is a method to decide between two assumptions or predictions between variables (null and alternative hypotheses) in a statistical relationship in a sample. The null hypothesis, denoted as H 0 , claims that no relationship exists between variables in a population and any relationship in the sample reflects a sampling error or occurrence by chance. The alternative hypothesis, denoted as H 1 , claims that there is a relationship in the population. In every study, researchers need to decide whether the relationship in a sample occurred by chance or reflects a relationship in the population. This is done by hypothesis testing using the following steps: 13   1. Assume that the null hypothesis is true.  2. Determine how likely the sample relationship would be if the null hypothesis were true. This probability is called the p value.  3. If the sample relationship would be extremely unlikely, reject the null hypothesis and accept the alternative hypothesis. If the relationship would not be unlikely, accept the null hypothesis. 

To summarize, researchers should know how to write a good hypothesis to ensure that their research progresses in the required direction. A hypothesis is a testable prediction about any behavior or relationship between variables, usually based on facts and observation, and states an expected outcome.  

We hope this article has provided you with essential insight into the different types of hypotheses and their functions so that you can use them appropriately in your next research project. 

References  

• Dalen, DVV. The function of hypotheses in research. Proquest website. Accessed April 8, 2024. https://www.proquest.com/docview/1437933010?pq-origsite=gscholar&fromopenview=true&sourcetype=Scholarly%20Journals&imgSeq=1  
• McLeod S. Research hypothesis in psychology: Types & examples. SimplyPsychology website. Updated December 13, 2023. Accessed April 9, 2024. https://www.simplypsychology.org/what-is-a-hypotheses.html  
• Scientific method. Britannica website. Updated March 14, 2024. Accessed April 9, 2024. https://www.britannica.com/science/scientific-method  
• The hypothesis in science writing. Accessed April 10, 2024. https://berks.psu.edu/sites/berks/files/campus/HypothesisHandout_Final.pdf  
• How to develop a hypothesis (with elements, types, and examples). Indeed.com website. Updated February 3, 2023. Accessed April 10, 2024. https://www.indeed.com/career-advice/career-development/how-to-write-a-hypothesis  
• Types of research hypotheses. Excelsior online writing lab. Accessed April 11, 2024. https://owl.excelsior.edu/research/research-hypotheses/types-of-research-hypotheses/  
• What is a research hypothesis: how to write it, types, and examples. Researcher.life website. Published February 8, 2023. Accessed April 11, 2024. https://researcher.life/blog/article/how-to-write-a-research-hypothesis-definition-types-examples/  
• Developing a hypothesis. Pressbooks website. Accessed April 12, 2024. https://opentext.wsu.edu/carriecuttler/chapter/developing-a-hypothesis/  
• What is and how to write a good hypothesis in research. Elsevier author services website. Accessed April 12, 2024. https://scientific-publishing.webshop.elsevier.com/manuscript-preparation/what-how-write-good-hypothesis-research/  
• How to write a great hypothesis. Verywellmind website. Updated March 12, 2023. Accessed April 13, 2024. https://www.verywellmind.com/what-is-a-hypothesis-2795239  
• 15 Hypothesis examples. Helpfulprofessor.com Published September 8, 2023. Accessed March 14, 2024. https://helpfulprofessor.com/hypothesis-examples/ 
• Editage insights. What is the interconnectivity between research objectives and hypothesis? Published February 24, 2021. Accessed April 13, 2024. https://www.editage.com/insights/what-is-the-interconnectivity-between-research-objectives-and-hypothesis  
• Understanding null hypothesis testing. BCCampus open publishing. Accessed April 16, 2024. https://opentextbc.ca/researchmethods/chapter/understanding-null-hypothesis-testing/#:~:text=In%20null%20hypothesis%20testing%2C%20this,said%20to%20be%20statistically%20significant  

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Hypothesis: meaning, criteria for formulation and it’s types.

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Read this article to learn about the meaning, criteria for formulation and types of hypothesis.

Meaning of Hypothesis:

In order to make the problem explicit and in order to focus attention in its solution, it is essential to start with certain known theories. Research, in real terms, depends upon a continuous interplay of theory and facts, upon a continuous stimulation of facts by theory and theory by facts. Theory is initiated by facts and facts lead to the rejection or reformulation of existing theory. Facts may also redefine or clarify the theory.

Hampel has compared a scientific theory to a network in which the terms and concepts are represented by knots and definitions and hypothesis by threads connecting the knots. From certain observational data we derive an interpretative string to some points in the theoretical framework. Then we proceed through definitions and hypothesis to other points from which another interpretative string permits to the plane of observation.

Theory thus gives meaning to empirically observed facts and puts them systematically. Theory is also built upon facts and various facts put in a theoretically framework may be analyzed and interpreted in a logical manner. Grounded on old facts and with the help of theoretical framework, new facts are discovered. In the process, certain deductions are formulated which are called hypotheses.

Thus “after internalizing the problem, after turning back on experience for possible solutions, after observing relevant phenomena, the scientist may formulate a hypothesis.” “A Hypothesis is a conjectural statement, a tentative proposition about relation between two or more phenomena or variables”. It is a tentative generalization, the validity of which remains to be tested.

At its initial stage, a hypothesis may be an imagined idea or a hunch or a mere guess. It is in the form of a declarative sentence and always indicates relation of one or more variable(s) with other variable(s) in a general or specific way. It is mostly based on accumulated knowledge. A hypothesis is made to examine the correct explanation of a phenomenon through investigation, to observe facts on the basis of collected data. If on the basis of verification, the hypothesis is found to be valid, a theory is obtained. Thus, hypothesis a theory entertained in order to study the facts and find out the validity of the theory.

The etymological meaning of hypothesis, therefore, is a theory which is not full reasoned, derived out of the combination of two words ‘hypo’ and ‘thesis’ meaning ‘less than’ and ‘reasoned theory of rational view point’ respectively. Accordingly Mill defines hypothesis as “any supposition which we make (either without actual evidence or an evidence avowedly insufficient) in order to endeavor to deduce conclusions in accordance with facts which are known to be real, under the idea that if the conclusions to which the hypothesis leads are known truths, the hypothesis itself either must be or at least likely to be, true”. Likewise, Goode and Hatt define it as “a proposition which can be put to test to determine validity”.

P.V. Young says that a hypothesis “is provisional central idea which becomes the basis for fruitful investigation, known as working theory” Coffey defines hypothesis as “an attempt at explanation : a provisional supposition made in order to explain scientifically some facts or phenomena”. Hypothesis is not a theory; rather hypotheses are linked and related to theory which is more elaborate in nature as compared to hypothesis.

Therefore William H. George, while distinguishing between theory and hypothesis, described theory as ‘elaborate hypothesis’. Hypothesis is not a claim of truth, but a claim for truth and hence serves as a bridge in the process of investigation which begins with a problem and ends with resolution of the problem. In the words of Cohen and Nagel “a hypothesis directs our search for the order.”

Criteria for Formulation of Hypothesis :

There exist two criteria for formulation of a good hypothesis. First, it is a statement about the relations between variables. Secondly it carries clear implications for testing the stated relations. Thus, these couple of criteria imply that the hypotheses comprise two or more variables which are measurable or potentially measurable and that they specify the way in which they are related. A statement which fails to meet these criteria is no scientific hypothesis in the true sense of the term. However, there are legitimate hypotheses, formulated in factor analytic studies.

The following examples may be cited in order to justify how the couple of criteria apply to hypotheses:

1. More intelligent persons will be less hostile than those of lower level of intelligence.

2. Group study contributes to higher grade achievement.

In the first hypothesis, we visualize a relation stated between one variable, ‘intelligence’, and another variable ‘hostility.’ Furthermore, measurement of these variables is also easily conceivable. In the second example, a relation has also been stated between the variables ‘group study’ and ‘grade achievement.’ There exists the possibility of the measurement of the variables are thus there is implication for testing the hypotheses. Thus both the criteria are satisfied. ‘

Types of Hypothesis :

Hypotheses may be of various kinds. It may be crude or refined. A crude hypothesis is at the lower level of abstraction, indicating only the kind of data to be collected, not leading to higher theoretical research. On the contrary, the refined hypothesis appears to be more significant in research.

It may be in the form of describing something in a given instance, that a particular object, situation or event has certain characteristics. It may be in the form of counting the frequencies or of association among the variables. It may be in the form of causal relationship that a particular characteristic or occurrence is one of the causes determining the other.

On the basis of levels of abstraction, Goode and Hatt have distinguished three broad types of hypotheses.

First, there are the simple levels of hypotheses indicating merely the uniformity in social behaviour. They are the most exact and the least abstract, as they state the existence of presence of empirical uniformities. Often it is said that such hypotheses do not involve much verification or do not require testing at all and they merely add up facts. But it is not correct to say so. Even empirical researches describing certain facts need testing of hypotheses and testing may result in providing with an altogether different profile.

Secondly, there are complex ideal hypotheses at a higher level of abstraction. These are more complex and aim at testing the existence of logically derived relationships between empirical uniformities. They are in the form of generalization, and therefore are also a little abstract. But empirical relationships are important in their context. Such hypotheses are useful in developing tools of analysis and in providing constructs for further hypothesizing.

Thirdly, there are hypotheses which are very complex and quite abstract. They are concerned with the interrelations of multiple analytic variables. They lead to the formulation of a relationship between changes in one property and changes in another.

The above kinds of hypotheses may be explained in an example. On the basis of empirical data we may show statistical regularity by wealth, religion region, size of community culture, tradition, health etc. First, we may formulate hypotheses in a simple manner on the basis of statistical regularity. Secondly, in order to formulate a complex ideal hypothesis we may combine all the factors together. As regards the formulation of the third category of hypothesis, more abstraction is brought in.

Only one of the factors can be studied at a time, such as relationship between religion and fertility or wealth and fertility, and all other variables may be controlled. Obviously, it is a very abstract way of handling the problem, because people may be affected by a multiplicity of variables. Yet, we are interested in studying the cause and effect relationship of one factor at one time. Hence, this level of hypothesizing is not only more abstract, simultaneously it is more sophisticated and provides scope for further research.

Related Articles:

• Conditions for a Valid Hypothesis: 5 Conditions
• Sources of Hypothesis in Social Research: 4 Sources

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Active funding opportunity

Nsf 24-582: nsf small business innovation research / small business technology transfer fast-track pilot programs (sbir-sttr fast-track), program solicitation, document information, document history.

• Posted: June 17, 2024

Program Solicitation NSF 24-582

Full Proposal Deadline(s) (due by 5 p.m. submitting organization’s local time):

     September 18, 2024

     November 06, 2024

     March 05, 2025

     July 02, 2025

     November 05, 2025

Important Information And Revision Notes

The NSF SBIR/STTR Fast-Track programs (also known as America’s Seed Fund powered by NSF) provide non-dilutive, fixed amount cooperative agreements for the development of a broad range of technologies based on discoveries in science and engineering with the potential for societal and economic impacts .

This new pilot effort shares the same goals as the NSF SBIR/STTR Phase I and Phase II funding opportunities, but the NSF SBIR/STTR Fast-Track pilot programs have different eligibility requirements. Small businesses applying to the NSF SBIR/STTR Fast-Track pilot programs must have a lineage of NSF research funding, at least one Senior/Key Personnel to have undergone formal customer discovery training, and the entire team must already be in place (not yet to be determined) at the time of proposal submission. For further information see Eligibility Criteria.

The maximum total SBIR/STTR Fast-Track award amount is $1,555,000 (inclusive of direct and indirect costs, Technical and Business Assistance (TABA) funding, and the small business fee) : $400,000 maximum for the Phase I component and $1,155,000 maximum for the Phase II component. The expected project duration will be between 24 months and 36 months. The duration of a Phase I component can be between 6 months and 12 months, to be specified by the company. The duration of a Phase II component can be between 18 months and 24 months, to be specified by the company.

NSF proposals are confidential and will only be shared with a select number of reviewers and NSF staff (as appropriate). All reviewers have agreed to maintain the confidentiality of the proposal content. Proposals to NSF do not constitute a public disclosure. If selected for an award, the company will be prompted to write a publicly available abstract that summarizes the intellectual merit and broader impact of the project.

The NSF SBIR/STTR Fast-Track pilot programs do not support clinical trials or proposals from companies whose commercialization pathway involves the production, distribution, or sale by the company of chemical components, natural or synthetic variations thereof, or other derivatives related to Schedule I controlled substances.

All proposals must be submitted through Research.gov .

NSF SBIR/STTR Fast-Track pilot proposals will not be accepted in Grants.gov. NSF Fast-Track SBIR and STTR pilot proposals are nearly identical but differ in the amount of work performed by the small business and a not-for-profit institution or a Federally funded research and development center (FFRDC) (as noted in the budget). For more details about the unique requirements of NSF STTR Fast-Track pilot awards, please refer to the Eligibility Information and Proposal Preparation and Submission Instructions sections of this solicitation.

NSF SBIR Fast-Track Pilot proposals submitted to this solicitation that meet all the requirements of an NSF STTR Fast-Track pilot proposal may, at NSF’s discretion, be converted to NSF STTR Fast-Track pilot proposal for award. Similarly, NSF STTR Fast-Track pilot proposals may be converted to NSF SBIR Fast-Track pilot awards at NSF’s discretion.

America’s Seed Fund powered by NSF is committed to assisting SBIR/STTR Phase II recipients to successfully commercialize their innovation research, grow their company and create jobs by attracting new investments and partnerships. To reinforce these commitments, the programs support a broad number of supplements and other opportunities . For more information, see: Supplemental Funding Overview , and the linked Dear Colleagues Letters.

• Funding Agreement : As used in this solicitation, the funding agreement is a Grant – a legal instrument of financial assistance between NSF and a recipient, consistent with 31 USC 6302-6305 and as noted in the NSF Proposal & Award Policies & Procedures Guide (PAPPG) Introduction, Section D ("Definitions & NSF-Recipient Relationships").
• Small Business Concerns (SBCs) : SBCs are independently owned and operated businesses that are not dominant in the field of operation. For this solicitation, firms qualifying as a small business concern are eligible to participate in the SBIR/STTR programs (see Section II. "Eligibility Information" of this solicitation for more details). Please note that the size limit of 500 employees includes affiliates. The firm must be in compliance with the SBA SBIR/STTR Policy Directive and the Code of Federal Regulations (13 CFR 121) .
• SBIR/STTR Data : As defined by the SBA SBIR/STTR Policy Directive , SBIR/STTR Data is all Data developed or generated in the performance of an SBIR or STTR award, including Technical Data and Computer Software developed or generated in the performance of an SBIR or STTR award. The term does not include information incidental to contract or grant administration, such as financial, administrative, cost or pricing or management information.
• SBIR/STTR Data Rights : The Federal Government may, use, modify, reproduce, perform, display, release, or disclose SBIR/STTR Data that are Technical Data within the Government; however, the Government shall not use, release, or disclose the data for procurement, manufacturing, or commercial purposes; or release or disclose the SBIR/STTR Data outside the Government except as permitted by paragraph 10(B) of the SBIR/STTR Policy Directive 's Data Rights Clause or by written permission of the recipient.
• the application of creative, original, and potentially transformative concepts to systematically study, create, adapt, or manipulate the structure and behavior of the natural or man-made worlds;
• the use of the scientific method to propose well-reasoned, well-organized activities based on sound theory, computation, measurement, observation, experiment, or modeling;
• the demonstration of a well-qualified individual, team, or organization ready to deploy novel methods of creating, acquiring, processing, manipulating, storing, or disseminating data or metadata; and/or
• the novel integration of new theories, analysis, data, or methods regarding cognition, heuristics, and related phenomena, which can be supported by scientific rationale.
• Non-Dilutive Funding : financing that does not involve equity, debt, or other elements of the business ownership structure.
• Technical Risk : Technical risk assumes that the possibility of technical failure exists for an envisioned product, service, or solution to be successfully developed. This risk is present even to those suitably skilled in the art of the component, subsystem, method, technique, tool, or algorithm in question. If the new product, service, or solution is successfully realized and brought to the market, it would be difficult for a well-qualified, competing firm to reverse-engineer or otherwise neutralize the competitive advantage generated by leveraging fundamental science or engineering research techniques.
• Technological Innovation  indicates that the new product or service is differentiated from current products or services; that is, the new technology holds the potential to result in a product or service with a substantial and durable advantage over competing solutions on the market. It also generally provides a barrier to entry for competitors.

The proposal submission system, Research.gov, will stop accepting proposals at 5:00 pm submitting organization’s local time. If your submission is late, you will not be able to submit again until the next deadline. Proposers are strongly urged to submit well in advance of the deadline.

An Intellectual Property (IP) Rights agreement is required for STTR proposals and strongly recommended for SBIR proposals when there is a subaward to another institution . A fully signed agreement is not required for STTR proposals at the initial proposal submission but will be required before a recommendation for an award can be made.

A small business must receive an official invitation via the Project Pitch , a process to submit a full Fast-Track proposal. Details regarding this process as well as how to submit a Fast-Track Project Pitch can be found in Section III.A. of this document. Small businesses that meet the Fast-Track eligibility criteria can submit a Fast-Track Project Pitch at any time. Small businesses that have been invited to submit a full Fast-Track proposal can submit a proposal based on that Project Pitch at any time up to 4 months after the date of the invitation.

In addition to the standard NSF Merit Review Criteria, this solicitation provides additional clarification on how Intellectual Merit and Broader Impact might be applied to startups and small businesses. Additional solicitation-specific merit review criteria focused on Commercialization Potential is also applied.

Four documents: Biographical Sketch(es), Current and Pending (Other) Support forms, Collaborators and Other Affiliations (COA), and Synergistic Activities must be submitted for the PI, Co-PI (if STTR), and each Senior/Key Personnel specified in the proposal. Biographical Sketches and Current and Pending Support forms must be prepared using SciENcv: Science Experts Network Curriculum Vitae . Collaborators & Other Affiliations (COA) Information is prepared using the instructions and spreadsheet template .

Synergistic Activities. Each individual identified as a Senior/Key person must provide a document of up to one-page that includes a list of up to five distinct examples of synergistic activities that demonstrate the broader impact of the individual’s professional and scholarly activities that focus on the integration and transfer of knowledge as well as its creation.

In compliance with the CHIPS and Science Act of 2022 , section 10636 (Person or entity of concern prohibition; 42 U.S.C. 19235 ): No person published on the list under section 1237(b) of the Strom Thurmond National Defense Authorization Act for Fiscal Year 1999 ( Public Law 105-261 ; 50 U.S.C. 1701 note ) or entity identified under section 1260h of the William M. (Mac) Thornberry National Defense Authorization Act for Fiscal Year 2021 ( 10 U.S.C. 113 note ; Public Law 116-283 ) may receive or participate in any grant, award, program, support, or other activity under the Directorate for Technology, Innovation and Partnerships (TIP) .

In accordance with Section 10632 of the CHIPS and Science Act of 2022 (42 U.S.C. § 19232), the Authorized Organizational Representative (AOR) must certify that all individuals identified as Senior/Key Personnel have been made aware of and have complied with their responsibility under that section to certify that the individual is not a party to a Malign Foreign Talent Recruitment Program.

In accordance with Section 223(a)(1) of the William M. (Mac) Thornberry National Defense Authorization Act for Fiscal Year 2021 (42 U.S.C. § 6605(a)(1)), each individual identified as Senior/Key Personnel is required to certify in SciENcv that the information provided in the Biographical Sketch and Current and Pending (Other) Support documents are accurate, current, and complete. Senior/Key Personnel are required to update their Current and Pending (Other) Support disclosures prior to award, and at any subsequent time the agency determines appropriate during the term of the award. See additional information on NSF Disclosure Requirements in the PAPPG, Chapter II.B. Each Senior/Key Person must also certify prior to proposal submission that they are not a party to a Malign Foreign Talent Recruitment Program and annually thereafter for the duration of the award.

Three (3) Letters of Support from potential product/service users or customers are required; Up to five (5) Letters of Support may be submitted.

Letters of Commitment that confirm the role of any subaward organization(s) in the project and explicitly state the subaward amount are also required.

Additional information on the due diligence process , used as part of the review and selection process, is included in Section VI. The due diligence process may include requests for clarification of the company structure, key personnel, conflicts of interest, foreign influence, cybersecurity practices, or other issues as determined by NSF. Participation in the due diligence process is not a guarantee that an award will be made.

SBIR/STTR Fast-Track proposals that have been declined by NSF are NOT eligible for reconsideration . A decision by NSF not to provide additional funding following either the Stage Gate 1 or Stage Gate 2 review will NOT be eligible for reconsideration or termination review as defined in Chapter XII.A.4 of the PAPPG .

This solicitation contains many instructions that deviate from the standard NSF PAPPG proposal preparation instructions. In the event of a conflict between the instructions in this solicitation and the PAPPG, use this solicitation’s instructions as a guide.

Any proposal submitted in response to this solicitation should be submitted in accordance with the NSF Proposal & Award Policies & Procedures Guide (PAPPG) that is in effect for the relevant due date to which the proposal is being submitted. The NSF PAPPG is regularly revised and it is the responsibility of the proposer to ensure that the proposal meets the requirements specified in this solicitation and the applicable version of the PAPPG. Submitting a proposal prior to a specified deadline does not negate this requirement.

Summary Of Program Requirements

General information.

Program Title:

NSF Small Business Innovation Research / Small Business Technology Transfer Fast-Track Pilot Programs (SBIR-STTR Fast-Track)

The NSF SBIR/STTR and SBIR/STTR Fast-Track pilot programs support moving scientific excellence and technological innovation from the lab to the market. By funding startups and small businesses, NSF helps build a strong national economy and stimulates the creation of novel products, services, and solutions in private, public, or government sectors with potential for broad impact; strengthens the role of small business in meeting federal research and development needs; increases the commercial application of federally supported research results; and develops and increases the US workforce, especially by fostering and encouraging participation by socially and economically disadvantaged and women-owned small businesses. These NSF SBIR/STTR Fast-Track pilot programs provide fixed amount cooperative agreements for the development of a broad range of technologies based on discoveries in science and engineering with potential for societal and economic impacts. Unlike fundamental or basic research activities that focus on scientific and engineering discovery itself, the NSF SBIR/STTR Fast-Track pilot programs support the creation of opportunities to move use-inspired and translational discoveries out of the lab and into the market or other use at scale, through startups and small businesses. The NSF SBIR/STTR Fast-Track pilot programs do not solicit specific technologies or procure goods and services from startups and small businesses. Any invention conceived or reduced to practice with the assistance of SBIR/STTR funding is subject to the Bayh-Dole Act. For more information refer to SBIR/STTR Frequently Asked Questions #75 . NSF promotes inclusion by encouraging proposals from diverse populations and geographic locations. The traditional NSF SBIR/STTR programs include two funding Phases – Phase I and Phase II. All proposers to the programs must first apply for Phase I funding – there is no direct-to-Phase II option. Under a traditional NSF SBIR/STTR Phase I award, a small business can receive non-dilutive funding for research and development (R&D) to demonstrate technical feasibility over 6 to 12 months and then, after completion of a Phase I project, companies may apply for Phase II funding to further develop the proposed technology. There are significant benefits for SBIR/STTR Fast-Track recipients: the submission of only one proposal for Phase I and Phase II and a faster transition from Phase I to Phase II. While startups and small businesses face many challenges, NSF SBIR/STTR Fast-Track funding is intended to specifically focus on challenges associated with technological innovation; that is, on the creation of new products, services, and other scalable solutions based on fundamental science or engineering. A successful Fast-Track proposal must demonstrate how NSF funding will help the small business create a proof-of-concept or prototype by retiring technical risk. NSF seeks unproven, leading-edge, technology innovations that demonstrate the following characteristics: The innovations are underpinned and enabled by a new scientific discovery or meaningful engineering innovation. The innovations still require intensive technical research and development to be fully embedded in a reliable product or service. The innovations have not yet been reduced to practice by anyone and it is not guaranteed, at present, that doing so is technically possible. The innovations provide a strong competitive advantage that are not easily replicable by competitors (even technically proficient ones). Once reduced to practice, the innovations are expected to result in a product or service that would either be disruptive to existing markets or create new markets/new market segments. The NSF SBIR/STTR Fast-Track pilot programs focus on stimulating technical innovation from diverse entrepreneurs and start-ups by translating new scientific and engineering concepts into products and services that can be scaled and commercialized into sustainable businesses with significant societal benefits. The programs provide non-dilutive funding for research and development (R&D) of use-inspired scientific and engineering activities at the earliest stages of the company and technology development. During the course of the award, the emphasis is expected to shift from de-risking those aspects preventing the innovation from reaching technical feasibility and driving the intended impact to a greater focus on commercially relevant development activities that will allow the company to differentiate itself and drive new value propositions to the market and society. NSF encourages input and participation from the full spectrum of diverse talent that society has to offer which includes underrepresented and underserved communities. These NSF programs are governed by 15 USC 638 and the National Science Foundation Act of 1950, as amended ( 42 USC §1861, et seq. ). Introduction to the Program The NSF SBIR/STTR programs focus on stimulating technical innovation from diverse entrepreneurs and startups by translating new scientific and engineering discoveries emerging from the private sector, federal labs, and academia into products and services that can be scaled and commercialized into sustainable businesses with significant societal benefits. These NSF SBIR/STTR Fast-Track pilot programs enable companies based on previous NSF awards (NSF award lineage) to submit a single proposal that, if awarded, can provide a faster pathway from Phase I to Phase II funding. Receipt of full funding under the Fast-Track pilot programs is contingent on the results of a company’s Phase II transition review. The NSF SBIR/STTR Fast-Track pilot programs are part of the Directorate for Technology, Innovation and Partnerships (TIP) , which was recently launched to accelerate innovation and enhance economic competitiveness by catalyzing partnerships and investments that strengthen the links between fundamental research and technology development, deployment, and use.

Cognizant Program Officer(s):

Please note that the following information is current at the time of publishing. See program website for any updates to the points of contact.

NSF SBIR/STTR Inbox, telephone: (703) 292-5111, email: [email protected]

• 47.041 --- Engineering
• 47.049 --- Mathematical and Physical Sciences
• 47.050 --- Geosciences
• 47.070 --- Computer and Information Science and Engineering
• 47.074 --- Biological Sciences
• 47.075 --- Social Behavioral and Economic Sciences
• 47.076 --- STEM Education
• 47.079 --- Office of International Science and Engineering
• 47.083 --- Office of Integrative Activities (OIA)
• 47.084 --- NSF Technology, Innovation and Partnerships

Award Information

Anticipated Type of Award: Fixed Amount Cooperative Agreement

• Approximately 20 awards for SBIR Fast-Track, pending the availability of funds.
• Approximately 16 awards for STTR Fast-Track, pending the availability of funds.
• Approximately $31 M for SBIR Fast-Track
• Approximately $25 M for STTR Fast-Track

Estimated program budget, number of awards and average award size/duration are subject to the availability of funds.

Eligibility Information

Who May Submit Proposals:

Proposals may only be submitted by the following: Small businesses concerns must meet ALL of the following requirements: Proposers that have submitted a SBIR/STTR Fast-Track Project Pitch and received an official invitation from a cognizant NSF SBIR/STTR Program Officer within the 4 months preceding the proposal submission date. To start this process, proposers must first create a log in and submit a Project Pitch document via the NSF SBIR/STTR Fast-Track Project Pitch online form . The cognizant NSF SBIR/STTR Program Officer will use the Project Pitch to determine whether the proposed project is a good fit for the Fast-Track program. Companies qualifying as a small business concern are eligible to participate in the NSF SBIR/STTR Fast-Track pilot programs (see Guide to SBIR/STTR Program Eligibility for more information). Please note that the size limit of 500 employees includes affiliates. The firm must be in compliance with the SBIR/STTR Policy Directive and the Code of Federal Regulations . For STTR proposals, the proposing small business must also include a partner research institution in the project, see additional details below. The SBIR/STTR Fast-Track pilot effort shares the same goals as the NSF SBIR/STTR Phase I and Phase II funding opportunities, but the Fast-Track pilot programs have different eligibility requirements. Small businesses applying to the NSF SBIR/STTR Fast-Track pilot programs must have 1) a lineage of NSF research funding, 2) at least one Senior/Key Personnel to have undergone formal customer discovery training, and 3) the entire team must already be in place (not yet to be determined) at the time of proposal submission. If the small business concern does not meet all three of these criteria, their proposal will be transferred to the NSF SBIR/STTR Phase I program for consideration. Lineage Eligibility Requirement. The technical innovation in the Fast-Track proposal must be derived from a prior NSF research award that is either currently active or was active within the previous five years from the date of submission of the Fast-Track proposal. The Fast-Track Project Pitch and proposal must include the NSF award number and title of the research award that is relied upon to meet the lineage requirement. The Fast-Track proposal’s PI or at least one Senior/Key Personnel must have been supported under the lineage award. If the Fast-Track team member relied upon to meet the lineage requirement is named on the lineage award, no further documentation will be required. If not, the Fast-Track proposal must include a letter from the PI or a Co-PI of the lineage award confirming that either the PI or a named Senior/Key Personnel on the Fast-Track team was engaged in research undertaken under the lineage award. In addition to regular NSF research awards (e.g., CAREER, individual investigator awards, center/institute awards, etc.), Partnerships for Innovation (PFI) and NSF Graduate Research Fellowship Program (GRFP) awards do count as NSF lineage for SBIR/STTR Fast-Track eligibility. NSF Innovation Corps (I-Corps) and NSF SBIR/STTR awards do not count as NSF research lineage and do not convey SBIR/STTR Fast-Track eligibility . Formal Customer Discovery Eligibility Requirement. Companies must have received formal customer discovery training, defined as follows, within the previous two years from the date of the Fast-Track proposal submission. At least one of the Senior/Key Personnel on the Fast-Track proposal must have undergone formal customer discovery training in relation to the proposed technology via a suitably qualified program, such as the NSF I-Corps program or a program at an incubator or accelerator, with a result that at the start of the Fast-Track project the proposing company has a clear understanding of the product-market fit and initial target customers for the proposed technology. Complete Team Eligibility Requirement. Companies must have a complete Fast-Track team in place at the time of proposal submission – i.e., there must be no “to-be-determined” company personnel in budget lines A or B; all company personnel in budget lines A and B must have confirmed their availability for the proposed Fast-Track project per the proposed Phase I and Phase II component budgets; the proposing team must possess the required expertise to perform the proposed Fast-Track project; and the team members must dedicate sufficient time to the technical tasks that must be undertaken to achieve the objectives of the Fast-Track project. In compliance with the CHIPS and Science Act of 2022 , Section 10636 (Person or entity of concern prohibition; 42 U.S.C. 19235): No person published on the list under section 1237(b) of the Strom Thurmond National Defense Authorization Act for Fiscal Year 1999 (Public Law 105-261; 50 U.S.C. 1701 note) or entity identified under section 1260h of the William M. (Mac) Thornberry National Defense Authorization Act for Fiscal Year 2021 (10 U.S.C. 113 note; Public Law 116-283) may receive or participate in any grant, award, program, support, or other activity under the Directorate for Technology, Innovation, and Partnerships. Individuals who are a current party to a Malign Foreign Talent Recruitment Program are not eligible to serve as a Senior/Key Person on an NSF proposal or on any NSF award made after May 20, 2024. See current PAPPG for additional information on required certifications associated with Malign Foreign Talent Organization. The Authorized Organizational Representative (AOR) must certify that all individuals identified as Senior/Key Personnel have been made aware of and have complied with their responsibility under that section to certify that the individual is not a party to a Malign Foreign Talent Recruitment Program. The small business concern’s R&D must be performed within the United States. Startups and small businesses funded by NSF must be majority U.S.-owned companies. The companies may not be majority-owned by one or more venture capital operating companies (VCOCs), hedge funds, or private equity firms. Proposals from joint ventures and partnerships are permitted, provided the proposing entity qualifies as a small business concern (see Guide to SBIR/STTR Program Eligibility for more information). “Collaborative Proposal from Multiple Organizations” (a special proposal type in Research.gov) are not allowed. Socially and economically disadvantaged small businesses and women-owned small businesses are also encouraged to apply.

Who May Serve as PI:

The primary employment of the Principal Investigator (PI) must be with the small business concern at the time of award and for the duration of the award, unless a new PI is approved by NSF. Primary employment is defined as at least 51 percent employed by the small business. NSF normally considers a full-time work week to be 40 hours and considers employment elsewhere of greater than 19.6 hours per week to be in conflict with this requirement. The PI must have a legal right to work for the proposing company in the United States, as evidenced by citizenship, permanent residency, or an appropriate visa. The PI does not need to be associated with an academic institution. There are no PI degree requirements (i.e., the PI is not required to hold a Ph.D. or any other degree). A PI must devote a minimum of three calendar months of effort per six months of performance to an NSF SBIR/STTR Fast-Track project.

Limit on Number of Proposals per Organization: 1

An organization must wait for a determination from NSF (e.g., award, decline, or returned without review) regarding a pending NSF SBIR/STTR Fast-Track pilot proposal before submitting a new Project Pitch in the next window. An organization that has submitted a traditional SBIR/STTR Project Pitch, received an invitation to submit a traditional SBIR/STTR Phase I proposal, or has a traditional SBIR/STTR Phase I proposal under review may not submit a Fast-Track Project Pitch until either the traditional SBIR/STTR Project Pitch has been declined (i.e., not invited) or the outcome of the invited traditional SBIR/STTR proposal submission has been made available to the organization. Proposals that have been Returned Without Review may be submitted using the same Project Pitch invitation (assuming that the proposal is received within 4 months of the original invitation).

Limit on Number of Proposals per PI or co-PI: 1

For NSF SBIR Fast-Track – 1 PI, co-PIs are not allowed. For NSF STTR Fast-Track - 1 PI and 1 Co-PI are required (the PI must be an employee of the proposing small business and the Co-PI must be part of the STTR partner research institution). An individual may be listed as the PI for only one proposal submitted at a time to the NSF SBIR/STTR programs (including traditional and Fast-Track). For NSF STTR Fast-Track proposals, a person may act as co-PI on an unlimited number of proposals.

Proposal Preparation and Submission Instructions

A. proposal preparation instructions.

• Letters of Intent: Not required
• Preliminary Proposal Submission: Not required

Full Proposal Preparation Instruction: This solicitation contains information that deviates from the standard NSF Proposal and Award Policies and Procedures Guide (PAPPG) proposal preparation guidelines. Please see the full text of this solicitation for further information.

B. Budgetary Information

Cost Sharing Requirements:

Inclusion of voluntary committed cost sharing is prohibited.

Indirect Cost (F&A) Limitations:

Not Applicable

Other Budgetary Limitations:

Other budgetary limitations apply. Please see the full text of this solicitation for further information.

C. Due Dates

Proposal review information criteria.

Merit Review Criteria:

National Science Board approved criteria. Additional merit review criteria apply. Please see the full text of this solicitation for further information.

Award Administration Information

Award Conditions:

Additional award conditions apply. Please see the full text of this solicitation for further information.

Reporting Requirements:

Standard NSF reporting requirements apply.

I. Introduction

The NSF SBIR/STTR Fast-Track pilot programs focus on stimulating technical innovation from diverse entrepreneurs and startups by translating new scientific and engineering discoveries emerging from the private sector, federal labs, and academia into products and services that can be scaled and commercialized into sustainable businesses with significant societal benefits. The NSF SBIR/STTR Fast-Track pilot programs support moving scientific excellence and technological innovation from the lab to the market. By funding startups and small businesses, NSF helps build a strong national economy and stimulates the creation of novel products, services, and solutions in private, public, or government sectors with potential for broad impact; strengthens the role of small business in meeting federal research and development needs; increases the commercial application of federally supported research results; and develops and increases the US workforce, especially by fostering and encouraging participation by socially and economically disadvantaged and women-owned small businesses.

While startups and small businesses face many challenges, the NSF SBIR/STTR Fast-Track pilot programs are intended to specifically focus on challenges associated with technological innovation; that is, on the creation of new products, services, and other scalable solutions based on fundamental science or engineering. A successful Fast-Track proposal must demonstrate how NSF funding will help the small business create a proof-of-concept or prototype by retiring technical risk.

NSF seeks unproven, leading-edge, technology innovations that demonstrate the following characteristics:

• The innovations are underpinned and enabled by a new scientific discovery or meaningful engineering innovation.
• The innovations still require intensive technical research and development to be fully embedded in a reliable product or service.
• The innovations have not yet been reduced to practice by anyone and it is not guaranteed, at present, that doing so is technically possible
• The innovations provide a strong competitive advantage that are not easily replicable by competitors (even technically proficient ones).
• Once reduced to practice, the innovations are expected to result in a product or service that would either be disruptive to existing markets or create new markets/new market segments.

The NSF SBIR/STTR Fast-Track pilot programs provide non-dilutive funding for the development of deep technologies, based on discoveries in fundamental science and engineering, that offer the potential for societal and economic impacts. The NSF SBIR/STTR Fast-Track pilot programs provide fixed amount cooperative agreements for the development of a broad range of technologies based on discoveries in science and engineering with potential for societal and economic impacts. Unlike fundamental or basic research activities that focus on scientific and engineering discovery itself, the NSF SBIR/STTR Fast-Track pilot programs support the creation of opportunities to move use-inspired and translational discoveries out of the lab and into the market or other use at scale, through startups and small businesses. The NSF SBIR/STTR pilot programs do not solicit specific technologies or procure goods and services from startups and small businesses. The funding provided is non-dilutive and NSF does not receive any stake or interest in the company or in the intellectual property resulting from the funded effort. NSF promotes inclusion by encouraging proposals from diverse populations and geographic locations.

II. Program Description

The aim of the NSF SBIR/STTR Fast-Track pilot programs is to enable eligible companies (see Section IV of this document) that have a complete R&D team (i.e., no “to-be-determined” team members) to submit a single proposal that, if awarded, can provide a faster pathway from Phase I to Phase II funding. A Fast-Track proposal will include a Phase I component and a Phase II component, each with a corresponding budget. Both Phase I and Phase II components of a Fast-Track proposal will be reviewed prior to the start of a Fast-Track project. On completion of the Phase I component, and contingent upon the results of a company’s Phase II transition review (see below for details), a Fast-Track awardee company will be able to transition directly to the Phase II component of the project. The primary benefits for Fast-Track awardee companies are (i) a pathway at the start of an awarded Fast-Track project to the full funding opportunities of the NSF SBIR/STTR Phase I and Phase II programs, and (ii) a faster transition from Phase I to Phase II than for traditional NSF SBIR/STTR Phase I awardees. Receipt of full funding under the Fast-Track programs is contingent upon the success of a company’s Phase II transition review.

The NSF SBIR/STTR Fast-Track pilot programs welcome proposals from almost all areas of technology. The program website presents a number of topic areas, but these are only meant to be suggestive of the types of topic areas that are anticipated. The programs are also open to proposals that focus on technical and market areas not explicitly noted in the aforementioned topics. Proposals that do not have an obvious fit in one of the specific topic areas can be submitted to “Other Topics”. NSF encourages eligible companies from all technology sectors and geographic areas to apply for funding. NSF does not test, verify, or otherwise use the technology developed under its SBIR/STTR Fast-Track awards.

The NSF SBIR/STTR Fast-Track pilot programs are expected to be highly competitive. Only a fraction of proposals submitted will be selected for an award. Thus, there may be many qualified businesses applying to the programs that do not receive funding.

NSF evaluates SBIR/STTR Fast-Track proposals under three distinct, but related merit review criteria: Intellectual Merit, Broader Impacts, and Commercialization Potential.

In addition to the standard NSF Merit Review Criteria (Section VI.A.), the following provides additional clarification of how Intellectual Merit and Broader Impact might be applied to startups and small businesses (Section II and IV.A.2).

The Intellectual Merit criterion encompasses the potential to advance knowledge and leverage fundamental science or engineering research techniques to overcome technical risk. This can be conveyed through the Research and Development (R&D ) of the project.

NSF SBIR/STTR Fast-Track proposals are evaluated via the concepts of Technical Risk and Technological Innovation. Technical Risk assumes that the possibility of technical failure exists for an envisioned product, service, or solution to be successfully developed. This risk is present even to those suitably skilled in the art of the component, subsystem, method, technique, tool, or algorithm in question. Technological Innovation indicates that the new product or service is differentiated from current products or services; that is, the new technology holds the potential to result in a product or service with a substantial and durable advantage over competing solutions on the market. It also generally provides a barrier to entry for competitors. This means that if the new product, service, or solution is successfully realized and brought to the market, it should be difficult for a well-qualified, competing firm to reverse-engineer or otherwise neutralize the competitive advantage generated by leveraging fundamental science or engineering research techniques.

The Broader Impacts criterion encompasses the potential for the company to drive a benefit to society in terms of addressing major societal challenges. Considering the products developed under these programs will have a broad societal reach, will be widely distributed, and will therefore have impacts that are far reaching with people and communities. It is important to ensure adequate assessment of potential benefits and unintended consequences of the proposed technology.

The NSF SBIR/STTR Fast-Track pilot programs support the vision of the NSF, which is a nation that leads the world in science and engineering research and innovation to the benefit of all, without barriers to participation. Proposers may also consider the Broader Impacts Review Criterion at 42 U.S.C. §1862p-14 as related to the potential for broadest societal impact.

An additional, solicitation-specific merit review criteria focused on Commercialization Potential is also required. The Commercialization Potential of the proposed product or service is the potential for the resulting technology to disrupt the targeted market segment by way of a strong and durable value proposition for the customers or users.

• The proposed product or service addresses an unmet, important, and scalable need for the target customer base.
• The proposed small business is structured and staffed to focus on aggressive commercialization of the product/service.
• The proposed small business can provide evidence of good product-market fit (as validated by direct and significant interaction with customers and related stakeholders).

More details and information regarding the NSF SBIR/STTR merit review criteria can be found in Section VI.A of this solicitation and the NSF SBIR/STTR website .

The review of an NSF SBIR/STTR Fast-Track proposal includes both the Phase I and Phase II components of the proposal. A team submitting an NSF SBIR/STTR Fast-Track proposal must have NSF-funded research lineage (see Section IV); customer discovery training in order to develop an understanding of the target market, product-market fit and initial target customers; and a complete team (no “to-be-determined” members).

The Phase I and Phase II components of an NSF SBIR/STTR Fast-Track proposal will be reviewed and evaluated separately. For cases in which reviewers and the cognizant Program Officer deem that the Phase I component is meritorious, but the Phase II component is not, the Program Officer may consider recommending the Fast-Track proposal for a traditional NSF SBIR/STTR Phase I award. The company would subsequently be eligible to apply for NSF SBIR/STTR Phase II funding via the traditional process (i.e., not via the Fast-Track process).

An NSF SBIR/STTR Fast-Track proposal must include specific, quantifiable performance targets for the Phase I component of the project. These Phase I targets may be renegotiated with the cognizant Program Officer during post-review diligence, so that at the start of the Fast-Track project there will be agreed performance targets in place for the Phase I component.

Phase II Transition Review : The Phase II transition review will consist of two stage gates:

Stage Gate 1: Progress Evaluation.

Approximately three (3) months prior to the end of the Phase I component, the NSF SBIR/STTR Fast-Track recipient will be required to participate in a reverse site visit during which they will present to NSF the results of the Phase I project to date. Detailed guidance regarding the reverse site visit will be provided to the recipient four to six weeks prior to the reverse site visit. NSF will evaluate progress made by the Fast-Track recipient company during the Phase I component, taking into account a number of factors including, but not limited to:

• Phase I performance compared with the agreed performance targets;
• commercial progress and commercial traction during Phase I;
• team suitability for Phase II; and
• additional resources – including company personnel, advisors, and funding that are accessible to the company for technical, regulatory, or commercial activities associated with the Phase II component.

Based on the results of NSF’s SBIR/STTR Fast-Track Stage Gate 1 review, if NSF determines, based on this progress evaluation, that a Fast-Track award recipient should have the opportunity to transition to the Phase II component, the company will advance to Stage Gate 2.

Alternatively, NSF may decide that an NSF SBIR/STTR Fast-Track award will not transition to the Phase II component. In such cases, the Fast-Track project will be limited to Phase I funding, and the award will conclude at the end of the Phase I component. NSF will communicate its decision and rationale back to the Fast-Track awardee. The company will not be eligible to apply for regular SBIR/STTR Phase II funding based on the Fast-Track award. NOTE: NSF’s decision not to provide SBIR/STTR Phase II funding following Stage Gate 1 is not subject to reconsideration or termination review as defined in Chapter XII.A.4 of the PAPPG.

Stage Gate 2: CAP Review

NSF SBIR/STTR Fast-Track award recipients who progress beyond the Stage Gate 1 will be required to prepare and submit administrative and supporting financial documentation for review by the NSF Cost Analysis and Pre-Award (CAP) Branch. See https://www.nsf.gov/bfa/dias/caar/sbirrev.jsp for detailed requirements. CAP reviews are conducted to evaluate a prospective recipient's ability to manage a federal award effectively and efficiently, as well as to establish the reasonableness of the dollar amount for the Phase II component of the award. Based on the results of the Stage Gate 2 review, NSF may decide that a Fast-Track award will not receive additional Phase II funding, and the award will conclude at the end of the Phase I component. NSF will communicate its decision and rationale back to the Fast-Track recipient. The company will not be allowed to apply for regular SBIR/STTR Phase II funding based on the Fast-Track award. NOTE: NSF’s decision not to provide SBIR/STTR Phase II funding following Stage Gate 2 is not subject to reconsideration or termination review as defined in Chapter XII.A.4 of the PAPPG.

Companies who pass both Stage Gates 1 and 2 will receive a funding increment for the Phase II component of the award, and they will be eligible to apply for the same Phase II supplemental funding opportunities as are available to a traditional NSF SBIR/STTR Phase II awardee.

III. Award Information

Anticipated Type of Award: Fixed Amount Cooperative Agreement Estimated Number of Awards: 36

Anticipated Funding Amount: $56,000,000

IV. Eligibility Information

Additional Eligibility Info:

Required Project Pitch Invitation: Potential proposers must receive an invitation to submit a full NSF SBIR/STTR Fast-Track pilot proposal. Please see Project Pitch website for details. STTR Research Institution.  The  SBIR/STTR Policy Directive  requires that STTR proposals include an eligible research institution as a subawardee on the project budget. The STTR partner research institution is typically either a not-for-profit institution focused on scientific or educational goals (such as a college or university), or a Federally Funded Research and Development Center (FFRDC). For an NSF STTR Fast-Track proposal, a minimum of 40% of the research, as measured by the budget, must be performed by the small business concern, and a minimum of 30% must be performed by a single partner research institution, with the balance permitted to be allocated to either of these, or to other subawards or consultants. Partnering. Proposing companies are encouraged to collaborate with experienced researchers at available facilities such as colleges, universities, national laboratories, and from other research sites. Funding for such collaborations may include research subawards or consulting agreements. The employment of faculty and students by the small business is allowed, however: For an NSF SBIR Fast-Track proposal , a minimum of two-thirds of the research, as measured by the budget, must be performed by the small business during the Phase I component of the project, and a minimum of one-half of the research, as measured by the budget, must be performed by the small business during the Phase II component of the project. The balance of the budget may be outsourced to subawards or consultants or a combination thereof. The proportion requirements cited above must be met in both the Phase I and Phase II budgets independently. For an NSF STTR Fast-Track proposal , the SBIR/STTR Policy Directive requires proposals to include an eligible research institution as a subawardee on the project budget. The institution is typically either a not-for-profit institution focused on scientific or educational goals (such as a college or university), or a Federally funded research and development center (FFRDC). A minimum of 40% of the research, as measured by the budget, must be performed by the small business. A minimum of 30% must be performed by a single partner research institution. The balance (remaining 30%) may be allocated to the small business, partner research institution, or to other subawards or consultants. The percentage requirements cited above must be met in both the Phase I and Phase II budgets independently. For Both SBIR and STTR Fast-Track proposals, proposals should NOT be marked as a "Collaborative Proposal from Multiple Organizations" during submission. Companies are allowed to switch between SBIR and STTR, and vice versa, as they transition from Phase I to Phase II. Government-Wide Required Benchmarks (applies to previous SBIR/STTR recipients only): Phase I to Phase II Transition Rate Benchmark. For Phase I proposers that have received more than 20 Phase I SBIR/STTR awards from any federal agency over the past five fiscal years, the minimum Phase I to Phase II Transition Rate over that period is 25%. Small businesses that fail to meet this transition requirement will be notified by the Small Business Administration and will not be eligible to submit a Phase I proposal for one (1) year. Commercialization Benchmark (applies to previous SBIR/STTR recipients only). The commercialization benchmark required by the SBIR/STTR Reauthorization Act of 2011 only applies to proposers that have received more than 15 Phase II Federal SBIR/STTR awards over the past 10 fiscal years, excluding the last two years. These companies must have achieved the minimum required commercialization activity to be eligible to submit a Phase I proposal, as determined by the information entered in the company registry, see Completing the Company Registry Commercialization Report: Instructions and Definitions . For more information, see Performance Benchmark Requirements .

V. Proposal Preparation And Submission Instructions

Full Proposal Instructions : Proposals submitted in response to this program solicitation should be prepared and submitted in accordance with the guidelines specified in the NSF Proposal & Award Policies & Procedures Guide (PAPPG). The complete text of the PAPPG is available electronically on the NSF website at: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg . Paper copies of the PAPPG may be obtained from the NSF Publications Clearinghouse, telephone (703) 292-8134 or by e-mail from [email protected] .

See PAPPG Chapter II.D.2 for guidance on the required sections of a full research proposal submitted to NSF. Please note that the proposal preparation instructions provided in this program solicitation may deviate from the PAPPG instructions.

This solicitation contains MANY instructions that deviate from the standard NSF PAPPG proposal preparation instructions. This solicitation contains the information needed to prepare and submit a proposal and refers to specific sections of the PAPPG ONLY when necessary (and noted throughout the solicitation). In the event of conflict between the instructions in this solicitation and the PAPPG, use this solicitation's instructions as a guide.

The following project activities are not responsive to the solicitation:

• Evolutionary development or incremental modification of established products or proven concepts;
• Straightforward engineering or test and optimization efforts that are not hypothesis driven;
• Evaluation or testing of existing products;
• Basic scientific research or research not connected to any specific market opportunity or potential new product;
• Business development, market research, and sales and marketing;
• Clinical trials;
• Research or commercialization pathways involving chemical components, natural or synthetic variations thereof, or other derivatives related to Schedule I controlled substances; or
• Non-profit business concerns.

Non-responsive proposals may be returned without review.

An NSF SBIR/STTR Fast-Track pilot proposal that is Returned Without Review as being not responsive to the solicitation may be significantly revised and submitted for the next deadline if the proposal is still within the timeframe for eligible submission.

Required Project Pitch submission: To submit a full NSF SBIR/STTR Fast-Track proposal, potential proposers must first submit a Project Pitch and receive an invitation. The Project Pitch gives NSF the ability to review for appropriateness to the NSF STTR/STTR Fast-Track programs prior to the full proposal submission process, ensuring that proposers do not expend time or resources preparing full proposals that are not aligned with the program requirements. To start this process, proposers must first create a log-in and submit a Project Pitch via the NSF SBIR/STTR Fast-Track Project Pitch online form . NSF SBIR/STTR program staff will use the Project Pitch to determine whether the proposed project is a good fit for the program objectives.

• All NSF SBIR/STTR Fast-Track Project Pitches MUST be submitted to “Fast-Track” using the drop-down on the site and MUST nominate the most appropriate technical topic area from the list,
• Proposers may submit a Project Pitch at any time, regardless of the NSF SBIR/STTR Fast-Track pilot solicitation window.
• Proposers must include their prior NSF award number (NSF lineage) in the Project Pitch.
• When submitting an SBIR/STTR Fast-Track proposal in Research.gov, you must enter your invited SBIR/STTR Fast-Track Project Pitch Number in the SBIR/STTR Fast-Track Questionnaire . The Phase I award number must be validated before you can continue with the proposal preparation.

REQUIRED REGISTRATIONS: Small businesses applying for NSF SBIR/STTR Fast-Track funding must be registered in the following systems in order to submit a proposal to NSF. The registrations below can take several weeks or even months to process, so please start early.

You must register your company name, physical address, and all other identifying information identically in each of these systems. We recommend that you register your small business in the following order:

• NSF will validate that each proposer’s UEI and SAM registration are valid and active prior to allowing submission of a proposal to NSF. If a registration is not active, an organization will not be able to submit a proposal. Additionally, if the SAM registration is not renewed annually and is not valid, NSF will block any award approval actions.
• Any subawardees or subcontractors are also required to obtain a UEI and register in Research.gov. Entities can obtain a SAM UEI without full SAM registration. If you have a subrecipient that is not fully registered in SAM, but has been assigned a UEI number, please call the IT Help desk for further assistance.
• Small Business Administration (SBA) Company Registration . A Small Business Concern Identification number (SBC ID) is required prior to submission of the proposal. SBA maintains and manages the Company Registry for SBIR/STTR proposers in order to track ownership and affiliation requirements. All SBCs must report ownership information prior to each SBIR/STTR proposal submission and update the SBC if any information changes prior to award. This registration process is free.
• Research.gov. Research.gov is NSF’s online grant management system – how you submit your proposal. For more information, consult the "About Account Management" page. This registration process is free.

Beware of scammers charging fees for SAM and/or SBA registrations.

B. Tips on the Proposal Preparation and Submission

It is suggested that you create a single PDF document for each section of the proposal, aggregate those PDF documents into a single file joining the various sections, then upload this single PDF to Research.gov. This will avoid issues resulting from Research.gov conversion to PDF formats.

Submit a complete proposal:

• Cover Sheet
• SBIR (or STTR) Fast-Track Questionnaire
• SBIR (or STTR) Fast-Track Certification Questions
• Project Summary
• Project Description
• References Cited
• Budget(s) (and Subaward Budget(s), if needed)
• Budget Justification(s) (and Subaward Budget Justification(s), if needed)
• Facilities, Equipment and Other Resources
• Biographical Sketch
• Current and Pending (Other) Support
• Collaborations and Other Affiliations (Single Copy Document)
• Synergistic Activities
• Data Management and Sharing Plan
• Mentoring Plan (Conditionally required)
• Project Schedule
• Letter(s) of Support (Required)
• IP (Intellectual Property) Rights Agreement (Required for STTR proposals and strongly recommended for SBIR proposals when there is a subaward to another institution)
• Other Personnel Biographical Information
• Other Supplementary Documents
• List of Suggested Reviewers (Single Copy Document)
• List of Reviewers Not to Include (Single Copy Document)
• Deviation Authorization (Single Copy Document)
• Additional Single Copy Documents

DO NOT upload information beyond what is specifically required and permitted into the proposal (e.g., do not include marketing materials, research results, academic papers, patent applications, etc.).

DO NOT include samples, videotapes, slides, appendices, or other ancillary items within a proposal submission. Websites containing demonstrations and Uniform Resource Locators (URLs) (if applicable) must be cited in the References Cited section. Note: reviewers are not required to access any information outside the proposal document. Please refer to the NSF PAPPG (Chapter II.C) for more details on accepted proposal fonts and format.

C. Detailed Instructions on Proposal Preparation

Full Proposal Set-up: In Research.gov , complete the following steps:

• Select "Prepare & Submit Proposals,” “Letters of Intent and Proposals”
• Select “Prepare New” and from the pull down “Full Proposal.”
• Funding Opportunity. Either filter by “SBIR” or “STTR” or “Fast-Track”, and select radio button for the NSF SBIR/STTR Fast-Track Pilot Programs.
• Where to Apply. Select program: SBIR Fast-Track or STTR Fast-Track.
• Proposal Type: Select SBIR or STTR.
• Proposal Details: Answer questions:
• Is your organization a sole proprietorship? Yes or No
• Enter Proposal Title, then click on Prepare Proposal
• You will now be on a new proposal page – Select Due Date (upper right corner)

Cover Sheet. The Cover Sheet requests general information about the proposal and proposing organization.

Other Federal Agencies (if applicable). If this proposal is being submitted to other Federal agencies, state or local governments, or non-governmental entities, enter a reasonable abbreviation, up to 10 characters, for each agency or entity. Only the first 5 agencies you enter will appear on the PDF version of the proposal, but all should be entered below. IT IS ILLEGAL TO ACCEPT DUPLICATE FUNDING FOR THE SAME WORK. IF A PROPOSER FAILS TO DISCLOSE EQUIVALENT OR OVERLAPPING PROPOSALS, THE PROPOSER COULD BE LIABLE FOR ADMINISTRATIVE, CIVIL, AND/OR CRIMINAL SANCTIONS.

Human Subjects (if applicable). According to 45 CFR 46 , a human subject is "a living individual about whom an investigator (whether professional or student) conducting research:

• Obtains information or biospecimens through intervention or interaction with the individual, and uses, studies, or analyzes the information or biospecimens; or
• Obtains, uses, studies, analyzes, or generates identifiable private information or identifiable biospecimens.”

NIH provides a Decision Tool to assist investigators in determining whether their project involves non-exempt human subjects research, meetings the criteria for exempt human subjects research, or does not involve human subjects research.

Projects involving research with human subjects must ensure that subjects are protected from research risks in conformance with the relevant Federal policy known as the Common Rule ( Federal Policy for the Protection of Human Subjects, 45 CFR 690 ). All projects involving human subjects must either (1) have approval from an Institutional Review Board (IRB) before issuance of an NSF award; or (2) must obtain a statement from the IRB indicating research exemption from IRB review; or 3) must obtain a just in time IRB designation and documentation. This documentation needs to be completed during due diligence discussions, in accordance with the applicable subsection, as established in section 101(b) of the Common Rule. If certification of exemption is provided after submission of the proposal and before the award is issued, the exemption number corresponding to one or more of the exemption categories also must be included in the documentation provided to NSF. The small business has three basic options with regard to human subjects review:

• Establish your own IRB (see Office for Human Research Protections (OHRP) at the Department of Health and Human Services (HHS): https://www.hhs.gov/ohrp/irbs-and-assurances.html#registernew .
• Use the review board of a (usually local) university or research institution, either via consultants to the project, a project subaward, or directly through its own contacts;
• Use a commercial provider.

For projects lacking definite plans for the use of human subjects, their data, or their specimens, pursuant to 45 CFR § 690.118 , NSF can accept a determination notice that establishes a limited time period under which the PI may conduct preliminary or conceptual work that does not involve human subjects. See more information and instructions regarding this documentation in the PAPPG.

Live Vertebrate Animals (if applicable). Any project proposing use of vertebrate animals for research or education shall comply with the Animal Welfare Act ( 7 USC 2131, et seq. ) and the regulations promulgated thereunder by the Secretary of Agriculture ( 9 CFR 1 .1 -4.11 ) pertaining to the humane care, handling, and treatment of vertebrate animals held or used for research, teaching or other activities supported by Federal awards.

In accordance with these requirements, proposed projects involving use of any vertebrate animal for research or education must be approved by the submitting organization's Institutional Animal Care and Use Committee (IACUC) before an award can be made. For this approval to be accepted by NSF, the organization must have a current Public Health Service (PHS) Approved Assurance. See also PAPPG for additional information on the administration of awards that utilize vertebrate animals. This documentation must be completed before issuance of an NSF award.

SBIR (or STTR) Fast-Track Questionnaire. The SBIR/STTR Fast-Track Questionnaire MUST be filled in completely including Topic and Subtopic, Project Pitch Number, Authorized Company Officer Information, Proposing Small Business Information, SBIR/STTR Award History, Affiliated Companies, and Other Information (including NSF Funding Lineage).

Other Information.

Proprietary Information. To the extent permitted by law, the Government will not release properly identified and marked technical and commercially sensitive data.

If the proposal does not contain proprietary information, uncheck the box in the Phase I Questionnaire.

If the proposal does contain proprietary information identify the proprietary technical data by clearly marking the information and also providing a legend. NSF SBIR/STTR data, including proposals, are protected from disclosure by the participating agencies for not less than 20 years from the delivery of the last report or proposal associated with the given project. Typically, proprietary information is identified in the text either with an asterisk at the beginning and end of the proprietary paragraph, underlining the proprietary sections, or choosing a different font type. An entire proposal should not be marked proprietary.

For Statistical Purposes. Please check all of the appropriate boxes and fill in award numbers as needed.

SBIR (or STTR) Fast-Track Certification Questions. The Fast-Track Certification Questions MUST be filled in completely.

Project Summary . One (1) page MAXIMUM]. The Project Summary should be written in the third person, informative to other persons working in the same or related fields, and insofar as possible, understandable to a scientifically or technically literate lay reader. It should not be an abstract of the proposal. Do not include proprietary information in the summary.

The Project Summary is completed in Research.gov by entering information into the three text boxes in the Project Summary. To be valid, a heading must be on its own line with no other text on that line.

• Overview: Describe the potential outcome(s) of the proposed activity in terms of a product, process, or service. Provide a list of key words or phrases that identify the areas of technical expertise to be invoked in reviewing the proposal and the areas of application that are the initial target of the technology. Provide the subtopic name.
• Intellectual Merit: This section MUST begin with “This Small Business Innovation Research (or Small Business Technology Transfer) Fast-Track project...” Address the intellectual merits of the proposed activity. Briefly describe the technical hurdle(s) that will be addressed by the proposed R&D (which should be crucial to successful commercialization of the innovation), the goals of the proposed R&D, and a high-level summary of the plan to reach those goals.
• Broader Impacts and Commercial Potential: Discuss the expected outcomes in terms of how the proposed project will bring the innovation closer to commercialization under a sustainable business model. In this box, also describe the potential commercial and market impacts that such a commercialization effort would have, if successful. Also discuss potential broader societal and economic impacts of the innovation (e.g., educational, environmental, scientific, societal, or other impacts on the nation and the world).

Project Description. Ten (10) pages MINIMUM and fifteen (15) pages MAXIMUM). The project description is the core of the proposal document, where the PI convinces the expert reviewers/panelists and NSF SBIR/STTR Fast-Track Program Officer that their proposed R&D project meets NSF’s criteria for Intellectual Merit, Broader Impacts, and Commercialization Potential. Note: The incorporation of URLs or websites within the Project Description is not acceptable and the proposal may not be accepted or will be Returned without Review.

The Project Description for a Fast-Track proposal is divided into the following sections:

• Succinctly describe the proposed technical innovation, highlighting those aspects that are innovative and transformative relative to the current state of the art. Describe the innovation in sufficient technical detail for a knowledgeable reviewer to understand why it is innovative and how it can provide benefits in the target applications.
• Describe the primary technical risks associated with developing the proposed innovation and the key technical objectives to be accomplished during Phase I, clearly explaining why these technical objectives are commercially relevant.
• Provide an R&D plan to achieve the key Phase I technical objectives, along with a corresponding timeline. The R&D plan must leverage fundamental science or engineering research and techniques. Associated with this R&D plan, provide a set of clear, quantitative Phase I technical performance targets. Note that these performance targets, if met, must be sufficient to establish or strongly suggest technical viability of the proposed technology, although it is recognized that substantial further development work will likely be needed to generate a commercial product or service.
• If the project involves subawards, explain why the subawardee(s) is(are) appropriate partners and describe the intended outcomes of the subawards.
• Describe the technical performance metrics that you will need to achieve in order to develop (i) a minimum viable product or service, and (ii) a first-generation commercial-grade product or service. Describe the intended technical outcomes of the Phase II component of the project in terms of these two stages of development, clearly explaining how far you plan to progress towards a commercial solution during Phase II.
• Describe the major non-commercial hurdles that will need to be overcome to achieve the above Phase II technical outcomes.
• Provide a detailed R&D plan to transition the Phase I results into the intended Phase II technical outcomes described above, along with a corresponding timeline.
• Clearly describe any security and privacy practices or standards, regulatory requirements, or other industry standards or practices that the proposed technology will need to comply with in order to be widely adopted and explain how you will ensure that the technology is compliant.
• Discuss manufacturing/production, deployment/distribution, and technical scalability of the proposed solution.
• If the project involves subawards, explain why the subawardee(s) is(are) appropriate partners and describe the intended outcomes of the or subawards.
• Explain the motivation for the company in proposing this project.
• Provide a concise description of the relevant qualifications, experience, and expertise of the company founders and the Senior/Key Personnel on the proposed project.
• Describe your vision for the company and the company's expected impact over the next five years.
• Describe any existing company operations and explain how the proposed effort would fit into these activities.
• Provide the revenue and funding history of the company. Include and explicitly indicate any government funding (federal, state, or local) and private investment.
• Describe the expertise and contributions to the project of any consultants that you proposed to engage during the project.
• Describe how you expect to expand the team going into Phase II and present a rationale for the team changes relative to Phase I. In your response include a discussion of Phase II team members who will not be supported by NSF funds.
• Describe the target market (including the size and geography of the target market) and initial target customer(s), with examples where possible.
• Describe results of ongoing customer discovery activities to date. Provide supporting data if possible.
• Clearly describe the proposed product or service, and how it will be delivered to the target customers.
• Clearly describe the value proposition.
• Describe the proposed commercialization and monetization models. Provide a pricing model with supporting evidence.
• Discuss commercial scalability of the proposed solution.
• Describe the competition and explain how your company will build a sustainable competitive advantage.
• Describe the company’s intellectual property strategy and provide a current status.
• Present a financing plan to bring the company to profitability and explain how you will enact this plan.
• Provide a 5-year pro-forma, with underlying assumptions and supporting evidence for the assumptions. Be sure to include a detailed breakdown of expected revenues, cost of goods sold, and company expenses.
• Describe how the proposed product or service offers the potential for broader societal impacts as well as economic benefit (through commercialization under a sustainable business model). Examples of such outcomes may include (but are not limited to) those found in the American Innovation and Competitiveness Act ( P.L. 114-329, Section 102 ) Broader Impacts Review Criterion.
• The NSF SBIR/STTR Fast-Track pilot programs fund the development of new, high-risk technology innovations intended to generate positive societal outcomes. Discuss the envisioned broader impacts and the specific implementation plan, including: the relevant metrics and measurement plan; potential partners; potential risks and associated mitigation strategies; and additional anticipated needs for resources and the plan to secure them.

References Cited. Provide a comprehensive listing of relevant references, including websites or relevant URLs, patent numbers, and other relevant intellectual property citations. If there are no references, include a statement to that effect.

Budget(s) and Budget Justification(s) . Proposers are required to submit budgets with their proposals, including specific dollar amounts by budget category. Proposers must submit a written justification explaining these amounts in detail. NSF SBIR/STTR Fast-Track Program Officers review these proposed budgets and rely on them in determining the final amount awarded for a given SBIR/STTR Fast-Track project. Enter budget figures for each project year into Research.gov. The system will automatically generate a cumulative budget for the entire project.

Detailed documentation of all budget line items is required and MUST be documented in detail on the Budget Justification. The budget should reflect the needs of the proposed R&D project. The maximum total budget shall not exceed $1,555,000: $400,000 for the Phase I component and $1,155,000 for the Phase II component.

IMPORTANT: The budget and budget justification for the Phase I component of the proposed SBIR/STTR Fast-Track project must be uploaded to the year 1 budget in Research.gov, while the Phase II component of the proposed Fast-Track project must be uploaded to the year 2 and year 3 budget in Research.gov.

The Budget Justification must be uploaded to the Research.gov Budget as a single PDF with two distinct sections – one section for the Phase I component of the Fast-Track project budget and one for the Phase II component. For each component, provide details for each non-zero line item of the budget, including a description and cost estimates. Identify each line item by its letter (e.g., A. Senior/Key Personnel). There is a five-page limit for the Budget Justification . Each Subaward Budget Justification, where required, also has a five-page limit. Additional information to help prepare your proposal budget is available here . The Budget Justification must also clearly state the expected duration of the corresponding Phase I or Phase II project component.

You can add Subaward Organization(s) to your proposal (required for STTR submissions and allowed for SBIR submissions), and make changes to personnel information by navigating to the Budget “Manage Personnel and Subaward Organizations” tab.

All activities on an NSF SBIR/STTR Fast-Track pilot project, including services that are provided by consultants, must be carried out in the United States ("United States" means the 50 states, the territories and possessions of the U.S. Federal Government, the Commonwealth of Puerto Rico, the District of Columbia, the Republic of the Marshall Islands, the Federated States of Micronesia, and the Republic of Palau).Based on a rare and unique circumstance, agencies may approve a particular portion of the R/R&D work to be performed or obtained in a country outside of the United States, for example, if a supply or material or other item or project requirement is not available in the United States. The Funding Agreement officer must approve each such specific condition in writing.

Guidelines for the budget and budget justification follows.

Line A – Senior/Key Personnel. List the PI, co-PI (if STTR), and Senior/Key Personnel by name, their time commitments (in calendar months), and the dollar amount requested. Only salaries and wages for employees of the proposing organization should be included on Line A. Research effort is to be estimated in “Months” (1 Month = 173 hours). Months do not include paid time off and represents actual effort that will be dedicated to the project. The PI must be budgeted for a minimum of three calendar months of effort per six months of performance to the proposed NSF SBIR/STTR Fast-Track project.

In the Budget Justification provide the name; title; a brief description of responsibilities for the PI, co-PI (if STTR), and each of the Senior/Key Personnel as well as the annual, monthly, or hourly salary rate; time commitment; and a calculation of the total requested salary.

You can add additional senior/key personnel to your proposal (e.g., for STTR submissions), and make changes to personnel information by navigating to the Budget “Manage Personnel and Subaward Organizations” tab.

The best source for determining an appropriate salary request is the Bureau of Labor Statistics (BLS). In the Budget Justification provide the title; annual, monthly, or hourly salary rate; time commitment; a calculation of the total requested salary; and a description of responsibilities for the PI, co-PI (if STTR), and each of the Senior/Key Personnel.

You can add additional senior/key personnel to your proposal (e.g., for STTR submissions), and make changes to personnel information by navigating to the "Manage Personnel and Subaward Organizations" page.

Line B - Other Personnel. List the number of people, months, and funding for additional personnel: Other Professionals (Technicians, Programmers, etc.), Administrative/Clerical, and/or Other. These personnel must be employed at the proposing company. The budget justification should state individual employee names and titles (to the extent known), expected role in the project, effort in months and annual salary for each person.

Postdoctoral scholars and students (undergraduate and graduate) are generally listed on a subaward budget to a research institution. If they are employees of the company, they may be listed in Line A. Senior/Key Personnel (Line A), or Line B. Other Professionals or Other, as appropriate.

Line C - Fringe Benefits. It is recommended that proposers allot funds for fringe benefits here ONLY if the proposer's usual (established) accounting practices provide that fringe benefits be treated as direct costs. If Fringe Benefits are included on Line C, describe what is included in fringe benefits and the calculations that were used to arrive at the amount requested.

Otherwise, fringe benefits should be included in Line I. Indirect Costs.

Line D - Equipment. Equipment is defined as non-expendable, tangible personal property, having a useful life of more than one year and an acquisition cost of $5,000 or more per unit. However, organizations may elect to establish their capitalization threshold as less than $5,000. Equipment should be budgeted consistently with the proposing organization's capitalization policy. Requests should not be made for general purpose or routine equipment that a business conducting research in the field should be expected to have available. The budget justification must explain the need for any equipment and include the item identification/description, vendor identification, quantity, price, and extended amount. The budget justification should also include, as a separate document if needed, pricing documentation (e.g., quotes, invoices, links to online price lists, past purchase orders, etc.) for each budgeted piece of equipment.

Note that the purchase of Equipment may NOT be included in the budget of the Phase I component of a Fast-Track proposal (Year 1), but MAY be included in the budget of the Phase II component of a Fast-Track proposal (Years 2 and 3).

Line E - Travel. NSF requires that the PI budget travel (for the first year of the project only) to attend the NSF SBIR/STTR Awardee Workshop. A good estimate for the Awardee Workshop is $2,000 per person and is limited to $4,000 per year. Other than the Awardee Workshop and funds for Technical And Business Assistance (TABA, see below), all budgeted travel must be directly related to the execution of the research effort. Only domestic travel will be considered.

The Budget Justification must include the purpose for domestic travel and, for each budgeted trip: the destination, purpose of travel, number of days, and the estimated costs for airfare, cab fare, car rental, per diem rates, hotel, and other incidentals. No supporting detail is required for attendance at the Awardee Workshop at $2,000 (or less) per person. If the workshop is organized as virtual only, proposers can (if awarded) reallocate these funds towards other project activities, pending the approval of the cognizant SBIR/STTR Program Officer.

Travel for purposes other than the project R&D effort (e.g., marketing, customer engagements) is not permitted in the NSF SBIR/STTR Fast-Track budget.

Foreign travel expenses are NOT permitted.

Line F - Participant Support Costs. Participant support costs are NOT permitted on an NSF SBIR/STTR Fast-Track budget.

Line G. Other Direct Costs.

Materials and Supplies. Materials and supplies are defined as tangible personal property, other than equipment, costing less than $5,000, or other lower threshold consistent with the policy established by the proposing organization. The Budget Justification should indicate the specifics of the materials and supplies required, including an itemized listing with item/description, vendor, unit cost, quantity, price, and extended amount. Items with a total cost exceeding $5,000 may require pricing documentation (e.g., quote, link to online price list, prior purchase order or invoice) after the proposal is reviewed, as part of the NSF SBIR/STTR Fast-Track Program Officer's due diligence efforts. Please see Section VI. for details.

Publication Costs/Documentation/Distrib. Publication, documentation and distribution costs are not allowed.

Consultant Services. Consultant services include specialized work that will be performed by professionals that are not employees of the proposing small business. All consultant activities must be carried out in the United States (see above).

No person who is an equity holder, employee, or officer of the proposing small business may be paid as a consultant unless an exception is recommended by the cognizant SBIR/STTR Fast-Track Program Officer and approved by the Division Director of Translational Impacts (TI).

The proposal must include a signed agreement ( Letter of Commitment ) from each consultant confirming the services to be provided (role in the project), primary organizational affiliation, number of days committed to the research effort, availability to provide services, and consulting daily rate. The agreement must clearly state the number of days on the project, the consulting daily rate (8 hours/day) and the total dollar amount of the consulting agreement. Include a copy of the signed Letter of Commitment in the "Other Supplementary Documents" section. Multiple letters should be combined as a single PDF before uploading.

The consulting daily rate represents the total labor compensation for an 8-hour period and may not exceed $1,000 per day. Any miscellaneous costs, such as supplies, that are not included as part of the daily rate must be identified and justified. Consultant travel should be shown under the domestic travel category, Line E, but counts as an outsourcing expense for the purpose of determining whether the small business concern meets the minimum level of effort for an NSF SBIR/STTR proposal. Any information above and beyond the above will be considered not responsive and may be removed from your proposal .

Biographical sketches for each consultant may be requested by the cognizant NSF SBIR/STTR Fast-Track Program Officer after the proposal is reviewed, as part of their due diligence efforts. Please see Section VI. for details.

Computer Services . This line can include funds for fee-for-service computing activities or resources (such as supercomputer time, cloud services, etc.). Any extended line item should be accompanied by pricing documentation (e.g., quote, link to online price list, prior purchase order, or invoice) in the budget justification. Requested services with a total cost exceeding $5,000 may require pricing documentation (e.g., quote, link to online price list, prior purchase order or invoice) after the proposal is reviewed, as part of the NSF SBIR/STTR Fast-Track Program Officer's due diligence efforts.

Subaward(s). Subawards may be utilized when a significant portion of the work is performed by another organization and when the work to be done is not widely commercially available. Work performed by a university or research laboratory is one example of a common subaward.

Subawards require a separate subaward budget and subaward budget justification, in the same format as the main budget. To enter a subaward budget in Research.gov, go to the Budget module tab and add Subaward Organization(s) by opening the “Manage Personnel and Subaward Organizations” tab. Each subawardee will have its own budget pages for each year of the project.

A subawardee research institution partner is mandatory for STTR Fast-Track proposals. Explicitly list who the research partner will be and provide a brief description of the work they will perform. A minimum of 40% of the research, as measured by the budget, must be performed by the small business concern and a minimum of 30% of the research, as measured by the budget, must be performed by a single subawardee research institution, with the balance permitted to be allocated to either of these, or to other subawards or consultants. Subawardees are not permitted to request profit (Line K) as part of their budgets.

The proposing organization's budget justification must discuss the tasks to be performed and how these are related to the overall project. Also discuss any organizational relationships (e.g., common ownership or related parties) between the proposing organization and the subawardee, and the type of subaward contemplated (e.g., fixed price or cost reimbursement).

Subawardees (the institution, not the individual PI or researcher) should also provide a Letter of Commitment that confirms the role of each subaward organization in the project and explicitly states the subaward amount(s). Provide this letter(s) as part of the Other Supplementary Documents.

For NSF SBIR Fast-Track proposals, subaward funds do not count as funds spent by the small business, and the total amount requested for subawards (when added to consultant funds and any other subawards) cannot exceed 1/3 of the total Phase I budget component and cannot exceed 1/2 of the total Phase II budget component.

No significant part of the research or substantive effort under an NSF award may be contracted or otherwise transferred to another organization without prior NSF authorization. The intent to enter into such arrangements should be disclosed in the proposal.

No person who is an equity holder, employee, or officer of the proposing small business may be paid under a subaward unless an exception is recommended by the NSF SBIR/STTR Program Director and approved by the TI Division Director.

Any subrecipients named in the proposal are also required to obtain a SAM UEI and register in Research.gov . Subrecipients named in the proposal, however, do not need to be registered in SAM. Entities can obtain a SAM UEI without full SAM registration. If you have a subrecipient that is not fully registered in SAM, but has been assigned a UEI number, please call the IT Help desk for further assistance.

It is the responsibility of the proposing organization to confirm that submitted subaward budgets have been approved by an Authorized Organizational Representative at the subawardee organization.

An IP (Intellectual Property) Rights Agreement is required for STTR proposals and strongly recommended for SBIR proposals when there is a subaward to another institution. A fully signed agreement is not required for STTR proposals at the initial proposal submission but will be required before a recommendation for an award can be made. Provide this Agreement, as a PDF, as part of the Optional Documents.

Other . This line includes the purchase of routine analytical or other services, or fabricated components from commercial sources. The budget justification must explain the need for the services, provide a description of the services, and give a detailed cost itemization. Any single "other" item with a total cost of $5,000 must be further itemized into smaller costs or supported by pricing documentation (e.g., quote, link to online pricing list, past purchase order) in the budget justification. This detail will be requested as part of the NSF SBIR/STTR Fast-Track Program Officer's due diligence efforts.

SBIR/STTR Fast-Track Technical and Business Assistance (TABA): Proposers are encouraged to include up to $6,500 in the Phase I component budget and up to $50,000 in the Phase II component budget to assist in technology commercialization efforts (as outlined in the current SBIR/STTR Policy Directive and the John S. McCain National Defense Authorization Act for Fiscal Year 2019 ). Specifically, this funding is for securing the services of one or more third-party service providers that will assist with one or more of the following commercialization activities:

• Phase II Commercialization Plan research and preparation
• Phase II Broader Impact plan research and preparation
• Making better technical decisions on SBIR/STTR Fast-Track projects;
• Solving technical problems that arise during SBIR/STTR projects;
• Minimizing technical risks associated with SBIR/STTR projects; and
• Commercializing the SBIR/STTR product or process, including securing intellectual property protections

If a proposer is not able to identify what commercial assistance may be required at the time of proposal submission, the proposing small business may block up to the maximum allowable amount for TABA activities (as detailed above) on Line G. Other with the understanding that prior to expending funds for these purposes, the recipient will be required to obtain written approval from the cognizant NSF SBIR/STTR Fast-Track Program Officer.

In addition to the above, for the Phase I component of a Fast-Track project only, NSF permits the inclusion of additional funds on the G budget line, as follows. The funds noted below may ONLY be spent on the commercial or business purposes explicitly permitted below. The proposer may budget up to $10,000 as a direct charge on line G.6 of a Phase I component budget for the following specific purposes related to financials and accounting:

• Hiring a certified public accountant (CPA) to prepare audited, compiled, or reviewed financial statements;
• Hiring a CPA to perform an initial financial viability assessment based on standard financial ratios so the recipient organization would have time to improve their financial position prior to the CAP assessment for the transition to the Phase II component of the Fast-Track project;
• Hiring a CPA to review the adequacy of the recipient's project cost accounting system; and/or purchasing a project cost accounting system.

If the proposer elects to budget funds for one of the above purposes, the Budget Justification should include a brief description of the desired use of funds. The use of funds must be approved by the cognizant NSF SBIR/STTR Fast-Track Program Officer prior to award.

Line I - Indirect Costs . Indirect costs are defined as costs that are necessary and appropriate for the operation of the business, but which are not specifically allocated to the NSF SBIR/STTR Fast-Track project. Common indirect cost expenses include legal and accounting expenses, employee health insurance, fringe benefits, rent, and utilities. If the proposing small business has a Federally negotiated rate, please specify the base and rate and include a copy of the negotiated indirect cost rate agreement. If the proposing business has a history of at least two years of stable operation that reflect the costs expected to occur during the execution of the SBIR/STTR award, please base the indirect rate estimate on this historical data (and provide an explanation if the rate is expected to deviate significantly from the rate used in recent years). Instructions for Indirect Cost Rate (IDC) Proposal Submission Procedures can be found here .

Recipients without experience and knowledge of Federal indirect cost rate negotiation and Federal Acquisition Regulation (FAR) Part 31 Cost Principles may want to consider engaging professional services in preparing an IDC proposal.

If the proposing small business has no negotiated rate with a federal agency, and no previous experience with Federal indirect cost rate negotiation, you may claim (without submitting justification) a total amount of indirect costs (inclusive of fringe benefits) either up to 50% of total budgeted salary and wages on the project or equal to 10% de minimis on MODIFIED total direct costs on the project. Modified Total Direct Cost (MTDC): MTDC means all direct salaries and wages, applicable fringe benefits, materials and supplies, services, travel, and up to the first $25,000 of each subaward (regardless of the period of performance of the subawards under the award). MTDC excludes equipment, capital expenditures, charges for patient care, rental costs, tuition remission, scholarships and fellowships, participant support costs and the portion of each subaward in excess of $25,000. Other items may only be excluded when necessary to avoid a serious inequity in the distribution of indirect costs, and with the approval of the cognizant agency for indirect costs.

Note: NSF does not fund Independent Research and Development (IR&D) as part of an indirect cost rate under its awards. See the FAR 31.205-18(a) for more information.

Line K - Fee . The small business fee is intended to be consistent with normal profit margins provided to profit-making firms for R&D work. Up to seven percent (7%) of the total indirect and direct project costs may be requested as a Small Business Fee for the Phase I budget component. Up to ten percent (10%) of the total indirect and direct project costs may be requested as a fee for the Phase II budget component. The fee applies solely to the small business concern receiving the award and not to any other participant in the project. The fee is not a direct or indirect "cost" item and may be used by the small business concern for any purpose, including additional effort under the NSF SBIR/STTR Fast-Track award (and including items on the "Prohibited Expenditures" list below).

Prohibited Expenditures including, but not limited to, Equipment (during the Phase I component), Foreign Travel (during the Phase I and Phase II components), Participant Support Cots, and Publication Costs are not allowable expenditures as either direct or indirect costs. However, these expenses may be purchased from the small business fee funds (Line K).

Budget Revisions. Budget revisions may be requested by the cognizant SBIR/STTR Program Officer. Revised budgets must contain a revised and complete Budget Justification as described above. Revised budgets with budget impact statements that only address revisions are not acceptable for budget processing, see Budget Revision Instructions .

Note: Should the proposal be considered for funding, the NSF SBIR/STTR Program Officer will refer the proposer to the Cost Analysis and Pre-Award Review (CAP) Division’s SBIR/STTR Administrative/Financial Reviews website . Proposing small businesses in this category will be given 10 calendar days to provide CAP the underlying supporting documentation for their budget. The organization should review and understand the CAP documentation requirements as it prepares its budget. Once NSF requests the underlying supporting documentation for the CAP review, proposers will not be given an opportunity to re-budget unsupported costs. Funding will be provided for only the dollar amount that is reasonable and adequately supported. The awarded budget will reflect the supported dollar amount for the proposed effort. Organizations that accept awards at less than the proposed dollar amount may not reduce the effort to be provided; however, organizations may choose to decline award offers.

Facilities, Equipment and Other Resources. Specify the availability and location of significant equipment, instrumentation, computers, and physical facilities necessary to complete the portion of the research that is to be carried out by the proposing firm in the Phase I or Phase II component of a Fast-Track project. Note that purchase of equipment is NOT permitted in the Phase I component of a Fast-Track proposal. If the equipment, instrumentation, computers, and facilities for this research are not the property (owned or leased) of the proposing firm, include a statement signed by the owner or lessor which affirms the availability of these facilities for use in the proposed research, reasonable lease or rental costs for their use, and any other associated costs. Upload images of the scanned statements into this section.

Many research projects require access to computational, data, analysis, and/or visualization resources to complete the work proposed. For projects that require such resources at scales beyond what may be available locally, researchers in all disciplines can apply for allocations for computer or data resources from over two dozen high-performance computational systems via the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program. See cognizant Program Officer or PAPPG for additional details. If a proposer wants to arrange the use of unique or one-of-a-kind Government facilities, a waiver must be obtained from the Small Business Administration to approve such use.

If no equipment, facilities, or other resources are required for this project, a statement to that effect should be uploaded here.

Senior/Key Personnel Documents. For the Principal Investigator (PI), Co-PI, and for each person listed in the “Senior/Key Personnel” section, the four required documents are listed below.

Biographical Sketch(es) . All proposals are required to include Biographical Sketches for each PI, co-PI (if STTR), and Senior/Key Personnel (individuals with critical expertise who will be working on the project and are employed at the proposing company or at a subaward organization). Proposers must prepare biographical sketch files using SciENcv (Science Experts Network Curriculum Vitae) , which will produce a compliant PDF. Senior/Key Personnel must prepare, save, certify, and submit these documents as part of their proposal via Research.gov.

Full requirements for these documents can be found in the current NSF Proposal and Award Policies and Procedures Guide. Frequently Asked Questions on using SciENcv can be found here .

Current and Pending (Other) Support. This information will provide reviewers with visibility into the potential availability of company personnel during the period of performance if awarded. All PIs, Co-PIs (if STTR), and Senior/Key Personnel must prepare Current and Pending (Other) Support files using SciENcv . Detailed information about the required content is available in the current PAPPG.

For the PI, co-PI (if STTR), and each of the Senior/Key Personnel listed on Line A or B of the budget, please provide the following information, regardless of whether the person will receive salary from the activity:

• Name of sponsoring organization.
• Total award amount (if already awarded) or expected award amount (if pending) for the entire award period covered (including indirect costs).
• Title and performance period of the proposal or award.
• Annual person-months (calendar months) devoted to the project by the PI or Senior/Key Personnel.

Please report:

• All current and pending support for ongoing projects and proposals (from any source, including in kind support or equity investment), including continuing grant and contract funding.
• Proposals submitted to other agencies. Concurrent submission of a proposal to other organizations will not influence the review of the proposal submitted to NSF.
• Upcoming submissions.
• The current Phase I proposal is considered "pending" and therefore MUST appear in the Current and Pending Support form for each PI and Senior/Key Personnel.

Collaborators and Other Affiliations (COA) Information (Single Copy Document). This document must be provided for the PI, Co-PI (if STTR) and each Senior/Key Person. This document will not be viewable by reviewers but will be used by NSF to manage the selection of reviewers. Download the required Collaborators and Other Affiliations template and follow the instructions. Detailed information about the required content is available in the current PAPPG. Frequently Asked Questions on COA can be found here .

Synergistic Activities. Each individual identified as a senior/key person must provide a PDF document of up to one-page that includes a list of up to five distinct examples that demonstrates the broader impact of the individual’s professional and scholarly activities that focus on the integration and transfer of knowledge as well as its creation. Examples of synergistic activities may include but are not limited to the training of junior scientists and engineers in innovation and entrepreneurship; the development of new and novel products, tools, and/or services based on deep technologies; broadening participation of groups underrepresented in STEM; service to the scientific and engineering communities outside the individual’s company; and/or participation in the national and/or international commercial market.

Data Management and Sharing Plan . The Data Management and Sharing Plan should include the statement, "All data generated in this NSF SBIR/STTR Fast-Track project is considered proprietary." This single sentence is sufficient to fulfill the Data Management and Sharing Plan requirement, but proposers may add more detail about how the resulting data will be managed, if they desire. The PDF cannot exceed 2 pages.

Mentoring Plan (Conditionally Required). If a proposal requests funding to support postdoctoral scholars or graduate students at a research institution (through a subaward), a Mentoring Plan MUST be uploaded to the system. The mentoring plan must describe the mentoring that will be provided to all postdoctoral scholars or graduate students supported by the project, regardless of whether they reside at the submitting organization or at any subrecipient organization. Describe only the mentoring activities that will be provided to all postdoctoral scholars or graduate students supported by the project. The PDF cannot exceed 1 page.

Individual Development Plans (IDP) for Postdoctoral Scholars and Graduate Students. For each NSF award that provides substantial support to postdoctoral scholars and graduate students, each individual must have an Individual Development Plan, which is updated annually. The IDP maps the educational goals, career exploration, and professional development of the individual. NSF defines “substantial support” as an individual that has received one person month or more during the annual reporting period under the NSF award. Certification that a postdoctoral scholar(s) and/or graduate student(s) has and IDP must be included in the annual and final reports.

Project Schedule. The required Project Schedule must show the estimated duration and timing of major project tasks that are required to implement the research plan. This document should clearly estimate the initiation and completion of tasks in relation to other tasks within the timeline of the award.

NSF recommends downloading the Project Schedule template and uploading a completed version of this form into Research.gov. This schedule should also provide projected levels of effort for each key person during each reporting period of the project. Key personnel to be listed generally include any senior/key personnel listed on Line A of the main project budget, any persons listed on Line A of any subaward budgets, or any budgeted consultants. The schedule should also include estimates of total level of effort (for all project personnel) and total expenditures for each six-month project period.

Optional. NOTE: Various subsections are REQUIRED depending on the type of proposal (SBIR or STTR), whether the company has a commercialization history, whether this proposal is a resubmission, etc. Please read section requirements carefully.

Letter(s) of Support (REQUIRED) . Three (3) Letters of Support from potential product/service users or customers are required; Up to five (5) Letters of Support may be included. All Letters of Support should be uploaded in Research.gov in one PDF.

Letters of Support should address market validation for the proposed innovation, market opportunity, or small business/team, and add significant credibility to the proposed effort. These Letters should ideally demonstrate that the company has developed partnerships and/or a meaningful dialog with relevant stakeholders (e.g., potential customers, strategic partners, or investors) for the proposed innovation and that a real business opportunity may exist. The Letters of Support must contain affiliation and contact information for the signatory stakeholder.

Letters of commitment and supporting documents from consultants and subawards (or any personnel identified in the Budget Justification) are NOT considered letters of support.

IP (Intellectual Property) Rights Agreement (Required for STTR and strongly recommended for SBIR proposals when there is a subaward to another institution). A fully signed Allocation of Intellectual Property Rights is not required at the initial proposal submission but will be required before a recommendation for an award can be made. For proposal submission, place a draft of the Allocation of Intellectual Property Rights or a letter that includes the name of the partner research institution stating that an agreement will be provided upon Program Officer notification of a potential award recommendation.

The SBIR/STTR Policy Directive indicates: “ The model (IP) agreement will direct the parties to, at a minimum:

• State specifically the degree of responsibility, and ownership of any product, process, or other invention or Innovation resulting from the cooperative research. The degree of responsibility shall include responsibility for expenses and liability, and the degree of ownership shall also include the specific rights to revenues and profits.
• State which party may obtain United States or foreign patents or otherwise protect any inventions resulting from the cooperative research.
• State which party has the right to any continuation of research, including non-STTR follow-on awards. ”

Other Personnel Biographical Information (Strongly Recommended) . This section can be used to provide additional biographical information about project participants who are not listed as Senior/Key Personnel for the small business or for a subawardee as well as for writers of Letters of Support. Biographical sketches should be prepared using SciENcv and uploaded as a single PDF.

Other Supplementary Documents. The required other supplementary documents of an NSF SBIR/STTR Phase II proposal are limited to the following (if applicable).

• Company Commercialization History (required if the proposer has received any prior SBIR or STTR Phase II awards). This section is required for any proposer who has ever received a Phase II SBIR or STTR award (from any Federal agency). All items MUST be addressed in the format given in the NSF Commercialization History Template . Changes to the NSF template, additional narratives and/or commercialization history documents from other agencies are not permitted.
• Letters of Commitment from Subawardees and Consultants (Required, but may be provided in post-award diligence). Please refer to Budget and Budget Justification for details.

List of Suggested Reviewers (Single Copy Document). This section can be used to suggest the names of reviewers who might be appropriate to assess the technical and commercial merits of the proposal. Reviewers who have significant personal or professional relationships with the proposing small business or its personnel should generally not be included.

List of Reviewers Not to Include (Single Copy Document).This section can be used by the proposer to suggest names (or even specific affiliations) of reviewers/panelists not to be involved in the review of their proposal.

Deviation Authorization (Single Copy Document).This section should generally not be used unless NSF staff have specifically instructed the proposer to do so.

Additional Single Copy Documents. This section should be blank.

Cost Sharing:

Proposers are required to prepare and submit all proposals for this solicitation via Research.gov. Detailed instructions regarding the technical aspects or proposal preparation and submission via Research.gov are available at: https://www.research.gov/research-portal/appmanager/base/desktop?_nfpb=true&_pageLabel=research_node_display&_nodePath=/researchGov/Service/Desktop/ProposalPreparationandSubmission.html . For Research.gov user support, call the Research.gov Help Desk at 1-800-673-6188 or e-mail [email protected] . The Research.gov Help Desk answers general technical questions related to the use of the Research.gov system. Specific questions related to this program solicitation should be referred to the NSF program staff contact(s) listed in Section VIII of this funding opportunity.

D. Research.gov Requirements

Proposers are required to prepare and submit all proposals for this program solicitation through use of the NSF Research.gov system. Detailed instructions regarding the technical aspects of proposal preparation and submission via Research.gov are available at: https://www.research.gov/research-web/content/aboutpsm . For Research.gov user support, call the Research.gov Help Desk at 1-800-381-1532 or e-mail [email protected] . The Research.gov Help Desk answers general technical questions related to the use of the Research.gov system. Specific questions related to this program solicitation should be referred to the NSF program staff contact(s) listed in Section VIII of this funding opportunity.

Submission of Electronically Signed Cover Sheets . The Authorized Organizational Representative (AOR) must electronically sign the proposal Cover Sheet to submit the required proposal certifications (see PAPPG Chapter II.C.1.d for a listing of the certifications). The AOR must provide the required electronic certifications at the time of proposal submission. Further instructions regarding this process are available on the Research.gov Website at: https://www.research.gov/research-web/content/aboutpsm .

VI. NSF Proposal Processing And Review Procedures

Proposals received by NSF are assigned to the appropriate NSF program for acknowledgement and, if they meet NSF requirements, for review. All proposals are carefully reviewed by a scientist, engineer, or educator serving as an NSF Program Officer, and usually by three to ten other persons outside NSF either as ad hoc reviewers, panelists, or both, who are experts in the particular fields represented by the proposal. These reviewers are selected by Program Officers charged with oversight of the review process. Proposers are invited to suggest names of persons they believe are especially well qualified to review the proposal and/or persons they would prefer not review the proposal. These suggestions may serve as one source in the reviewer selection process at the Program Officer's discretion. Submission of such names, however, is optional. Care is taken to ensure that reviewers have no conflicts of interest with the proposal. In addition, Program Officers may obtain comments from site visits before recommending final action on proposals. Senior NSF staff further review recommendations for awards. A flowchart that depicts the entire NSF proposal and award process (and associated timeline) is included in PAPPG Exhibit III-1.

A comprehensive description of the Foundation's merit review process is available on the NSF website at: https://www.nsf.gov/bfa/dias/policy/merit_review/ .

Proposers should also be aware of core strategies that are essential to the fulfillment of NSF's mission, as articulated in Leading the World in Discovery and Innovation, STEM Talent Development and the Delivery of Benefits from Research - NSF Strategic Plan for Fiscal Years (FY) 2022 - 2026 . These strategies are integrated in the program planning and implementation process, of which proposal review is one part. NSF's mission is particularly well-implemented through the integration of research and education and broadening participation in NSF programs, projects, and activities.

One of the strategic objectives in support of NSF's mission is to foster integration of research and education through the programs, projects, and activities it supports at academic and research institutions. These institutions must recruit, train, and prepare a diverse STEM workforce to advance the frontiers of science and participate in the U.S. technology-based economy. NSF's contribution to the national innovation ecosystem is to provide cutting-edge research under the guidance of the Nation's most creative scientists and engineers. NSF also supports development of a strong science, technology, engineering, and mathematics (STEM) workforce by investing in building the knowledge that informs improvements in STEM teaching and learning.

NSF's mission calls for the broadening of opportunities and expanding participation of groups, institutions, and geographic regions that are underrepresented in STEM disciplines, which is essential to the health and vitality of science and engineering. NSF is committed to this principle of diversity and deems it central to the programs, projects, and activities it considers and supports.

A. Merit Review Principles and Criteria

The National Science Foundation strives to invest in a robust and diverse portfolio of projects that creates new knowledge and enables breakthroughs in understanding across all areas of science and engineering research and education. To identify which projects to support, NSF relies on a merit review process that incorporates consideration of both the technical aspects of a proposed project and its potential to contribute more broadly to advancing NSF's mission "to promote the progress of science; to advance the national health, prosperity, and welfare; to secure the national defense; and for other purposes." NSF makes every effort to conduct a fair, competitive, transparent merit review process for the selection of projects.

1. Merit Review Principles

These principles are to be given due diligence by PIs and organizations when preparing proposals and managing projects, by reviewers when reading and evaluating proposals, and by NSF program staff when determining whether or not to recommend proposals for funding and while overseeing awards. Given that NSF is the primary federal agency charged with nurturing and supporting excellence in basic research and education, the following three principles apply:

• All NSF projects should be of the highest quality and have the potential to advance, if not transform, the frontiers of knowledge.
• NSF projects, in the aggregate, should contribute more broadly to achieving societal goals. These "Broader Impacts" may be accomplished through the research itself, through activities that are directly related to specific research projects, or through activities that are supported by, but are complementary to, the project. The project activities may be based on previously established and/or innovative methods and approaches, but in either case must be well justified.
• Meaningful assessment and evaluation of NSF funded projects should be based on appropriate metrics, keeping in mind the likely correlation between the effect of broader impacts and the resources provided to implement projects. If the size of the activity is limited, evaluation of that activity in isolation is not likely to be meaningful. Thus, assessing the effectiveness of these activities may best be done at a higher, more aggregated, level than the individual project.

With respect to the third principle, even if assessment of Broader Impacts outcomes for particular projects is done at an aggregated level, PIs are expected to be accountable for carrying out the activities described in the funded project. Thus, individual projects should include clearly stated goals, specific descriptions of the activities that the PI intends to do, and a plan in place to document the outputs of those activities.

These three merit review principles provide the basis for the merit review criteria, as well as a context within which the users of the criteria can better understand their intent.

2. Merit Review Criteria

All NSF proposals are evaluated through use of the two National Science Board approved merit review criteria. In some instances, however, NSF will employ additional criteria as required to highlight the specific objectives of certain programs and activities.

The two merit review criteria are listed below. Both criteria are to be given full consideration during the review and decision-making processes; each criterion is necessary but neither, by itself, is sufficient. Therefore, proposers must fully address both criteria. (PAPPG Chapter II.D.2.d(i). contains additional information for use by proposers in development of the Project Description section of the proposal). Reviewers are strongly encouraged to review the criteria, including PAPPG Chapter II.D.2.d(i), prior to the review of a proposal.

When evaluating NSF proposals, reviewers will be asked to consider what the proposers want to do, why they want to do it, how they plan to do it, how they will know if they succeed, and what benefits could accrue if the project is successful. These issues apply both to the technical aspects of the proposal and the way in which the project may make broader contributions. To that end, reviewers will be asked to evaluate all proposals against two criteria:

• Intellectual Merit: The Intellectual Merit criterion encompasses the potential to advance knowledge; and
• Broader Impacts: The Broader Impacts criterion encompasses the potential to benefit society and contribute to the achievement of specific, desired societal outcomes.

The following elements should be considered in the review for both criteria:

• Advance knowledge and understanding within its own field or across different fields (Intellectual Merit); and
• Benefit society or advance desired societal outcomes (Broader Impacts)?
• To what extent do the proposed activities suggest and explore creative, original, or potentially transformative concepts?
• Is the plan for carrying out the proposed activities well-reasoned, well-organized, and based on a sound rationale? Does the plan incorporate a mechanism to assess success?
• How well qualified is the individual, team, or organization to conduct the proposed activities?
• Are there adequate resources available to the PI (either at the home organization or through collaborations) to carry out the proposed activities?

Broader impacts may be accomplished through the research itself, through the activities that are directly related to specific research projects, or through activities that are supported by, but are complementary to, the project. NSF values the advancement of scientific knowledge and activities that contribute to achievement of societally relevant outcomes. Such outcomes include, but are not limited to: full participation of women, persons with disabilities, and other underrepresented groups in science, technology, engineering, and mathematics (STEM); improved STEM education and educator development at any level; increased public scientific literacy and public engagement with science and technology; improved well-being of individuals in society; development of a diverse, globally competitive STEM workforce; increased partnerships between academia, industry, and others; improved national security; increased economic competitiveness of the United States; and enhanced infrastructure for research and education.

Proposers are reminded that reviewers will also be asked to review the Data Management and Sharing Plan and the Mentoring Plan, as appropriate.

Additional Solicitation Specific Review Criteria

The NSF SBIR/STTR Fast-Track programs have additional criteria that reflect the emphasis on commercialization and complement the standard NSF review criteria listed above. The following elements will be considered in the review of the Commercialization Potential .

• Is there a significant market opportunity that could be addressed by the proposed product, process, or service?
• Does the company possess a significant and durable competitive advantage, based on scientific or technological innovation, that would be difficult for competitors to neutralize or replicate?
• Is there a compelling potential business model?
• Does the proposing company/team have the essential elements, including expertise, structure, and experience, that would suggest the potential for strong commercial outcomes?
• Will NSF support serve as a catalyst to improve substantially the technical and commercial impact of the underlying commercial endeavor?

NSF SBIR/STTR Fast-Track Award Considerations

An NSF SBIR/STTR Fast-Track proposal includes Phase I and Phase II components. Each component includes an R&D plan and a budget. In addition, the proposal will include a section on the company and team and a section on the Commercialization Plan. Hence, the core of a Fast-Track proposal comprises the following elements:

• Phase I Budget and Budget Justification
• Phase II Budget and Budget Justification
• The Company and Team
• Commercialization Plan

The review of an NSF SBIR/STTR Fast-Track proposal will include a review of both the Phase I and Phase II components of the proposal. A team submitting an NSF SBIR/STTR Fast-Track proposal must have NSF-funded research lineage; an understanding of the target market, product-market fit and initial target customers; and a complete team.

An NSF SBIR/STTR Fast-Track proposal must include specific, quantifiable performance targets for the Phase I component of the project. These Phase I targets may be renegotiated with the cognizant Program Officer during post-review diligence, so that at the start of the Fast-Track project, there will be agreed performance targets in place for the Phase I component.

Due Diligence. Once the panel and/or ad hoc review of an individual NSF SBIR or STTR Fast-Track proposal has concluded and the proposal is considered potentially meritorious, a follow-on due diligence process may be conducted in which the Principal Investigator will be asked to provide additional information and/or to answer questions specific to their proposal in order to inform the final decision. This due diligence process will address weaknesses and questions raised during the external merit review as well as by the cognizant SBIR/STTR Fast-Track Program Officer. The due diligence process may include requests for clarification of the company structure, key personnel, conflicts of interest, foreign influence, cybersecurity practices, or other issues as determined by NSF. Participation in the diligence process is not a guarantee of an award.

Financial Viability. If the small business' proposal is to be further considered for funding after it is competitively reviewed, the cognizant NSF SBIR/STTR Fast-Track Program Officer will refer the proposer to the Cost Analysis and Pre-Award Review (CAP) Administrative/Financial Reviews Site . These reviews are conducted to evaluate a prospective recipient's ability to manage a Federal award responsibly, effectively, and efficiently.

After programmatic approval has been obtained, the proposals recommended for funding will be forwarded to the Division of Grants and Agreements for review of business, financial, and policy implications. After an administrative review has occurred, Grants and Agreements Officers perform the processing and issuance of an award or other agreement. Proposers are cautioned that only a Grants and Agreements Officer may make commitments, obligations, or awards on behalf of NSF or authorize the expenditure of funds. No commitment on the part of NSF should be inferred from technical or budgetary discussions with an NSF Program Officer. A Principal Investigator or organization that makes financial or personnel commitments in the absence of a grant or cooperative agreement signed by the NSF Grants and Agreements Officer does so at their own risk.

The Phase I and Phase II components of a NSF SBIR/STTR Fast-Track proposal will be reviewed and evaluated separately. NSF SBIR/STTR Fast-Track proposals submitted to this solicitation for which the Phase I component is considered meritorious but the Phase II component is not considered meritorious may, based on budgetary considerations and at NSF's discretion, be considered for award as regular NSF SBIR/STTR Phase I projects, in which case (if awarded) the company would subsequently apply for NSF SBIR/STTR Phase II funding via the regular process (i.e., not via the Fast-Track process).

NSF requires each NSF SBIR/STTR Fast-Track recipient company to attend and participate in the NSF SBIR/STTR Phase I Awardees Conference.

Once an award or declination decision has been made, Principal Investigators are provided feedback about their proposals. In all cases, reviews are treated as confidential documents. Verbatim copies of reviews, excluding the names of the reviewers or any reviewer-identifying information, and the panel summary (if a panel summary was prepared) will be available to the proposer via research.gov.

NSF SBIR Phase II proposals submitted to this solicitation which are considered meritorious, and which meet all the requirements of the NSF STTR Phase II program may, based on budgetary considerations and at NSF's discretion, be converted for award as an NSF STTR Phase II project. NSF may also, at its discretion, convert NSF STTR Phase II proposals to NSF SBIR Phase II proposals.

Supplemental Funding. America’s Seed Fund powered by NSF is committed to assisting SBIR/STTR Phase II recipients to successfully commercialize their innovation research, grow their company and create jobs by attracting new investments and partnerships. To reinforce these commitments, the programs support a broad number of supplements and other opportunities. For more information, see: Supplemental Funding Overview , and the linked Dear Colleagues Letters.

Debriefing on Unsuccessful Proposals . As outlined in Chapter IV of the PAPPG, a proposer may request additional information from the cognizant Program Officer or Division Director. Proposers may contact the cognizant Program Officer to set up a date/time for a debrief call.

Resubmission. Declined NSF SBIR/STTR Fast-Track proposals are NOT eligible for resubmission. A proposer of a previously declined proposal must submit a new Project Pitch and, if invited, submit a new proposal after substantial revision, addressing the reviewers’, panel’s (if appropriate), and Program Officer’s concerns.

B. Review and Selection Process

Proposals submitted in response to this program solicitation will be reviewed by Ad hoc Review and/or Panel Review.

Reviewers will be asked to evaluate proposals using two National Science Board approved merit review criteria and, if applicable, additional program specific criteria. A summary rating and accompanying narrative will generally be completed and submitted by each reviewer and/or panel. The Program Officer assigned to manage the proposal's review will consider the advice of reviewers and will formulate a recommendation.

After scientific, technical and programmatic review and consideration of appropriate factors, the NSF Program Officer recommends to the cognizant Division Director whether the proposal should be declined or recommended for award. NSF strives to be able to tell proposers whether their proposals have been declined or recommended for funding within six months. Large or particularly complex proposals or proposals from new recipients may require additional review and processing time. The time interval begins on the deadline or target date, or receipt date, whichever is later. The interval ends when the Division Director acts upon the Program Officer's recommendation.

After programmatic approval has been obtained, the proposals recommended for funding will be forwarded to the Division of Grants and Agreements or the Division of Acquisition and Cooperative Support for review of business, financial, and policy implications. After an administrative review has occurred, Grants and Agreements Officers perform the processing and issuance of a grant or other agreement. Proposers are cautioned that only a Grants and Agreements Officer may make commitments, obligations or awards on behalf of NSF or authorize the expenditure of funds. No commitment on the part of NSF should be inferred from technical or budgetary discussions with a NSF Program Officer. A Principal Investigator or organization that makes financial or personnel commitments in the absence of a grant or cooperative agreement signed by the NSF Grants and Agreements Officer does so at their own risk.

Once an award or declination decision has been made, Principal Investigators are provided feedback about their proposals. In all cases, reviews are treated as confidential documents. Verbatim copies of reviews, excluding the names of the reviewers or any reviewer-identifying information, are sent to the Principal Investigator/Project Director by the Program Officer. In addition, the proposer will receive an explanation of the decision to award or decline funding.

VII. Award Administration Information

A. notification of the award.

Notification of the award is made to the submitting organization by an NSF Grants and Agreements Officer. Organizations whose proposals are declined will be advised as promptly as possible by the cognizant NSF Program administering the program. Verbatim copies of reviews, not including the identity of the reviewer, will be provided automatically to the Principal Investigator. (See Section VI.B. for additional information on the review process.)

B. Award Conditions

An NSF award consists of: (1) the award notice, which includes any special provisions applicable to the award and any numbered amendments thereto; (2) the budget, which indicates the amounts, by categories of expense, on which NSF has based its support (or otherwise communicates any specific approvals or disapprovals of proposed expenditures); (3) the proposal referenced in the award notice; (4) the applicable award conditions, such as Grant General Conditions (GC-1)*; or Research Terms and Conditions* and (5) any announcement or other NSF issuance that may be incorporated by reference in the award notice. Cooperative agreements also are administered in accordance with NSF Cooperative Agreement Financial and Administrative Terms and Conditions (CA-FATC) and the applicable Programmatic Terms and Conditions. NSF awards are electronically signed by an NSF Grants and Agreements Officer and transmitted electronically to the organization via e-mail.

*These documents may be accessed electronically on NSF's Website at https://www.nsf.gov/awards/managing/award_conditions.jsp?org=NSF . Paper copies may be obtained from the NSF Publications Clearinghouse, telephone (703) 292-8134 or by e-mail from [email protected] .

More comprehensive information on NSF Award Conditions and other important information on the administration of NSF awards is contained in the NSF Proposal & Award Policies & Procedures Guide (PAPPG) Chapter VII, available electronically on the NSF Website at https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg .

Administrative and National Policy Requirements

Build America, Buy America

As expressed in Executive Order 14005, Ensuring the Future is Made in All of America by All of America’s Workers (86 FR 7475), it is the policy of the executive branch to use terms and conditions of Federal financial assistance awards to maximize, consistent with law, the use of goods, products, and materials produced in, and services offered in, the United States.

Consistent with the requirements of the Build America, Buy America Act (Pub. L. 117-58, Division G, Title IX, Subtitle A, November 15, 2021), no funding made available through this funding opportunity may be obligated for infrastructure projects under an award unless all iron, steel, manufactured products, and construction materials used in the project are produced in the United States. For additional information, visit NSF’s Build America, Buy America webpage.

Special Award Conditions:

NSF SBIR/STTR Fast-Track awards are subject to the availability of funds. NSF has no obligation to make any specific number of Fast-Track awards based on a solicitation and may elect to make several or no awards under any specific technical topic or subtopic.

The NSF SBIR/STTR Fast-Track fixed amount cooperative agreements will not exceed $1,555,000 per award and normally will be made for a 24-month period of performance.

NSF requires each NSF SBIR/STTR Fast-Track recipient company to attend and participate in the NSF SBIR/STTR Awardees’ Conference.

Terms and Conditions for awards made under this SBIR/STTR Phase II solicitation were posted in May 2024 and are available on the Award Conditions page, under SBIR/STTR Terms and Conditions . The linked page includes "SBIR/STTR Phase II Cooperative Agreement Financial & Administrative Terms and Conditions (SBIR/STTR-II-CA-FATC)" AND "SBIR/STTR Phase II General Terms & Conditions."

The award notice specifies a pre-determined, fixed amount of NSF support for the project described in the referenced proposal. This amount is based upon the budget approved by NSF for the referenced proposal, as amended.

Phase II Transition:

Phase II component funding will be released to the Fast-Track recipient contingent on successfully passing both Stage Gates 1 and 2 of the Phase II Transition Review.

Companies that do not pass either Stage Gate 1 or 2 will be limited to Phase I funding, and the award will conclude at the end of the Phase I component. The final $25,000 will be made available to the company upon submission and NSF approval of the Phase I final project report and upon submission of a Project Outcomes report.

A decision by NSF not to provide additional funding following either the Stage Gate 1 or Stage Gate 2 review will NOT be eligible for reconsideration or termination review as defined in Chapter XII.A.4 of the PAPPG.

Payment Schedule:

Companies that pass both Stage Gates 1 and 2 will receive access to the final $25,000 of Phase I component funding and a funding increment for the Phase II component of the award. Phase II component payments will generally be managed in accordance with the following schedule:

• 25% Advance Payment.
• 25% upon acceptance by an NSF SBIR Fast-Track Program Officer of first interim report.
• 25% upon acceptance by an NSF SBIR Fast-Track Program Officer of second interim report.
• The remainder of funds, less $25,000, upon acceptance by an NSF SBIR Fast-Track Program Officer of third interim report.
• Final $25,000 upon acceptance by an NSF SBIR Fast-Track Program Officer of a satisfactory final annual project report and upon submission of a Project Outcomes report.

A deviation from the standard payment schedule can be requested if the standard schedule poses significant difficulties for the recipient or would negatively affect the execution of the project. If the standard payment schedule as described above is not appropriate, please request alternative amounts for each payment, and provide a brief justification for the departure from the standard schedule.

Payment of the award amount is subject to compliance with the award terms and conditions and NSF's acceptance of the reports submitted by the recipient. On the basis of its review of these reports and/or other pertinent information, NSF reserves the right to modify the payment schedule or suspend or terminate the award, if NSF determines that such actions are appropriate. If estimated total expenditures are significantly less than the award amount, the recipient shall contact NSF to renegotiate the scope of this award. Similarly, if the recipient expects that the full scope of work will be completed at a total cost significantly lower than the award amount, it is the obligation of the recipient to promptly notify NSF.

C. Reporting Requirements

For all multi-year grants (including both standard and continuing grants), the Principal Investigator must submit an annual project report to the cognizant Program Officer no later than 90 days prior to the end of the current budget period. (Some programs or awards require submission of more frequent project reports). No later than 120 days following expiration of a grant, the PI also is required to submit a final annual project report, and a project outcomes report for the general public.

Failure to provide the required annual or final annual project reports, or the project outcomes report, will delay NSF review and processing of any future funding increments as well as any pending proposals for all identified PIs and co-PIs on a given award. PIs should examine the formats of the required reports in advance to assure availability of required data.

PIs are required to use NSF's electronic project-reporting system, available through Research.gov, for preparation and submission of annual and final annual project reports. Such reports provide information on accomplishments, project participants (individual and organizational), publications, and other specific products and impacts of the project. Submission of the report via Research.gov constitutes certification by the PI that the contents of the report are accurate and complete. The project outcomes report also must be prepared and submitted using Research.gov. This report serves as a brief summary, prepared specifically for the public, of the nature and outcomes of the project. This report will be posted on the NSF website exactly as it is submitted by the PI.

More comprehensive information on NSF Reporting Requirements and other important information on the administration of NSF awards is contained in the NSF Proposal & Award Policies & Procedures Guide (PAPPG) Chapter VII, available electronically on the NSF Website at https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg .

VIII. Agency Contacts

Please note that the program contact information is current at the time of publishing. See program website for any updates to the points of contact.

General inquiries regarding this program should be made to:

For questions related to the use of NSF systems contact:

• NSF Help Desk: 1-800-381-1532
• Research.gov Help Desk e-mail: [email protected]

For questions relating to Grants.gov contact:

Grants.gov Contact Center: If the Authorized Organizational Representatives (AOR) has not received a confirmation message from Grants.gov within 48 hours of submission of application, please contact via telephone: 1-800-518-4726; e-mail: [email protected] .

IX. Other Information

The NSF website provides the most comprehensive source of information on NSF Directorates (including contact information), programs and funding opportunities. Use of this website by potential proposers is strongly encouraged. In addition, "NSF Update" is an information-delivery system designed to keep potential proposers and other interested parties apprised of new NSF funding opportunities and publications, important changes in proposal and award policies and procedures, and upcoming NSF Grants Conferences . Subscribers are informed through e-mail or the user's Web browser each time new publications are issued that match their identified interests. "NSF Update" also is available on NSF's website .

Grants.gov provides an additional electronic capability to search for Federal government-wide grant opportunities. NSF funding opportunities may be accessed via this mechanism. Further information on Grants.gov may be obtained at https://www.grants.gov .

About The National Science Foundation

The National Science Foundation (NSF) is an independent Federal agency created by the National Science Foundation Act of 1950, as amended (42 USC 1861-75). The Act states the purpose of the NSF is "to promote the progress of science; [and] to advance the national health, prosperity, and welfare by supporting research and education in all fields of science and engineering."

NSF funds research and education in most fields of science and engineering. It does this through grants and cooperative agreements to more than 2,000 colleges, universities, K-12 school systems, businesses, informal science organizations and other research organizations throughout the US. The Foundation accounts for about one-fourth of Federal support to academic institutions for basic research.

NSF receives approximately 55,000 proposals each year for research, education and training projects, of which approximately 11,000 are funded. In addition, the Foundation receives several thousand applications for graduate and postdoctoral fellowships. The agency operates no laboratories itself but does support National Research Centers, user facilities, certain oceanographic vessels and Arctic and Antarctic research stations. The Foundation also supports cooperative research between universities and industry, US participation in international scientific and engineering efforts, and educational activities at every academic level.

Facilitation Awards for Scientists and Engineers with Disabilities (FASED) provide funding for special assistance or equipment to enable persons with disabilities to work on NSF-supported projects. See the NSF Proposal & Award Policies & Procedures Guide Chapter II.F.7 for instructions regarding preparation of these types of proposals.

The National Science Foundation has Telephonic Device for the Deaf (TDD) and Federal Information Relay Service (FIRS) capabilities that enable individuals with hearing impairments to communicate with the Foundation about NSF programs, employment or general information. TDD may be accessed at (703) 292-5090 and (800) 281-8749, FIRS at (800) 877-8339.

The National Science Foundation Information Center may be reached at (703) 292-5111.

The National Science Foundation promotes and advances scientific progress in the United States by competitively awarding grants and cooperative agreements for research and education in the sciences, mathematics, and engineering. To get the latest information about program deadlines, to download copies of NSF publications, and to access abstracts of awards, visit the NSF Website at 2415 Eisenhower Avenue, Alexandria, VA 22314 (NSF Information Center) (703) 292-5111 (703) 292-5090 Send an e-mail to: or telephone: (703) 292-8134 (703) 292-5111 Privacy Act And Public Burden Statements The information requested on proposal forms and project reports is solicited under the authority of the National Science Foundation Act of 1950, as amended. The information on proposal forms will be used in connection with the selection of qualified proposals; and project reports submitted by proposers will be used for program evaluation and reporting within the Executive Branch and to Congress. The information requested may be disclosed to qualified reviewers and staff assistants as part of the proposal review process; to proposer institutions/grantees to provide or obtain data regarding the proposal review process, award decisions, or the administration of awards; to government contractors, experts, volunteers and researchers and educators as necessary to complete assigned work; to other government agencies or other entities needing information regarding proposers or nominees as part of a joint application review process, or in order to coordinate programs or policy; and to another Federal agency, court, or party in a court or Federal administrative proceeding if the government is a party. Information about Principal Investigators may be added to the Reviewer file and used to select potential candidates to serve as peer reviewers or advisory committee members. See System of Record Notices , NSF-50 , "Principal Investigator/Proposal File and Associated Records," and NSF-51 , "Reviewer/Proposal File and Associated Records.” Submission of the information is voluntary. Failure to provide full and complete information, however, may reduce the possibility of receiving an award. An agency may not conduct or sponsor, and a person is not required to respond to, an information collection unless it displays a valid Office of Management and Budget (OMB) control number. The OMB control number for this collection is 3145-0058. Public reporting burden for this collection of information is estimated to average 120 hours per response, including the time for reviewing instructions. Send comments regarding the burden estimate and any other aspect of this collection of information, including suggestions for reducing this burden, to: Suzanne H. Plimpton Reports Clearance Officer Policy Office, Division of Institution and Award Support Office of Budget, Finance, and Award Management National Science Foundation Alexandria, VA 22314 Thesis Action Plan New Academic Project Planner Literature Navigator Thesis dialogue blueprint, writing wizard's template, research proposal compass. Why students love us Rebels Blog Why we are different All Products Coming Soon What Makes a Good Hypothesis: Key Elements and Tips A well-crafted hypothesis is the cornerstone of any successful research study. It serves as a clear, testable statement that provides direction and focus for scientific inquiry. Understanding the key elements that make a good hypothesis can significantly enhance the quality and impact of your research. This article explores the essential components and offers practical tips for developing strong hypotheses. Key Takeaways A good hypothesis should be based on thorough research and be testable. It is crucial to clearly define independent, dependent, and control variables in your hypothesis. A strong hypothesis connects theoretical frameworks with empirical data, often derived from a comprehensive literature review. Evaluating and refining your hypothesis is an iterative process that benefits from peer review and feedback. Clarity and precision are essential in hypothesis writing to avoid ambiguity and ensure testability. Defining a Good Hypothesis A good hypothesis is a foundational element in any research project. It serves as a clear, testable statement that predicts an expected relationship between variables. Crafting a well-defined hypothesis is crucial for guiding your research and ensuring its credibility and reliability. Formulating a Testable Hypothesis Creating a testable hypothesis is a fundamental step in the research process. A good hypothesis can be clearly refuted or supported by an experiment. To achieve this, you must ensure that your hypothesis is specific and measurable. This section will guide you through the essential steps to develop a robust hypothesis, provide examples of testable hypotheses , and highlight common pitfalls to avoid. Role of Variables in a Hypothesis Understanding the role of variables in a hypothesis is crucial for formulating a robust and testable hypothesis. Variables are the building blocks of any hypothesis, and they help in defining the relationship you aim to investigate. A well-defined hypothesis clearly identifies the independent and dependent variables and often includes control variables to ensure the validity of the results. Theoretical Framework and Hypothesis Development Connecting theory to hypothesis. A robust theoretical framework is essential for developing a strong hypothesis. Theories provide a foundation upon which hypotheses are built, ensuring that your research is grounded in established knowledge. By connecting your hypothesis to existing theories, you can better articulate the significance of your research question and its potential contributions to the field. Literature Review's Role Conducting a thorough literature review is crucial in hypothesis development. It helps you identify gaps in current research and understand how your work can address these gaps. Reviewing existing studies allows you to refine your research question and formulate a hypothesis that is both innovative and testable. As noted in the article "Organizing Your Social Sciences Research Paper," the theoretical framework must demonstrate an understanding of relevant theories and concepts. Examples from Various Disciplines Different academic disciplines utilize theoretical frameworks in unique ways to develop hypotheses. For instance, in social sciences, hypotheses often emerge from theories related to human behavior and societal structures. In natural sciences, hypotheses might be derived from established scientific laws and principles. By examining examples from various fields, you can gain insights into how to effectively link theory and hypothesis in your own research. Evaluating the Strength of a Hypothesis Evaluating the strength of a hypothesis is a critical step in the research process. A robust hypothesis not only guides your research but also ensures that your findings are credible and meaningful. Here are some key elements to consider when evaluating a hypothesis: Criteria for Evaluation A strong hypothesis should be clear, specific, and testable. It must be grounded in existing theory and literature, providing a solid foundation for your research. Additionally, it should be capable of being supported or refuted through empirical data. A well-formulated hypothesis will often lead to more reliable and valid results. Peer Review Process The peer review process is essential for validating the strength of your hypothesis. By subjecting your hypothesis to the scrutiny of other experts in the field, you can identify potential weaknesses and areas for improvement. This process not only enhances the credibility of your research but also ensures that your hypothesis is robust and well-founded. Revising and Refining Hypotheses Hypothesis development is an iterative process. Based on feedback from the peer review process and initial data collection, you may need to revise and refine your hypothesis. This step is crucial for ensuring that your hypothesis remains relevant and testable as your research progresses. Remember, the goal is to create a hypothesis that can withstand rigorous statistical storytelling and empirical testing. Practical Tips for Writing Hypotheses Writing a strong hypothesis is a critical step in the research process. Here are some practical tips to help you craft effective hypotheses. Clarity and Precision A good hypothesis should be clear and precise. Avoid vague language and ensure that every word serves a purpose. Every word matters in a hypothesis, as it forms the crucial link between theoretical ideas and empirical data. This clarity will help you avoid the thesis anxiety that often accompanies the research process. Avoiding Ambiguity Ambiguity can undermine the strength of your hypothesis. Make sure your hypothesis is specific and testable. If a hypothesis cannot be settled with a specific empirical test, it should be broken down into several hypotheses. This approach will help you avoid common pitfalls and ensure that your hypothesis is robust. Iterative Process of Refinement Developing a good hypothesis is often an iterative process. You may need to write and rewrite your hypothesis several times to find the best wording. This process of refinement is essential for crafting a hypothesis that accurately describes what you are testing. Remember, the importance of a strong thesis statement in academic writing cannot be overstated. Case Studies of Good Hypotheses Successful hypotheses in science. In scientific research, a well-formulated hypothesis is crucial for guiding experiments and interpreting results. For instance, the hypothesis that "increasing the concentration of carbon dioxide will enhance the rate of photosynthesis in plants" has been extensively tested and supported by numerous studies. This hypothesis is testable and grounded in existing theory , making it a strong example of a good scientific hypothesis. Social Science Examples In social sciences, hypotheses often explore relationships between variables in human behavior. A notable example is the hypothesis that "higher levels of education lead to increased political participation." This hypothesis is not only testable but also relevant to societal issues, making it a valuable research question. Utilizing tools like literature navigator and academic project planner can help in refining such hypotheses. Lessons Learned from Failed Hypotheses Failed hypotheses are not necessarily a setback; they offer valuable lessons. For example, the hypothesis that "reducing prices will make customers happy" might seem logical but lacks specificity and testability. Such hypotheses highlight the importance of clarity and precision in hypothesis formulation. By learning from these failures, researchers can improve their strategies for choosing a research topic, from clarity to feasibility . In our "Case Studies of Good Hypotheses" section, we delve into real-world examples that showcase the power of well-crafted hypotheses in academic research. These case studies not only highlight successful strategies but also provide actionable insights for your own thesis journey. Ready to transform your thesis writing experience? Visit our website to explore our step-by-step Thesis Action Plan and claim your special offer now ! In conclusion, crafting a good hypothesis is a fundamental step in the scientific research process. It serves as the foundation upon which experiments are designed and data is interpreted. A well-formulated hypothesis should be clear, concise, and testable, incorporating both independent and dependent variables. It must be grounded in existing literature and theoretical frameworks, ensuring that it can be empirically evaluated. By adhering to these key elements, researchers can enhance the validity and reliability of their studies, ultimately contributing to the advancement of knowledge in their respective fields. As such, understanding and applying the principles of hypothesis formulation is essential for any aspiring researcher. Frequently Asked Questions What is a hypothesis. A hypothesis is a testable statement that predicts the relationship between two or more variables. It serves as a foundation for scientific research and experimentation. What are the key characteristics of a good hypothesis? A good hypothesis should be clear, concise, testable, and based on existing knowledge. It should also include independent and dependent variables and be capable of being supported or refuted through experimentation. How do I formulate a testable hypothesis? To formulate a testable hypothesis, start by conducting thorough research on your topic. Identify the variables involved, and then create a clear and concise statement predicting the relationship between these variables. Ensure that your hypothesis can be tested through experimentation or observation. What are independent and dependent variables in a hypothesis? Independent variables are the factors that are manipulated or changed in an experiment, while dependent variables are the factors that are measured or observed. The dependent variable is expected to change in response to the independent variable. What is the role of a literature review in hypothesis development? A literature review helps you understand the existing research on your topic, identify gaps in knowledge, and build a theoretical framework. It provides the background information needed to formulate a well-informed and testable hypothesis. Can a hypothesis be revised or refined? Yes, a hypothesis can be revised or refined based on new evidence or feedback from the peer review process. It is important to remain flexible and open to modifying your hypothesis as your research progresses. Discovering Statistics Using IBM SPSS Statistics: A Fun and Informative Guide Unlocking the Power of Data: A Review of 'Essentials of Modern Business Statistics with Microsoft Excel' Discovering Statistics Using SAS: A Comprehensive Review Trending Topics for Your Thesis: What's Hot in 2024 How to Deal with a Total Lack of Motivation, Stress, and Anxiety When Finishing Your Master's Thesis Mastering the First Step: How to Start Your Thesis with Confidence Thesis Action Plan Blog Articles Affiliate Program Terms and Conditions Payment and Shipping Terms Privacy Policy Return Policy © 2024 Research Rebels, All rights reserved. Your cart is currently empty. 318 IMAGES What is Hypothesis? Functions- Characteristics-types-Criteria Good Hypothesis PPT Hypothesis Maker 5 Rules for Creating a Good Research Hypothesis Criteria or Characteristics of Good Hypothesis VIDEO 9.1 Video 1 Stating a Hypothesis for Significance Testing Criteria or Characteristics of Good Hypothesis The Good Genes Hypothesis Concept of hypothesis, sources of hypothesis, characteristics of a good hypothesis and types Selecting the Appropriate Hypothesis Test [FIL] Hypothesis COMMENTS What Makes a Good Hypothesis? Essential Criteria and Examples A good hypothesis should be clear and precise, avoiding vague language and ambiguity. It must be testable and falsifiable, meaning it can be supported or refuted through experimentation. Grounding in existing knowledge is crucial; a hypothesis should be based on prior research or established theories. How to Write a Strong Hypothesis How to Write a Strong Hypothesis | Steps & Examples What Makes a Good Hypothesis? Key Elements and Examples Conciseness and Clarity. A good hypothesis is brief and to the point. It should clearly state the expected outcome without unnecessary words. For example, instead of saying, "If we give plants more sunlight, they might grow taller," you could say, "Plants grow taller with increased sunlight." This makes the hypothesis easier to test and understand. What Makes a Good Hypothesis? Key Elements and Examples The key elements of a good hypothesis include clarity, testability, and a clear cause-and-effect relationship. By adhering to these principles, researchers can formulate hypotheses that are not only robust but also capable of withstanding rigorous scientific scrutiny. As demonstrated through various examples, a good hypothesis not only predicts ... Formulating Hypotheses for Different Study Designs Formulating Hypotheses for Different Study Designs. Generating a testable working hypothesis is the first step towards conducting original research. Such research may prove or disprove the proposed hypothesis. Case reports, case series, online surveys and other observational studies, clinical trials, and narrative reviews help to generate ... Step-by-Step Guide: How to Craft a Strong Research Hypothesis What is and How to Write a Good Hypothesis in Research? How to Write a Strong Hypothesis How to Write a Strong Hypothesis | Guide & Examples - Scribbr How to Write a Hypothesis in 6 Steps, With Examples How to Write a Hypothesis in 6 Steps, With Examples Research Hypothesis: Definition, Types, Examples and Quick Tips Research Hypothesis: Definition, Types, Examples and ... How to Write a Hypothesis w/ Strong Examples How to Write a Good Hypothesis. Writing a good hypothesis is definitely a good skill to have in scientific research. But it is also one that you can definitely learn with some practice if you don't already have it. Just keep in mind that the hypothesis is what sets the stage for the entire investigation. It guides the methods and analysis. What is a Research Hypothesis: How to Write it, Types, and Examples What is a research hypothesis: How to write it, types, and ... How to Formulate a Hypothesis: Example and Explanation Complex Hypothesis Examples. A complex hypothesis involves more than two variables. An example could be, "If students sleep for at least 8 hours and eat a healthy breakfast, then their test scores and overall well-being will improve." This type of hypothesis examines multiple factors and their combined effects. How to Write a Research Hypothesis: Good & Bad Examples Another example for a directional one-tailed alternative hypothesis would be that. H1: Attending private classes before important exams has a positive effect on performance. Your null hypothesis would then be that. H0: Attending private classes before important exams has no/a negative effect on performance. 5 Characteristics of a Good Hypothesis: A Guide for Researchers By adhering to these criteria, a good hypothesis statement guides research efforts effectively. What Is Not a Characteristic of a Good Hypothesis. A characteristic that does not align with a good hypothesis is subjectivity. A hypothesis should be objective, based on empirical observations or existing theories, and free from personal bias. Writing Your Dissertation Hypothesis: A Comprehensive Guide for Steps to Writing Your Hypothesis. 1. Identify Your Research Question. Your research question is the starting point for developing your hypothesis. A good research question should be clear, focused, and researchable. It often stems from a literature review where gaps in existing research are identified. What Are the Elements of a Good Hypothesis? A hypothesis is an educated guess or prediction of what will happen. In science, a hypothesis proposes a relationship between factors called variables. A good hypothesis relates an independent variable and a dependent variable. The effect on the dependent variable depends on or is determined by what happens when you change the independent variable. How to Write a Hypothesis? Types and Examples Characteristics of a hypothesis. So, what makes a good hypothesis? Here are some important characteristics of a hypothesis. 8,9 . Testable: You must be able to test the hypothesis using scientific methods to either accept or reject the prediction. Falsifiable: It should be possible to collect data that reject rather than support the hypothesis. What Makes a Good Hypothesis: Key Elements and Tips A well-crafted hypothesis not only guides your research design but also ensures that your study is focused and testable. In this article, we will explore the key elements that make a good hypothesis and provide practical tips for developing one. Key Takeaways. A good hypothesis should be clear, precise, and testable. How to Write a Strong Hypothesis in 6 Simple Steps Learning how to write a hypothesis comes down to knowledge and strategy. So where do you start? Learn how to make your hypothesis strong step-by-step here. A Strong Hypothesis Good Hypothesis : Poor Hypothesis: When there is less oxygen in the water, rainbow trout suffer more lice. Kristin says: "This hypothesis is good because it is testable, simple, written as a statement, and establishes the participants (trout), variables (oxygen in water, and numbers of lice), and predicts effect (as oxygen levels go down, the numbers of lice go up)." Hypothesis: Meaning, Criteria for Formulation and it's Types There exist two criteria for formulation of a good hypothesis. First, it is a statement about the relations between variables. Secondly it carries clear implications for testing the stated relations. Thus, these couple of criteria imply that the hypotheses comprise two or more variables which are measurable or potentially measurable and that ... What Makes a Good Hypothesis? Key Elements to Consider In conclusion, crafting a good hypothesis is a fundamental step in the scientific research process. A well-formulated hypothesis not only provides a clear direction for research but also ensures that the study is structured and focused. Key elements such as clarity, testability, and grounding in existing literature are essential for a robust ... NSF 24-582: NSF Small Business Innovation Research / Small Business NSF 24-582: NSF Small Business Innovation Research ... What Makes a Good Hypothesis: Key Elements and Tips A good hypothesis can be clearly refuted or supported by an experiment. To achieve this, you must ensure that your hypothesis is specific and measurable. This section will guide you through the essential steps to develop a robust hypothesis, provide examples of testable hypotheses , and highlight common pitfalls to avoid. essay homework paper phd project resume review study thesis