examples of hypothesis testing and confidence intervals in nursing

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StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

StatPearls [Internet].

Hypothesis testing, p values, confidence intervals, and significance.

Jacob Shreffler ; Martin R. Huecker .

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Last Update: March 13, 2023 .

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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Disclosure: Jacob Shreffler declares no relevant financial relationships with ineligible companies.

Disclosure: Martin Huecker declares no relevant financial relationships with ineligible companies.

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 2023 Mar 13]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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8 Hypothesis Testing Examples in Real Life

Hypothesis testing refers to the systematic and scientific method of examining whether the hypothesis set by the researcher is valid or not. Hypothesis testing verifies that the findings of an experiment are valid and the particular results did not happen by chance. If the particular results have occurred by chance then that experiment can not be repeated and its findings won’t be reliable. For example, if you conduct a study that finds that a particular drug is responsible for the blood pressure problem in diabetic patients. But, when you repeat this experiment and it does not give the same results, no one would trust this experiment’s findings. Hence, hypothesis testing is a very crucial step to verify the experimental findings. The main criterion of hypothesis testing is to check whether the null hypothesis is rejected or retained. The null hypothesis assumes that there does not exist any relationship between the variables under investigation, while the alternate hypothesis confirms the association between the variables under investigation. If the null hypothesis is rejected, it means that alternative hypotheses (research hypothesis) are accepted, and if the null hypothesis is accepted, the alternate hypothesis is rejected automatically. In this article, we’ll learn about hypothesis testing and various real-life examples of hypothesis testing.

Understanding Hypothesis Testing

The hypothesis testing broadly involves the following steps,

Step 1 : Formulate the research hypothesis and the null hypothesis of the experiment.
Step 2: Set the characteristics of the comparison distribution.
Step3: Set the criterion for decision making, i.e., cut off sample score for the comparison to reject or retain the null hypothesis.
Step:4 Set the outcome of the sample on the comparison distribution.
Step 5: Make a decision whether to reject or retain the null hypothesis.

Let us understand these steps through the following example,

Suppose the researcher wants to examine whether the memorizing power of students improves after consuming caffeine or not. To examine this he conducts experiments, the experiment involves two groups say group A (experimental group) and group B (control group). Group A consumed the coffee before the memory test, while group B consumed the water before the memory test. The average normally distributed score of the people of the experimental group has a standard deviation of 4 and a mean of 19. On the basis of the score, the researcher can state that there is an association between the two variables, i.e., the memory power and the caffeine, but the researcher can not predict any particular direction, i.e., which out of the experimental group and the control group had performed better in the memory tests. Hence, the level of significance value, i.e., 5 per cent will help to draw the conclusion. Following is the stepwise hypothesis testing of this example,

Step 1: Formulating Null hypothesis and alternate hypothesis

There exist two sample populations, i.e., group A and group B.

Group A: People who consumed coffee before the experiment

Group B: People who consumed water before the experiment.

On the basis of this, the null hypothesis and the alternative hypothesis would be as follows.

Alternate Hypothesis: Group A will perform differently from Group B, i.e., there exists an association between the two variables.

Null Hypothesis: There will not be any difference between the performance of both groups, i.e., Group A and Group B both will perform similarly.

Step 2: Characteristics of the comparison distribution

The characteristics of the comparison distribution in this example are given below,

Population Mean = 19

Standard Deviation= 4, normally distributed.

Step 3: Cut off score

In this test the direction of effect is not stated, i.e., it is a two-tailed test. In the case of a two-tailed test, the cut off sample scores is equal to +1.96 and -1.99 at the 5 per cent level.

Step 4: Outcome of Sample Score

The sample score is then converted into the Z value. Using the appropriate method of conversion this value is turned out to be equal to 2.

Step 5: Decision Making

The Z score value of 2 is far more than the cut off Z value, ie., +1.96, hence the result is significant, ie., rejection of the null hypothesis, i.e., there exists an association between the memory power and the consumption of the coffee before the test.

Click here , to understand hypothesis testing in detail.

Hypothesis Testing Real Life Examples

Following are some real-life examples of hypothesis testing.

1. To Check the Manufacturing Processes

Hypothesis testing finds its application in the manufacturing processes such as in determining whether the implication of the new technique or process in the manufacturing plant caused the anomalies in the quality of the product or not. Let us suppose, that manufacturing plant X decides to verify that the particular method results in an increase in the defective products per quarter, say this number to be 200. Now, to verify this the researcher needs to calculate the mean of the number of defective products produced before the start and the end of the quarter.

Following is the representation of the Hypothesis testing of this example,

Null Hypothesis (Ho) : The average of the defective products produced is the same before and after the implementation of the new manufacturing method, i.e., μ after = μ before

Alternative Hypothesis (Ha) : The average number of defective products produced are different before and after the implementation of the new manufacturing method, i.e., μ after ≠ μ before

If the resultant p-value of the hypothesis testing comes lesser than the significant value, i.e., α = .05, then the null hypothesis is rejected and it can be concluded that the changes in the method of production lead to the rise in the number of defective products production per quarter.

2. To Plan the Marketing Strategies

Many businesses often use hypothesis testing to determine the impact of the newly implemented marketing techniques, campaigns or other tactics on the sales of the product. For example, the marketing department of the company assumed that if they spend more the digital advertisements it would lead to a rise in sales. To verify this assumption, the marketing department may raise the digital advertisement budget for a particular period, and then analyse the collected data at the end of that period. They have to perform hypothesis testing to verify their assumption. Here,

Null Hypothesis (Ho) : The average sales are the same before and after the rise in the digital advertisement budget, i.e., μafter = μbefore

Alternative Hypothesis (Ha) : The average sales increase after the rise in the digital advertisement budget, i.e., μafter > μbefore

If the P-value is smaller than the significant value (say .05), then the null hypothesis can be rejected by the marketing department, and they can conclude that the rise in the digital advertisement budget can result in a rise in the sales of the product.

3. In Clinical Trials

Many pharmacists and doctors use hypothesis testing for clinical trials. The impact of the new clinical methods, medicines or procedures on the condition of the patients is analysed through hypothesis testing. For example, a pharmacist believes that the new medicine is resulting in the rise of blood pressure in diabetic patients. To test this assumption, the researcher has to measure the blood pressure of the sample patients (patients under investigation) before and after the intake of the new medicine for nearly a particular period say one month. The following procedure of the hypothesis testing is then followed,

Null Hypothesis (H0) : The average blood pressure is the same after and before the consumption of the medicine, i.e., μafter = μbefore

Alternative Hypothesis (Ha): The average blood pressure after the consumption of the medicine is less than the average blood pressure before the consumption of the medicine, i.e., μafter < μbefore

If the p-value of the hypothesis test is less than the significance value (say .o5), the null hypothesis is rejected, i.e., it can be concluded that the new drug is responsible for the rise in the blood pressure of diabetic patients.

4. In Testing Effectiveness of Essential Oils

Essential oils are gaining popularity nowadays due to their various benefits. Various essential oils such as ylang-ylang, lavender, and chamomile claim to reduce anxiety. You might like to test the true healing powers of all these essential oils. Suppose you assume that the lavender essential oil has the ability to reduce stress and anxiety. To check this assumption you may conduct the hypothesis testing by restating the hypothesis as follows,

Null Hypothesis (Ho) : Lavender essential has no effect on reducing anxiety.

Alternative Hypothesis (Ha): Lavender oil helps in reducing anxiety.

In this experiment, group A, i.e., the experimental group are provided with the lavender oil, while group B, i.e., the control group is provided with the placebo. The data is then collected using the various statistical tools and the stress level of both the groups, i.e., the experimental and the control group is then analysed. After the calculation, the significance level, and the p-value are found to be 0.25, and 0.05 respectively. The p values are less than the significance values, hence the null hypothesis is rejected, and it can be concluded that the lavender oil helps in reducing the stress among the people.

5. In Testing Fertilizer’s Impact on Plants

Nowadays, hypothesis testing is also used to examine the impact of pesticides, fertilizers, and other chemicals on the growth of plants or animals. Let us suppose a researcher wants to check his assumption that the particular fertilizer may result in the faster growth of the plant in a month than its usual growth of 10 inches. To verify this assumption he consistently gave that fertilizer to the plant for nearly a month. Following is the mathematical procedure of the hypothesis testing in this case,

Null Hypothesis (H0): The fertilizer does not have any influence on the growth of the plant. i.e., μ = 20 inches

Alternative Hypothesis (Ha): The fertiliser results in the faster growth of the plant, i.e., μ > 20 inches

Now, if the p-value of the hypothesis testing comes smaller than the level of significance, say .05, then the null hypothesis can be rejected, and you can conclude that the particular fertilizer is responsible for the faster plant growth.

6. In Testing the Effectiveness of Vitamin E

Suppose the researcher assumes that Vitamin E helps in the faster growth of the Hair. He conduct an experiment in which the experimental group is provided with vitamin E for three months while the controlled group is provided with the placebo. The results are then analysed after the duration of three months. To verify his assumption he restates the hypothesis as follows,

Null Hypothesis (H0) : There is no association between the Vitamin E and the hair growth of the sample group, i.e., μafter = μbefore

Alternative Hypothesis (Ha) : The group of people who consumed the vitamin E shows faster hair growth than the average hair growth of them before the consumption of the Vitamin E provided other variables remains constant. Here, μafter > μbefore.

After performing the statistical analysis, the significance level and the p-value in this scenario are o.o5, and 0.20 respectively. Hence, the researcher can conclude that the consumption of vitamin E results in faster hair growth.

7. In Testing the Teaching Strategy

Suppose the two teachers say Mr X and Mr Y argue about the best teaching strategy. Mr X says that children will perform better in the annual exams if they are given the weekly tests, while Mr Y argues that the weekly test would not impact the performance of the children in the annual exams and it is waste of time. Now, to verify who is right between the both, we may conduct hypothesis testing. The researcher may formulate the hypothesis as follows,

Null Hypothesis (Ho): There is no association between the weekly tests on the performance of the children in the annual exams, i.e., the average marks scored by the children when they were given the weekly exams and when not, were the same. (μafter = μbefore)

Alternative Hypothesis (Ha): The children will perform better in the annual exams, when they have to give the weekly tests, rather than just giving the annual exams, i.e., μafter > μbefore.

Now, if the p-value of the hypothesis testing comes smaller than the level of significance, say .05, then the null hypothesis can be rejected, and the researcher can conclude that the children will perform better in the annual exams if the weekly examination system would be implemented.

8. In Verifying the Assumption related to Intelligence

Suppose a principle states that the students studying in her school have an IQ level of above average. To support her statement, the researcher may take a sample of around 50 random students from that school. Let’s say the average IQ score of those children is around 110, and the average IQ score of the mean population is 100 with a standard deviation of 15. The hypothesis testing is given as follows,

Null Hypothesis (Ho) : The population mean IQ score of 100 is a general fact, i.e., μ = 100.

Alternative Hypothesis (Ha): The average IQ score of the students is above average, i.e., μ > 100

It’s a one-tailed test as we are aiming for the ‘greater than’ assumption. Let us suppose the alpha level or we can say the significance level, in this case, is 5 per cent, i.e., 0.05, and this corresponds to the Z score equal to 1.645. The Z score is found by the statistical formula given by (112.5 – 100) / (15/√30) = 4.56. Now, the final step is to compare the values of the expected z score and the calculated z score. Here, the calculated Z score is lesser than the expected Z score, hence, the Null Hypothesis is rejected, i.e., the average IQ score of the children belonging to that school is above average.

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Hypothesis Testing and Confidence Intervals

A confidence interval is a range of possible figures/values that can capture an unknown variable given a specific degree of confidence (Perdices, 2017). On the other hand, hypothesis testing allows researchers to draw a conclusion on the possibility of a certain hypothesis using sample data from the population of interest. There are two types of hypotheses: the null and the alternative hypotheses. A null hypothesis states no statistical difference between two variables, while the alternative hypothesis and an alternative hypothesis are the opposite of the null hypothesis (Perdices, 2017). P-values and alpha values can be used to draw a conclusion. Let’ say the alpha value is 0.05. If the alpha value is smaller, the null hypothesis is rejected, and the alternative hypothesis is accepted, but if larger, the null hypothesis is accepted (Shreffle & Huecker, 2021).

There is a relationship between confidence intervals and hypothesis testing. Hypothesis testing and confidence intervals share the same characteristics since they are both inferential techniques. Inferential techniques utilize a sample to test the strength or validity of a hypothesis or estimate a population variable. The difference between confidence interval and hypothesis testing is that hypothesis testing focuses on the null hypothesis variables while confidence interventions focus on the population variables’ estimates. Both confidence intervals and hypothesis testing can be utilized to draw a conclusion.

We used evidence-based medicine to inform our practice and interventions in my workplace. We utilize our clinical judgment to determine the clinical significance of research studies by carefully analyzing study design, sample size, data analysis, hypothesis testing, and confidence intervals. We understand that statistical significance may not be equivalent to clinical significance. We are encouraged as healthcare professionals to use our experiences and knowledge to determine the applicability of study results and draw conclusions by understanding practical implications and not only statistical significance.

Perdices, M. (2017). Null hypothesis significance testing,p-values, effects sizes, and confidence intervals. Brain Impairment , 19 (1), 70-80. https://doi.org/10.1017/brimp.2017.28

Shreffle, J., & Huecker, M. R. (2021, March 25). Hypothesis testing, P values, confidence intervals, and significance – StatPearls – NCBI bookshelf . National Center for Biotechnology Information. https://www.ncbi.nlm.nih.gov/books/NBK557421/

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Confidence Intervals and Hypothesis Testing in Healthcare Essay

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Introduction

Interpretation of confidence intervals and p value.

While determining the trends and relationships that are present in population parameters, several statistical tests and analyses are usually conducted (Sukamolson, 2006). Confidence interval (CI) is a statistical measure that is used to determine the reliability of a population parameter in a statistical test. CIs are usually made up of several values. These values are essential in statistical tests since they provide reliable estimation of an unknown parameter (Sukamolson, 2006).

While conducting a study, study hypothesis are usually designed according to the objectives and goals that are to be achieved in the end. To determine whether the parameters of the population or sample size reflect to these objectives, plausible values of the study are usually determined. However, the effectiveness of these values depends on the degree of confidence that the study wants to attain. It is as a result of this that confidence intervals are developed in a research study.

For instance, when a study is developed at 95% confidence interval, all the values of the parameter that are within this range are accepted while the ones that are outside the range are rejected. Thus, in an event whereby the plausible values of the null hypothesis fall within this confidence interval, the null hypothesis is accepted. However, if the values are implausible (outside the 95% confidence interval), the null hypothesis is rejected (Sukamolson, 2006).

However, it should be noted that there are differences between CI and hypothesis testing. As it has been stated, confidence intervals are used to show the degree of freedom (or reliability) with which a specific statistical test is to be carried at. Most statistical tests are carried out at 95% level of confidence (Sukamolson, 2006). However, to determine whether there was a significant difference between the variables that were tested, a different parameter is used. This parameter is known as the p value. Thus, the p value is used to determine whether the variables of a given study are within a desired range to accept or reject the null hypothesis. This is the main difference that exists between CIs and hypothesis testing.

While conducting a study, a researcher usually determines the degree of accuracy with which he/she wants his study to be at. In normal cases, a normal distribution is used to display the graphical representation of the mean and standard deviation of the parameters of the study. This distribution is usually constructed in accordance to the margin of error that a researcher wants to encounter. For example, if the researcher wants the study to have a margin of error of 5%, he/she needs to set at + and -1.96 standard deviations for a two tailed test. If the researcher uses a one-tailed test, the margin of error shall be set at 1.64 (Myers, 2001).

However, the interpretation of the results depends on the p value of the study. The p value is the value that is used to determine the level of significance of the variables of the study (Myers, 2001). This value can be computed using statistical software packages or manually. For instance, if a researcher wants to determine the age at which the teenagers start to engage in sexual intercourse at a CI of 95% using a two-tailed tests, the p value should be less than 0.025 for the null hypothesis of the study to be accepted. If the value is more than 0.025, the null hypothesis shall be rejected.

Therefore, confidence intervals and hypothesis tests are used to complement each other in statistical tests.

Myers, R. (2001). Elements of Research: Study Design and Data Analysis. New York, NY: McGraw-Hill.

Sukamolson, S. (2006). Fundamentals of Quantitative Research . Chulalongkorn: Chulalongkorn University Press.

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HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research

Grand Canyon University HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research- Step-By-Step Guide

This guide will demonstrate how to complete the Grand Canyon University HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research assignment based on general principles of academic writing. Here, we will show you the A, B, Cs of completing an academic paper, irrespective of the instructions. After guiding you through what to do, the guide will leave one or two sample essays at the end to highlight the various sections discussed below.

How to Research and Prepare for HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research

Whether one passes or fails an academic assignment such as the Grand Canyon University HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research depends on the preparation done beforehand. The first thing to do once you receive an assignment is to quickly skim through the requirements. Once that is done, start going through the instructions one by one to clearly understand what the instructor wants. The most important thing here is to understand the required format—whether it is APA, MLA, Chicago, etc.

After understanding the requirements of the paper, the next phase is to gather relevant materials. The first place to start the research process is the weekly resources. Go through the resources provided in the instructions to determine which ones fit the assignment. After reviewing the provided resources, use the university library to search for additional resources. After gathering sufficient and necessary resources, you are now ready to start drafting your paper.

How to Write the Introduction for HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research

The introduction for the Grand Canyon University HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research is where you tell the instructor what your paper will encompass. In three to four statements, highlight the important points that will form the basis of your paper. Here, you can include statistics to show the importance of the topic you will be discussing. At the end of the introduction, write a clear purpose statement outlining what exactly will be contained in the paper. This statement will start with “The purpose of this paper…” and then proceed to outline the various sections of the instructions.

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How to Write the Body for HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research

After the introduction, move into the main part of the HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research assignment, which is the body. Given that the paper you will be writing is not experimental, the way you organize the headings and subheadings of your paper is critically important. In some cases, you might have to use more subheadings to properly organize the assignment. The organization will depend on the rubric provided. Carefully examine the rubric, as it will contain all the detailed requirements of the assignment. Sometimes, the rubric will have information that the normal instructions lack.

Another important factor to consider at this point is how to do citations. In-text citations are fundamental as they support the arguments and points you make in the paper. At this point, the resources gathered at the beginning will come in handy. Integrating the ideas of the authors with your own will ensure that you produce a comprehensive paper. Also, follow the given citation format. In most cases, APA 7 is the preferred format for nursing assignments.

How to Write the Conclusion for HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research

After completing the main sections, write the conclusion of your paper. The conclusion is a summary of the main points you made in your paper. However, you need to rewrite the points and not simply copy and paste them. By restating the points from each subheading, you will provide a nuanced overview of the assignment to the reader.

How to Format the References List for HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research

The very last part of your paper involves listing the sources used in your paper. These sources should be listed in alphabetical order and double-spaced. Additionally, use a hanging indent for each source that appears in this list. Lastly, only the sources cited within the body of the paper should appear here.

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Sample Answer for HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research Included After Question

Topic 3 dq 2.

Evaluate and provide examples of how hypothesis testing and confidence intervals are used together in health care research. Provide a workplace example that illustrates your ideas.

A Sample Answer For the Assignment: HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research

Title: hlt-362 topic 3 dq 2:evaluate and provide examples of how hypothesis testing and confidence intervals are used together in health care research.

To understand how hypothesis testing and confidence intervals (CI) work together we must first understand what exactly they are. Hypothesis Tests are tests conducted by forming two opposing hypothesis (Research HA and Null Ho) and attempting to validate each in order to reach a possible outcome. Confidence Intervals are a “range of likely values of the parameter with a specified level of confidence (similar to a probability)” (Sullivan, 2022).

Both of these are known as inferential methods which both rely on approximated sampling distributions. CI is used to find a range of possible values and an estimate on the overall accuracy of the parameter value. Hypothesis testing is useful because it tells us how confident we can be when drawing conclusions about the parameter of our sample population.HLT-362 Topic 3 DQ 2

An example of this is testing the overall performance of a new medication being offered at a clinic. One must hypothesise the effect it will have on the patient population and try to find the parameters on the satisfaction of those taking said medication. By using these two methods in conjunction, the provider can have a good educated guess on the outcome and prepare accordingly.

Click here to ORDER an A++ paper from our Verified MASTERS and DOCTORATE WRITERS: EVALUATE AND PROVIDE EXAMPLES OF HOW HYPOTHESIS TESTING AND CONFIDENCE INTERVALS ARE USED TOGETHER IN HEALTH CARE RESEARCH HLT 362

Sullivan, L. (2022, January 1). Confidence Intervals . Retrieved from Boston University School of Public Health: https://sphweb.bumc.bu.edu/otlt/mph-modules/bs/bs704_confidence_intervals/bs704_confidence_intervals_print.html

A Sample Answer 2 For the Assignment: HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research

The hypothesis is a question the researcher would like to answer. A hypothesis drives a better outcome for patient care that goes evidence-based practice. The person must collect data in a controlled manner designated best to test the hypothesis. When using the Null hypothesis as current information, the alternative hypothesis attempts to reject the null. At the same time, the Ho and the Ha are mathematic opposites. Clinical significance is the application in improving the quality of life of an individual and provides the bridge from health research to patient care (Ambrose, 2018).HLT-362 Topic 3 DQ 2

While confidence intervals and hypothesis tests are similar, they contain inferential methods relying upon sampling. The LOC is a percentage of confidence level in deciding the difficulty of rejecting the hypothesis. Most people doing this research are > 90% LOC; otherwise, the test would not be warranted. The level of significance is α=1-c. Both the LOC and level of relevance reflect how sure you are of whether the data is making the correct decision or not.

The American Heart Association guidelines for resuscitation were based on the pneumonic of ABC- Airway, Breathing, and Circulation. The pneumonic is the null hypothesis. The alternative view was the use of Circulation, airways, and breathing. The research data reflected the Ha > Ho. The concentration of effective quality chest compressions leads to a worldwide change in how CPR is performed. The LOC was high enough to recruit large city Fire Dept such as Phoenix Fire to provide data regarding cardiac arrest and outcomes.

References HLT-362 Topic 3 DQ 2

Ambrose, J. (2018a). Applied Statistics for Health Care. Grand Canyon University. https://doi.org/https://lc. gcumedia.com/hlt362v/applied- statistics-for-health- care/v1.1/#/chapter/3

A Sample Answer 3 For the Assignment: HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research

Both hypothesis testing and confidence interval are necessary for determining the validity of the research. Ambrose describes both type I and type II errors as flaws in the research outcomes that can be avoided with proper data analysis (2018). The text even further states “The researcher has an ethical responsibility to avoid making a type I or II error” (Ambrose, 2018). It falls upon nursing leadership to review current research and implement evidence-based nursing care and interventions. Accepting and promoting false research can ultimately create negative outcomes for patients and the care they receive.

Resource: HLT-362 Topic 3 DQ 2

Ambrose, J. (2018). Clinical inquiry and hypothesis testing. Applied Statistics for Health Care. https://lc.gcumedia.com/hlt362v/applied-statistics-for-health-care/v1.1/#/chapter/3

A Sample Answer 4 For the Assignment: HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research

Great discussion this week, sharing your examples of how hypothesis testing and confidence intervals are used together in health care practice. You also highlighted the criteria we use in rejecting the null hypothesis. In summary, this week we were able to:

Evaluate hypothesis testing approaches and their applications to health care.
Describe the roles of dependent and independent variables in hypothesis testing.
Describe the evidence used to “reject” or “do not reject” the null hypothesis.
Evaluate the relationship between hypothesis testing and confidence intervals.

Watch “When Should I Use Qualitative vs. Quantitative Research?” on the YouTube website located at http://www.youtube.com/watch?v=638W_s5tRq8&feature=youtube_gdata to help you understand when you use qualitative and quantitative research in your clinical practice.

This short video was very helpful. Thank you for sharing it. HLT-362 Topic 3 DQ 2

My understanding of the difference between the use of Qualitative vs. Quantitative research is: Quantitative studies rely on numerical or measurable data. In contrast, qualitative studies rely on personal accounts or documents that illustrate in detail how people think or respond within society. For example, qualitative data is collected by surveys and questionnaires, interviews, focus groups, and observations just to mention some. Quantitative has a numeral value therefore it is measured in numerical value.

Hypothesis testing and confidence intervals are used together in healthcare research. A hypothesis is a forecast statement of what will occur between two variables. The independent and dependent variables are identified in the hypothesis and analyzed with gathered data to show correlations and relationships between variables. A hypothesis is created when variables are identified.

A confidence interval (CI) is an interval estimate of the mean which is a range of values of the data. These values are close to the mean in a negative or positive direction. The CI shows the risks of being wrong. If the CI reduces the risk of error increases. A CI of 95% says that 5% of the mean will not be true yet 95% will be a true mean. (Ambrose, 2018)HLT-362 Topic 3 DQ 2

A workplace example where the CI is used is a study suggesting that working shift work for long hours during pregnancy can be associated with adverse pregnancy risks. The study showed that working a fixed night shift measured a CI of 95% with increased odds of miscarrying when compared to standard working hours.

The study also revealed that working rotating shifts revealed a CI of 95% of increasing odds for preterm delivery. (Cai et al., 2019) Using CIs with multiple variables, this study concluded that pregnant women increase risks of adverse pregnancy outcomes if working rotating shifts, fixed night shifts or longer hours.HLT-362 Topic 3 DQ 2

I work in a female-dominated industry and department. I frequently see my pregnant colleagues being placed on alternative duty or light duty while pregnant. This study concerns me because the “light duty” does not decrease their hours but instead keeps them from working “on their feet” all shift. It would be interesting to see if these coworkers have increased adverse pregnancies in their work situations.

Ambrose, J. (2018). Applied Statistics for Health Care . Gcumedia.com. https://lc.gcumedia.com/hlt362v/applied-statistics-for-health-care/v1.1/#/chapter/3
Cai, C., Vandermeer, B., Khurana, R., Nerenberg, K., Featherstone, R., Sebastianski, M., & Davenport, M. H. (2019). The impact of occupational shift work and working hours during pregnancy on health outcomes: a systematic review and meta-analysis. American Journal of Obstetrics and Gynecology , 221 (6). https://doi.org/10.1016/j.ajog.2019.06.051

A Sample Answer 5 For the Assignment: HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research

Both confidence intervals and hypothesis tests are inferential methods that depend on a sample distribution that is approximated. Confidence intervals are used to measure a population parameter using data from a survey. Hypothesis experiments are used to test a hypothesis using data from a study. Hypothesis testing necessitates the presence of a parameter that has been hypothesized. A hypothesis test determines whether the outcome is exceptional, whether it is reasonable chance variation, or whether it is too extreme to be considered chance variation.

In health-care science, hypothesis testing, and confidence intervals are used together. This is used as an interval estimation for the mean with confidence interval (CI). A confidence interval (CI) is a set of values that are like the mean and can affect the direction in either a positive or negative way. Means using a procedure that contains the population mean with a defined proportion of the time, usually 95 percent or 99 percent of the time, are given a confidence interval (CI).

The CI is the range in which the researcher could be incorrect. A 95% confidence interval indicates that 95% of a research sample will contain the true mean, while the remaining 5% will not. Confidence intervals will help you compare the accuracy of various estimates with this in mind. For example, 95 percent of the data collected in a test survey of 100 participants will be correct, while five out of 100 will be incorrect.

If the 95 percent is decreased, the chance of error increases (Ambrose, 2018). Since hypothesis testing and confidence intervals are used together in health care research, this is important to note.

If you wanted to know the mean of temperatures obtained in a hospital with COVID-19 patients, you’d need to think about hypothesis testing and confidence intervals. Since it’s necessary to have a true mean of the temperatures of the sample collected, a CI of 95 percent will be better than a CI of 90 percent for this example. This is because the CI is determined by first determining the sample size, then determining the mean and standard deviation, and finally determining the degree of confidence interval.

It’s crucial to understand analytical quantitative analysis, which requires hypothesis testing and confidence intervals, to produce reliable findings from samples for the populations being studied. This is particularly relevant in health care, where positive outcomes can be established to enhance patient care.

References:

How hypothesis tests work: Confidence intervals and confidence levels. (2019, June 25). Statistics By Jim. https://statisticsbyjim.com/hypothesis-testing/hypothesis-tests-confidence-intervals-levels/
LibGuides: Maths: Hypothesis testing. (2020, May 13). LibGuides at La Trobe University. https://latrobe.libguides.com/maths/hypothesis-testing More about hypothesis testing. (n.d.). https://bolt.mph.ufl.edu/6050-6052/unit-4/module-12/more-about-hypothesis-testing

A Sample Answer 6 For the Assignment: HLT-362 Topic 3 DQ 2:Evaluate and Provide Examples of How Hypothesis Testing and Confidence Intervals are Used Together in Health Care Research

Hypothesis testing and confidence intervals are significant contributors to evidence-based practice and guide the protocols of patient intervention, ultimately influencing patient outcomes. Hypothesis testing can be employed to correlate associations between variables, which leads to quality improvement and supports improvements to standard practice. However, it is the role of nursing leadership to responsibly interpret research and apply it for the improved patient outcomes (Ambrose, 2021). Confidence intervals represent the risk that the research could be wrong. The combined use of hypothesis testing with confidence intervals allows for results to be extrapolated and applied to the general population. While the confidence interval can be important in applying the research results from the sample to the population, it is important to be aware of the ability to manipulate the confidence interval if thorough methods are not employed. Small sample size and lack of replication are both contributing factors to misinterpreted p-values or statistical significance.

In the example of a 2019 study by Henson et al., the authors compared select outcomes associated with postoperative analgesic approaches in patients undergoing TKA. The two approaches were femoral nerve blocks and periarticular injections. Using 2-tailed t tests, the researchers examined pain perception, use of opioid analgesics, length of stay and total cost of care. Additionally, they examined readmission rates using a 2-sample z test for proportions. With a stratified population sample of 144 patients, the authors found an association between patients who received an FNB with a lower pain perception (p = .0497). Results also showed a possible correlation between a decrease in opioid consumption in those who received a PAI (p = .037). By calculating the mean, along with the standard deviation of the five selected variables, the authors were able to gain data regarding reported decrease in pain, it is clear that the study needs further replication with additional factors examined (Henson, et al., 2019). However, studies like this set the groundwork for future studies to build upon and gain even more in-depth knowledge regarding the topic or practice at hand.

Ambrose, J. (2021). Clinical inquiry and hypothesis testing. In Grand Canyon University (Ed.). Applied statistics for health care (ch.3). https://bibliu.com/app/#/view/books/1000000000581/epub/Chapter3.html#page_31

Henson, K. S., Thomley, J. E., Lowrie, L. J., & Walker, D. (2019). Comparison of Selected Outcomes Associated with Two Postoperative Analgesic Approaches in Patients Undergoing Total Knee Arthroplasty. AANA Journal , 87 (1), 51–57.

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Evaluate and provide examples of how hypothesis testing and confidence intervals are used together in health care research.

Hypothesis testing and confidence intervals are used together in health care research. With confidence interval (CI), this is used as an interval estimate for the mean. CI is a range of values that are set close to the mean and can impact the direction either positively or negatively (Ambrose, 2018). CI are used for means using a procedure that contains the population mean with a specified proportion of the time, typically either 95% or 99% of the time (Lane, n.d.). The CI is the interval that the researcher can be wrong. CI of 95% shows that 95% of a research study will include the true mean, and the other 5% will not be true (Ambrose, 2018). For example, in a test survey of 100 participants, 95% of the collected data will be right and five out of the 100 will be wrong. If the 95% is reduced, this increased the risk for error (Ambrose, 2018). This is important to consider with health care research because hypothesis testing and confidence intervals are used together.

For example, if you wanted to know the mean of temperatures collected in a hospital with COVID-19 patients, it’s important to consider the hypothesis testing and confidence interval with that study. A CI of 95% for this example would be better than a CI of 90%, because it’s important to have a true mean of the temperatures of the sample collected. This is because the CI is calculated by knowing the sample size, identifying the mean and standard deviation, and choosing the level of confidence interval (Ambrose, 2018). For my example, I chose 95% as the level of confidence interval.

It’s important to understand analytical quantitative research which involves hypothesis testing and confidence intervals in order to generate valid results from the samples for populations that are researched (El-Masri, 2016), especially for health care so that positive outcomes can be formed to improve patient care.

Ambrose, J. (2018). Clinical inquiry and hypothesis testing. Applied Statistics for Health Care. Retrieved from https://lc.gcumedia.com/hlt362v/applied-statistics-for-health-care/v1.1/#/chapter/3

El-Masri, M.M. (2016, June 1). Statistical versus clinical significance in nursing research. Canadian Journal of Nursing Research, 48(2), 31-32. doi: https://doi.org/10.1177/0844562116677895