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What Is Accountability in Nursing?
In American Nurse Today, Marcia M. Rachel explains that accountability in nursing must include obligation, willingness, intent, ownership and commitment as essential components. She states that only a combination of these elements create accountability, which creates one side of a coin along with responsibility.
Dr. Rachel explains obligation as a duty that comes with consequences. Willingness entails accepting by choice and without reluctance. She describes intent as the purpose that accompanies the plan. Ownership means having power or control over something and commitment encompasses a feeling of being emotionally compelled. Dr. Rachel further explains the difference between accountability and responsibility by defining responsibility as a requirement for the job performance. She states that responsibility encompasses the expectation of accountability, which means someone holds nurses answerable for the outcomes of their actions. Dr. Rachel acknowledges that clarity, commitment and consequences must also exist to create accountability. Clarity means the nurse has clear and specific goals and expectations. Commitment means the nurse stays with the task in order to reach an objective. Nurses must also design appropriate consequences as well as face consequences in such a way increases responsibilities and holds nurses accountable. Dr. Rachel claims accountability runs through the entire nursing practice in all settings and at all levels as an energizing force throughout an organization.
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Step 1: Get a Reality Check
Nursing is a highly respected profession, but it isn’t an easy path to take. If you’re looking for a quick way to get into a job, nursing probably isn’t right for you. Most nurses need at least two years of formal education before they’re qualified, and that education usually involves a lot of highly technical scientific topics. You also should consider whether the job is right for your personality. Nurses typically need to be patient with difficult people, calm in a crisis and willing to work long hours.
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There are several different nursing profession roles, ranging from certified nursing assistants (CNAs) (who require the least education) to highly specialized nurse practitioners or registered nurses (RNs) (who often have formal degrees and may even obtain graduate-level educations in their fields). Beyond simply choosing what level of nursing education is right for you, it might also be a good idea to consider what area of nursing is most appealing (whether it’s assisting in surgery or working in a pediatrician’s office) before you pursue education.
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Hypothesis Types and Research
Dennis F. Polit. Nursing Research: Generating and Assessing Evidence for Nursing Practice, 9th edition. New Delhi: Lippincott Williams and Wilkins; 2012, 58–93p.
Nursing Research society of India, Nursing research and statistics, 1st edition. India: Pearson Publication; 2013, 48–51p.
Polit DF, Hungler BP. Nursing Research Principles and Methods. Philadelphia: Lippincott; 1999.
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- 1. Formulating hypothesis Maria Carmela L. Domocmat, MSN, RN Assistant Professor, Northern Luzon Adventist College
- 2. Hypothesis is your prediction of the relationship you expect to find. A tentative prediction about the relationship between two or more variables. 10/2/2014 Maria Carmela L. Domocmat, RN, MSN
- 3. Hypothesis Translates a research question into a prediction of expected outcomes. research question identifies the variables/concepts under investigation and asks how the concepts might be related hypothesis is the predicted answer.
- 4. Example Does history of sexual abuse in childhood affect the development of irritable bowel syndrome in women? Hypothesis Women who were sexually abused in childhood have a higher incidence of irritable bowel syndrome than women who were not.
- 5. What is the importance of hypothesis The use of hypotheses in quantitative studies tends to induce critical thinking and to facilitate understanding and interpretation of the data.
- 6. Characteristics 1.A good hypothesis is researchable 2.Should be stated in declarative form 3.Should state, in definite terms, the relationship between variables 4.Should be testable 5.Should follow the findings of previous studies 6.Should be related to a body of theory
- 7. Hypothesis and theory Hypotheses sometimes follow directly from a theoretical framework Remember: the validity of a theory is never examined directly. It is through hypothesis testing that the worth of a theory can be evaluated.
- 8. Hypothesis and theory Ex: theory of reinforcement behavior that is positively reinforced (rewarded) tends to be learned or repeated. The theory itself is too abstract to be put to an empirical test, but if the theory is valid, it should be possible to make predictions about certain kinds of behavior.
- 9. Example Elderly patients who are praised (reinforced) by nursing personnel for self-feeding require less assistance in feeding than patients who are not praised. Pediatric patients who are given a reward (e.g.,a balloon or permission to watch television)when they cooperate during nursing procedures tend to be more obliging during those procedures than nonrewarded peers.
- 10. How should the hypothesis be stated? Hypothesis can be stated as Directional or nondirectional Simple or complex Research or Null
- 11. Simple hypothesis Statement of causal relationship one independent variable and one dependent variable. Complex hypothesis Statement of causal or associative relationship between two or more independent variables and/or two or more dependent variables.
- 12. Directional hypothesis Specifies not only the existence but also the expected direction of the relationship between the dependent and the independent variables. Nondirectional hypothesis Does not specify the direction of the relationship between the dependent and the independent variables.
- 13. Which of the following is directional and nondirectional hypothesis? 1. Older patients are more at risk of experiencing a fall than younger patients. 2. There is a relationship between the age of a patient and the risk of falling. 3. The older the patient, the greater the risk that she or he will fall. 4. Older patients differ from younger ones with respect to their risk of falling. 5. Younger patients tend to be less at risk of a fall than older patients. 6. The risk of falling increases with the age of the patient.
- 14. Research hypothesis also referred to as substantive, declarative, or scientific hypotheses Are statements of expected relationships between variables. Null hypothesis or statistical hypotheses state that there is no relationship between the independent and dependent variables.
- 15. Research or Null hypothesis? “Patients’ age is unrelated to their risk of falling” “Older patients are just as likely as younger patients to fall.”
- 16. References Galero-tejero, E. (2011). A simplified approach to thesis and dissertation writing. Quezon City: National Bookstore. Talbot, L.A. (1995). Principles and practice of nursing research. USA: Mosby Year Book,Inc. Polit D.E. & Beck, CT. (2008). Nursing research: Generating and assessing evidence for nursing practice [8th ed]. Philadelphia: Wolster Kluwer, Lippincott Williams & Wilkins. Nieswiadomy, R.M. (2008). Foundations of Nursing Research [5th ed]. Singapore: Pearson Education South Asia Pte Ltd.
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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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Table of Contents
What is Hypothesis?
- Hypothesis is a logical prediction of certain occurrences without the support of empirical confirmation or evidence.
- In scientific terms, it is a tentative theory or testable statement about the relationship between two or more variables i.e. independent and dependent variable.
Different Types of Hypothesis:
1. Simple Hypothesis:
- A Simple hypothesis is also known as composite hypothesis.
- In simple hypothesis all parameters of the distribution are specified.
- It predicts relationship between two variables i.e. the dependent and the independent variable
2. Complex Hypothesis:
- A Complex hypothesis examines relationship between two or more independent variables and two or more dependent variables.
3. Working or Research Hypothesis:
- A research hypothesis is a specific, clear prediction about the possible outcome of a scientific research study based on specific factors of the population.
4. Null Hypothesis:
- A null hypothesis is a general statement which states no relationship between two variables or two phenomena. It is usually denoted by H 0 .
5. Alternative Hypothesis:
- An alternative hypothesis is a statement which states some statistical significance between two phenomena. It is usually denoted by H 1 or H A .
6. Logical Hypothesis:
- A logical hypothesis is a planned explanation holding limited evidence.
7. Statistical Hypothesis:
- A statistical hypothesis, sometimes called confirmatory data analysis, is an assumption about a population parameter.
Although there are different types of hypothesis, the most commonly and used hypothesis are Null hypothesis and alternate hypothesis . So, what is the difference between null hypothesis and alternate hypothesis? Let’s have a look:
Major Differences Between Null Hypothesis and Alternative Hypothesis:
Importance of hypothesis:.
- It ensures the entire research methodologies are scientific and valid.
- It helps to assume the probability of research failure and progress.
- It helps to provide link to the underlying theory and specific research question.
- It helps in data analysis and measure the validity and reliability of the research.
- It provides a basis or evidence to prove the validity of the research.
- It helps to describe research study in concrete terms rather than theoretical terms.
Characteristics of Good Hypothesis:
- Should be simple.
- Should be specific.
- Should be stated in advance.
References and For More Information:
https://ocw.jhsph.edu/courses/StatisticalReasoning1/PDFs/2009/BiostatisticsLecture4.pdf
https://keydifferences.com/difference-between-type-i-and-type-ii-errors.html
https://www.khanacademy.org/math/ap-statistics/tests-significance-ap/error-probabilities-power/a/consequences-errors-significance
https://stattrek.com/hypothesis-test/hypothesis-testing.aspx
http://davidmlane.com/hyperstat/A2917.html
https://study.com/academy/lesson/what-is-a-hypothesis-definition-lesson-quiz.html
https://keydifferences.com/difference-between-null-and-alternative-hypothesis.html
https://blog.minitab.com/blog/adventures-in-statistics-2/understanding-hypothesis-tests-why-we-need-to-use-hypothesis-tests-in-statistics
- Characteristics of Good Hypothesis
- complex hypothesis
- example of alternative hypothesis
- example of null hypothesis
- how is null hypothesis different to alternative hypothesis
- Importance of Hypothesis
- null hypothesis vs alternate hypothesis
- simple hypothesis
- Types of Hypotheses
- what is alternate hypothesis
- what is alternative hypothesis
- what is hypothesis?
- what is logical hypothesis
- what is null hypothesis
- what is research hypothesis
- what is statistical hypothesis
- why is hypothesis necessary
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- Definition/Introduction
Medical providers often rely on evidence-based medicine to guide decision-making in practice. Often a research hypothesis is tested with results provided, typically with p values, confidence intervals, or both. Additionally, statistical or research significance is estimated or determined by the investigators. Unfortunately, healthcare providers may have different comfort levels in interpreting these findings, which may affect the adequate application of the data.
- Issues of Concern
Without a foundational understanding of hypothesis testing, p values, confidence intervals, and the difference between statistical and clinical significance, it may affect healthcare providers' ability to make clinical decisions without relying purely on the research investigators deemed level of significance. Therefore, an overview of these concepts is provided to allow medical professionals to use their expertise to determine if results are reported sufficiently and if the study outcomes are clinically appropriate to be applied in healthcare practice.
Hypothesis Testing
Investigators conducting studies need research questions and hypotheses to guide analyses. Starting with broad research questions (RQs), investigators then identify a gap in current clinical practice or research. Any research problem or statement is grounded in a better understanding of relationships between two or more variables. For this article, we will use the following research question example:
Research Question: Is Drug 23 an effective treatment for Disease A?
Research questions do not directly imply specific guesses or predictions; we must formulate research hypotheses. A hypothesis is a predetermined declaration regarding the research question in which the investigator(s) makes a precise, educated guess about a study outcome. This is sometimes called the alternative hypothesis and ultimately allows the researcher to take a stance based on experience or insight from medical literature. An example of a hypothesis is below.
Research Hypothesis: Drug 23 will significantly reduce symptoms associated with Disease A compared to Drug 22.
The null hypothesis states that there is no statistical difference between groups based on the stated research hypothesis.
Researchers should be aware of journal recommendations when considering how to report p values, and manuscripts should remain internally consistent.
Regarding p values, as the number of individuals enrolled in a study (the sample size) increases, the likelihood of finding a statistically significant effect increases. With very large sample sizes, the p-value can be very low significant differences in the reduction of symptoms for Disease A between Drug 23 and Drug 22. The null hypothesis is deemed true until a study presents significant data to support rejecting the null hypothesis. Based on the results, the investigators will either reject the null hypothesis (if they found significant differences or associations) or fail to reject the null hypothesis (they could not provide proof that there were significant differences or associations).
To test a hypothesis, researchers obtain data on a representative sample to determine whether to reject or fail to reject a null hypothesis. In most research studies, it is not feasible to obtain data for an entire population. Using a sampling procedure allows for statistical inference, though this involves a certain possibility of error. [1] When determining whether to reject or fail to reject the null hypothesis, mistakes can be made: Type I and Type II errors. Though it is impossible to ensure that these errors have not occurred, researchers should limit the possibilities of these faults. [2]
Significance
Significance is a term to describe the substantive importance of medical research. Statistical significance is the likelihood of results due to chance. [3] Healthcare providers should always delineate statistical significance from clinical significance, a common error when reviewing biomedical research. [4] When conceptualizing findings reported as either significant or not significant, healthcare providers should not simply accept researchers' results or conclusions without considering the clinical significance. Healthcare professionals should consider the clinical importance of findings and understand both p values and confidence intervals so they do not have to rely on the researchers to determine the level of significance. [5] One criterion often used to determine statistical significance is the utilization of p values.
P values are used in research to determine whether the sample estimate is significantly different from a hypothesized value. The p-value is the probability that the observed effect within the study would have occurred by chance if, in reality, there was no true effect. Conventionally, data yielding a p<0.05 or p<0.01 is considered statistically significant. While some have debated that the 0.05 level should be lowered, it is still universally practiced. [6] Hypothesis testing allows us to determine the size of the effect.
An example of findings reported with p values are below:
Statement: Drug 23 reduced patients' symptoms compared to Drug 22. Patients who received Drug 23 (n=100) were 2.1 times less likely than patients who received Drug 22 (n = 100) to experience symptoms of Disease A, p<0.05.
Statement:Individuals who were prescribed Drug 23 experienced fewer symptoms (M = 1.3, SD = 0.7) compared to individuals who were prescribed Drug 22 (M = 5.3, SD = 1.9). This finding was statistically significant, p= 0.02.
For either statement, if the threshold had been set at 0.05, the null hypothesis (that there was no relationship) should be rejected, and we should conclude significant differences. Noticeably, as can be seen in the two statements above, some researchers will report findings with < or > and others will provide an exact p-value (0.000001) but never zero [6] . When examining research, readers should understand how p values are reported. The best practice is to report all p values for all variables within a study design, rather than only providing p values for variables with significant findings. [7] The inclusion of all p values provides evidence for study validity and limits suspicion for selective reporting/data mining.
While researchers have historically used p values, experts who find p values problematic encourage the use of confidence intervals. [8] . P-values alone do not allow us to understand the size or the extent of the differences or associations. [3] In March 2016, the American Statistical Association (ASA) released a statement on p values, noting that scientific decision-making and conclusions should not be based on a fixed p-value threshold (e.g., 0.05). They recommend focusing on the significance of results in the context of study design, quality of measurements, and validity of data. Ultimately, the ASA statement noted that in isolation, a p-value does not provide strong evidence. [9]
When conceptualizing clinical work, healthcare professionals should consider p values with a concurrent appraisal study design validity. For example, a p-value from a double-blinded randomized clinical trial (designed to minimize bias) should be weighted higher than one from a retrospective observational study [7] . The p-value debate has smoldered since the 1950s [10] , and replacement with confidence intervals has been suggested since the 1980s. [11]
Confidence Intervals
A confidence interval provides a range of values within given confidence (e.g., 95%), including the accurate value of the statistical constraint within a targeted population. [12] Most research uses a 95% CI, but investigators can set any level (e.g., 90% CI, 99% CI). [13] A CI provides a range with the lower bound and upper bound limits of a difference or association that would be plausible for a population. [14] Therefore, a CI of 95% indicates that if a study were to be carried out 100 times, the range would contain the true value in 95, [15] confidence intervals provide more evidence regarding the precision of an estimate compared to p-values. [6]
In consideration of the similar research example provided above, one could make the following statement with 95% CI:
Statement: Individuals who were prescribed Drug 23 had no symptoms after three days, which was significantly faster than those prescribed Drug 22; there was a mean difference between the two groups of days to the recovery of 4.2 days (95% CI: 1.9 – 7.8).
It is important to note that the width of the CI is affected by the standard error and the sample size; reducing a study sample number will result in less precision of the CI (increase the width). [14] A larger width indicates a smaller sample size or a larger variability. [16] A researcher would want to increase the precision of the CI. For example, a 95% CI of 1.43 – 1.47 is much more precise than the one provided in the example above. In research and clinical practice, CIs provide valuable information on whether the interval includes or excludes any clinically significant values. [14]
Null values are sometimes used for differences with CI (zero for differential comparisons and 1 for ratios). However, CIs provide more information than that. [15] Consider this example: A hospital implements a new protocol that reduced wait time for patients in the emergency department by an average of 25 minutes (95% CI: -2.5 – 41 minutes). Because the range crosses zero, implementing this protocol in different populations could result in longer wait times; however, the range is much higher on the positive side. Thus, while the p-value used to detect statistical significance for this may result in "not significant" findings, individuals should examine this range, consider the study design, and weigh whether or not it is still worth piloting in their workplace.
Similarly to p-values, 95% CIs cannot control for researchers' errors (e.g., study bias or improper data analysis). [14] In consideration of whether to report p-values or CIs, researchers should examine journal preferences. When in doubt, reporting both may be beneficial. [13] An example is below:
Reporting both: Individuals who were prescribed Drug 23 had no symptoms after three days, which was significantly faster than those prescribed Drug 22, p = 0.009. There was a mean difference between the two groups of days to the recovery of 4.2 days (95% CI: 1.9 – 7.8).
- Clinical Significance
Recall that clinical significance and statistical significance are two different concepts. Healthcare providers should remember that a study with statistically significant differences and large sample size may be of no interest to clinicians, whereas a study with smaller sample size and statistically non-significant results could impact clinical practice. [14] Additionally, as previously mentioned, a non-significant finding may reflect the study design itself rather than relationships between variables.
Healthcare providers using evidence-based medicine to inform practice should use clinical judgment to determine the practical importance of studies through careful evaluation of the design, sample size, power, likelihood of type I and type II errors, data analysis, and reporting of statistical findings (p values, 95% CI or both). [4] Interestingly, some experts have called for "statistically significant" or "not significant" to be excluded from work as statistical significance never has and will never be equivalent to clinical significance. [17]
The decision on what is clinically significant can be challenging, depending on the providers' experience and especially the severity of the disease. Providers should use their knowledge and experiences to determine the meaningfulness of study results and make inferences based not only on significant or insignificant results by researchers but through their understanding of study limitations and practical implications.
- Nursing, Allied Health, and Interprofessional Team Interventions
All physicians, nurses, pharmacists, and other healthcare professionals should strive to understand the concepts in this chapter. These individuals should maintain the ability to review and incorporate new literature for evidence-based and safe care.
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This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.
- Cite this Page Shreffler J, Huecker MR. Hypothesis Testing, P Values, Confidence Intervals, and Significance. [Updated 2022 Mar 18]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2022 Jan-.
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Trends in hypothesis testing and related variables in nursing research: a retrospective exploratory study
Affiliation.
- 1 Northern Illinois University, DeKalb, IL, USA. [email protected]
- PMID: 21560925
- DOI: 10.7748/nr2011.04.18.3.38.c8462
Aim: To compare the inclusion and the influences of selected variables on hypothesis testing during the 1980s and 1990s.
Background: In spite of the emphasis on conducting inquiry consistent with the tenets of logical positivism, there have been no studies investigating the frequency and patterns of hypothesis testing in nursing research
Data sources: The sample was obtained from the journal Nursing Research which was the research journal with the highest circulation during the study period under study. All quantitative studies published during the two decades including briefs and historical studies were included in the analyses
Review methods: A retrospective design was used to select the sample. Five years from the 1980s and 1990s each were randomly selected from the journal, Nursing Research. Of the 582 studies, 517 met inclusion criteria.
Discussion: Findings suggest that there has been a decline in the use of hypothesis testing in the last decades of the 20th century. Further research is needed to identify the factors that influence the conduction of research with hypothesis testing.
Conclusion: Hypothesis testing in nursing research showed a steady decline from the 1980s to 1990s. Research purposes of explanation, and prediction/ control increased the likelihood of hypothesis testing.
Implications for practice: Hypothesis testing strengthens the quality of the quantitative studies, increases the generality of findings and provides dependable knowledge. This is particularly true for quantitative studies that aim to explore, explain and predict/control phenomena and/or test theories. The findings also have implications for doctoral programmes, research preparation of nurse-investigators, and theory testing.
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- Research Hypothesis
- By: Sema A. Kalaian & Rafa M. Kasim
- In: Encyclopedia of Survey Research Methods
- Chapter DOI: https:// doi. org/10.4135/9781412963947
- Subject: Anthropology , Business and Management , Criminology and Criminal Justice , Communication and Media Studies , Economics , Education , Geography , Health , Marketing , Nursing , Political Science and International Relations , Psychology , Social Policy and Public Policy , Social Work , Sociology
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A research hypothesis is a specific, clear, and testable proposition or predictive statement about the possible outcome of a scientific research study based on a particular property of a population, such as presumed differences between groups on a particular variable or relationships between variables. Specifying the research hypotheses is one of the most important steps in planning a scientific quantitative research study. A quantitative researcher usually states an a priori expectation about the results of the study in one or more research hypotheses before conducting the study, because the design of the research study and the planned research design often is determined by the stated hypotheses. Thus, one of the advantages of stating a research hypothesis is that it requires the researcher to fully think ...
- Research Design
- Research Management
- Beneficence
- Cell Suppression
- Certificate of Confidentiality
- Common Rule
- Confidentiality
- Consent Form
- Disclosure Limitation
- Ethical Principles
- Falsification
- Informed Consent
- Institutional Review Board (IRB)
- Minimal Risk
- Perturbation Methods
- Protection of Human Subjects
- Respondent Debriefing
- Survey Ethics
- Voluntary Participation
- Conversational Interviewing
- Dependent Interviewing
- Interviewer Effects
- Interviewer Neutrality
- Interviewer Variance
- Interviewer-Related Error
- Nondirective Probing
- Standardized Survey Interviewing
- Verbatim Responses
- Mode Effects
- Mode-Related Error
- Aided Recall
- Aided Recognition
- Attitude Measurement
- Attitude Strength
- Aural Communication
- Balanced Question
- Behavioral Question
- Bipolar Scale
- Bogus Question
- Check All That Apply
- Closed-Ended Question
- Cognitive Interviewing
- Construct Validity
- Context Effect
- Contingency Question
- Demographic Measure
- Dependent Variable
- Don't Knows (DKs)
- Double Negative
- Double-Barreled Question
- Drop-Down Menus
- Event History Calendar
- Factorial Survey Method (Rossi's Method)
- Feeling Thermometer
- Forced Choice
- Gestalt Psychology
- Graphical Language
- Guttman Scale
- Item Order Randomization
- Item Response Theory
- Knowledge Question
- Language Translations
- Likert Scale
- List-Experiment Technique
- Mail Questionnaire
- Mutually Exclusive
- Open-Ended Question
- Paired Comparison Technique
- Precoded Question
- Psychographic Measure
- Question Order Effects
- Question Stem
- Questionnaire
- Questionnaire Design
- Questionnaire Length
- Questionnaire-Related Error
- Radio Buttons
- Random Order
- Random Start
- Randomized Response
- Reference Period
- Response Alternatives
- Response Order Effects
- Self-Administered Questionnaire
- Self-Reported Measure
- Semantic Differential Technique
- Sensitive Topics
- Step-Ladder Question
- Unaided Recall
- Unbalanced Question
- Unfolding Question
- Vignette Question
- Visual Communication
- Acquiescence Response Bias
- Behavior Coding
- Cognitive Aspects of Survey Methodology (CASM)
- Comprehension
- Extreme Response Style
- Key Informant
- Misreporting
- Nonattitude
- Nondifferentiation
- Overreporting
- Panel Conditioning
- Panel Fatigue
- Positivity Bias
- Primacy Effect
- Recency Effect
- Record Check
- Respondent Burden
- Respondent Fatigue
- Respondent-Related Error
- Response Bias
- Response Latency
- Reverse Record Check
- Satisficing
- Social Desirability
- Telescoping
- Underreporting
- Coder Variance
- Content Analysis
- Field Coding
- Focus Group
- Intercoder Reliability
- Interrater Reliability
- Interval Measure
- Level of Measurement
- Litigation Surveys
- Measurement Error
- Nominal Measure
- Ordinal Measure
- Ratio Measure
- Reliability
- Replication
- Missing Data
- Nonresponse
- Completed Interview
- Completion Rate
- Contact Rate
- Contactability
- Cooperation Rate
- Final Dispositions
- Hang-Up During Introduction (HUDI)
- Household Refusal
- Language Barrier
- Noncontact Rate
- Noncontacts
- Noncooperation Rate
- Nonresidential
- Nonresponse Rates
- Number Changed
- Out of Order
- Out of Sample
- Partial Completion
- Refusal Rate
- Respondent Refusal
- Response Rates
- Standard Definitions
- Temporary Dispositions
- Unable to Participate
- Unavailable Respondent
- Unknown Eligibility
- Unlisted Household
- Advance Contact
- Contingent Incentives
- Controlled Access
- Cooperation
- Differential Attrition
- Differential Nonresponse
- Economic Exchange Theory
- Fallback Statements
- Ignorable Nonresponse
- Introduction
- Leverage-Saliency Theory
- Noncontingent Incentives
- Nonignorable Nonresponse
- Nonresponse Bias
- Nonresponse Error
- Refusal Avoidance
- Refusal Avoidance Training (RAT)
- Refusal Conversion
- Refusal Report Form (RRF)
- Response Propensity
- Social Exchange Theory
- Social Isolation
- Total Design Method (TDM)
- Unit Nonresponse
- Advance Letter
- Bilingual Interviewing
- Data Management
- Dispositions
- Field Director
- Field Period
- Mode of Data Collection
- Multi-Level Integrated Database Approach (MIDA)
- Paper-and-Pencil Interviewing (PAPI)
- Quality Control
- Reinterview
- Sample Management
- Sample Replicates
- Survey Costs
- Technology-Based Training
- Verification
- Video Computer-Assisted Self-Interviewing (VCASI)
- Audio Computer-Assisted Self-Interviewing (ACASI)
- Case-Control Study
- Computer-Assisted Personal Interviewing (CAPI)
- Computer-Assisted Self-Interviewing (CASI)
- Computerized Self-Administered Questionnaires (CSAQ)
- Control Sheet
- Face-to-Face Interviewing
- Residence Rules
- Interviewer
- Interviewer Characteristics
- Interviewer Debriefing
- Interviewer Monitoring
- Interviewer Monitoring Form (IMF)
- Interviewer Productivity
- Interviewer Training
- Interviewing
- Nonverbal Behavior
- Respondent-Interviewer Rapport
- Role Playing
- Training Packet
- Usability Testing
- Cover Letter
- Disk by Mail
- Mail Survey
- Access Lines
- Answering Machine Messages
- Call Forwarding
- Call Screening
- Calling Rules
- Computer-Assisted Telephone Interviewing (CATI)
- Do-Not-Call (DNC) Registries
- Federal Communications Commission (FCC) Regulations
- Federal Trade Commission (FTC) Regulations
- Inbound Calling
- Interactive Voice Response (IVR)
- Listed Number
- Matched Number
- Nontelephone Household
- Number Portability
- Number Verification
- Outbound Calling
- Predictive Dialing
- Privacy Manager
- Research Call Center
- Reverse Directory
- Suffix Banks
- Supervisor-to-interviewer Ratio
- Telephone Consumer Protection Act 1991
- Telephone Penetration
- Telephone Surveys
- Touchtone Data Entry
- Unmatched Number
- Unpublished Number
- Videophone Interviewing
- Voice over Internet Protocol (VoIP) and the Virtual Computer-Assisted Telephone Interview (CATI) Facility
- ABC News/Washington Post Poll
- Approval Ratings
- Bandwagon and Underdog Effects
- Call-in Polls
- Computerized-Response Audience Polling (CRAP)
- Convention Bounce
- Deliberative Poll
- Election Night Projections
- Election Polls
- Favorability Ratings
- Horse Race Journalism
- Leaning Voters
- Likely Voter
- Media Polls
- Methods Box
- National Council on Public Polls (NCPP)
- National Election Pool (NEP)
- National Election Studies (NES)
- New York Times/CBS News Poll
- Polling Review Board (PRB)
- Precision Journalism
- Pre-Election Polls
- Pre-Primary Polls
- Prior Restraint
- Probable Electorate
- Pseudo-Polls
- Rolling Averages
- Sample Precinct
- Self-Selected Listener Opinion Poll (SLOP)
- Straw Polls
- Subgroup Analysis
- Tracking Polls
- Trend Analysis
- Trial Heat Question
- Undecided Voters
- Agenda Setting
- Consumer Sentiment Index
- Issue Definition (Framing)
- Knowledge Gap
- Mass Beliefs
- Opinion Norms
- Opinion Question
- Perception Question
- Political Knowledge
- Public Opinion
- Public Opinion Research
- Quality of Life Indicators
- Question Wording as Discourse Indicators
- Social Capital
- Spiral of Silence
- Third-Person Effect
- Topic Saliency
- Trust in Government
- Adaptive Sampling
- Add-a-Digit Sampling
- Address-Based Sampling
- Area Probability Sample
- Capture-Recapture Sampling
- Cell Phone Only Household
- Cell Phone Sampling
- Cluster Sample
- Complex Sample Surveys
- Convenience Sampling
- Coverage Error
- Cross-Sectional Survey Design
- Cutoff Sampling
- Designated Respondent
- Directory Sampling
- Disproportionate Allocation to Strata
- Dual-Frame Sampling
- Duplication
- Eligibility
- Email Survey
- EPSEM Sample
- Equal Probability of Selection
- Error of Nonobservation
- Errors of Commission
- Errors of Omission
- Establishment Survey
- External Validity
- Field Survey
- Finite Population
- Geographic Screening
- Hagan and Collier Selection Method
- Half-Open Interval
- Internet Pop-Up Polls
- Internet Surveys
- Interpenetrated Design
- Inverse Sampling
- Kish Selection Method
- Last-Birthday Selection
- List Sampling
- List-Assisted Sampling
- Log-in Polls
- Longitudinal Studies
- Mall Intercept Survey
- Mitofsky-Waksberg Sampling
- Multi-Mode Surveys
- Multiple-Frame Sampling
- Multiplicity Sampling
- Multi-Stage Sample
- Network Sampling
- Neyman Allocation
- Noncoverage
- Nonprobability Sampling
- Nonsampling Error
- Optimal Allocation
- Overcoverage
- Panel Survey
- Population of Inference
- Population of Interest
- Post-Stratification
- Primary Sampling Unit (PSU)
- Probability of Selection
- Probability Proportional to Size (PPS) Sampling
- Probability Sample
- Propensity Scores
- Propensity-Weighted Web Survey
- Proportional Allocation to Strata
- Proxy Respondent
- Purposive Sample
- Quota Sampling
- Random Sampling
- Random-Digit Dialing (RDD)
- Ranked-Set Sampling (RSS)
- Rare Populations
- Registration-Based Sampling (RBS)
- Repeated Cross-Sectional Design
- Replacement
- Representative Sample
- Respondent-Driven Sampling (RDS)
- Reverse Directory Sampling
- Rotating Panel Design
- Sample Design
- Sample Size
- Sampling Fraction
- Sampling Frame
- Sampling Interval
- Sampling Pool
- Sampling Without Replacement
- Self-Selected Sample
- Self-Selection Bias
- Sequential Sampling
- Simple Random Sample
- Small Area Estimation
- Snowball Sampling
- Stratified Sampling
- Superpopulation
- Systematic Sampling
- Target Population
- Telephone Households
- Troldahl-Carter-Bryant Respondent Selection Method
- Undercoverage
- Unit Coverage
- Unit of Observation
- Within-Unit Coverage
- Within-Unit Coverage Error
- Within-Unit Selection
- Zero-Number Banks
- American Association for Public Opinion Research (AAPOR)
- American Community Survey (ACS)
- American Statistical Association Section on Survey Research Methods (ASA-SRMS)
- Behavioral Risk Factor Surveillance System (BRFSS)
- Bureau of Labor Statistics (BLS)
- Cochran, W. G.
- Council for Marketing and Opinion Research (CMOR)
- Council of American Survey Research Organizations (CASRO)
- Crossley, Archibald
- Current Population Survey (CPS)
- Gallup Poll
- Gallup, George
- General Social Survey (GSS)
- Hansen, Morris
- Institute for Social Research (ISR)
- International Field Directors and Technologies Conference (IFD&TC)
- International Journal of Public Opinion Research (IJPOR)
- International Social Survey Programme (ISSP)
- Joint Program in Survey Methodology (JPSM)
- Journal of Official Statistics (JOS)
- Kish, Leslie
- National Health and Nutrition Examination Survey (NHANES)
- National Health Interview Survey (NHIS)
- National Household Education Surveys (NHES) Program
- National Opinion Research Center (NORC)
- Pew Research Center
- Public Opinion Quarterly (POQ)
- Roper Center for Public Opinion Research
- Roper, Elmo
- Sheatsley, Paul
- Statistics Canada
- Survey Methodology
- Survey Sponsor
- Telemarketing
- U.S. Bureau of the Census
- World Association for Public Opinion Research (WAPOR)
- Alpha, Significance Level of Test
- Alternative Hypothesis
- Analysis of Variance (ANOVA)
- Attenuation
- Auxiliary Variable
- Balanced Repeated Replication (BRR)
- Bootstrapping
- Composite Estimation
- Confidence Interval
- Confidence Level
- Contingency Table
- Control Group
- Correlation
- Cronbach's Alpha
- Cross-Sectional Data
- Data Swapping
- Design Effects (deff)
- Design-Based Estimation
- Ecological Fallacy
- Effective Sample Size
- Experimental Design
- Factorial Design
- Finite Population Correction (fpc) Factor
- Frequency Distribution
- Hot-Deck Imputation
- Independent Variable
- Interaction Effect
- Internal Validity
- Interval Estimate
- Intracluster Homogeneity
- Jackknife Variance Estimation
- Level of Analysis
- Main Effect
- Margin of Error (MOE)
- Mean Square Error
- Model-Based Estimation
- Multiple Imputation
- Noncausal Covariation
- Null Hypothesis
- Panel Data Analysis
- Percentage Frequency Distribution
- Point Estimate
- Population Parameter
- Post-Survey Adjustments
- Probability
- Random Assignment
- Random Error
- Recoded Variable
- Regression Analysis
- Relative Frequency
- Replicate Methods for Variance Estimation
- Research Question
- Sampling Bias
- Sampling Error
- Sampling Variance
- Seam Effect
- Significance Level
- Solomon Four-Group Design
- Standard Error
- Standard Error of the Mean
- Statistical Package for the Social Sciences (SPSS)
- Statistical Power
- Systematic Error
- Taylor Series Linearization
- Test-Retest Reliability
- Total Survey Error (TSE)
- Type I Error
- Type II Error
- Unbiased Statistic
- Variance Estimation
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