HLT 362 Provide two different examples of how research uses hypothesis testing, and describe the criteria for rejecting the null hypothesis

When a clinical trial begins, there is a belief or assumption which is to be proven or disproved. The belief or assumption is known as the hypothesis. A null hypothesis in a study states that there is no relationship between the variables. An alternative hypothesis shows that there is a relationship between the variables indicating it is the opposite of the null hypothesis. (Helbig & Ambrose, 2018)

A prediction between two variables is a hypothesis that identifies independent and dependent variables. However, correlations between variables do not always prove causation. A study is underway to determine if cells with high cholesterol levels are more susceptible to the SARS-CoV-2 virus than low cholesterol cells. (Wang et al., 2020) A null hypothesis is important in this study to determine whether or not individuals with high cholesterol are more susceptible to lethal Covid infections to improve outcomes, decrease errors, and determine changes in practice to improve patient outcomes.

Another study in which the null hypothesis is critical is a double-blind clinical trial to research if high-dose vitamin D decreases the risk of pre-diabetic individuals progressing towards diabetes. (Niroomand et al., 2019)

These two examples are important in my practice and patient interactions because both diabetes and covid-19 are prevalent at this time. If simple medications and vitamins can be used to improve patient outcomes and health then it is important for nurses to be able to interpret this data.

References:

Helbig, J., & Ambrose, J. (2018). Applied Statistics for Health Care. Gcumedia.com. https://lc.gcumedia.com/hlt362v/applied-statistics-for-health-care/v1.1/#/chapter/3

Wang, H., Yuan, Z., Pavel, M. A., Hobson, R., & Hansen, S. B. (2020). The role of high cholesterol in age-related COVID19 lethality. BioRxiv. https://doi.org/10.1101/2020.05.09.086249

Niroomand, M., Fotouhi, A., Irannejad, N., & Hosseinpanah, F. (2019). Does high-dose vitamin D supplementation impact insulin resistance and risk of development of diabetes in patients with pre-diabetes? A double-blind randomized clinical trial. Diabetes Research and Clinical Practice, 148, 1–9. https://doi.org/10.1016/j.diabres.2018.12.008

Click here to ORDER an A++ paper from our Verified MASTERS and DOCTORATE WRITERS HLT 362 Provide two different examples of how research uses hypothesis testing, and describe the criteria for rejecting the null hypothesis:

Hypothesis and prediction are two different things, but they are frequently confused.

Both are statements assumed to be true, based on existing theories and evidence. However, there are a couple of key differences to remember:

A hypothesis is a general statement of how you think the phenomenon works.
Meanwhile, your prediction shows how you will test your hypothesis.
The hypothesis should always be written beforethe prediction.

Remember that the prediction should prove the hypothesis to be correct.

The purpose of an experiment is to gather evidenceto test your prediction. Gather your apparatus, measuring equipment and a pen to keep track of your results.

Example:

When magnesium reacts with water, it forms magnesium hydroxide, Mg(OH)2. This compound is slightly alkaline. If you add anindicator solution to the water, it will change colour when magnesium hydroxide has been produced and the reaction is complete.

To test the reaction rate at different temperatures, heat beakers of water to the desired temperature, then add the indicator solution and the magnesium. Use a timer to track how long it takes for the water to change colour for each water temperature. The less time it takes for the water to change colour, the faster the rate of reaction.

1. CGP, GCSE AQA Combined Science Revision Guide, 2021

2. Jessie A. Key, Factors that Affect the Rate of Reactions, Introductory Chemistry – 1st Canadian Edition, 2014

3. Neil Campbell, Biology: A Global Approach Eleventh Edition, 2018

4. Paul Strode, The Global Epidemic of Confusing Hypotheses with Predictions Fixing an International Problem, Fairview High School,2011

In research, hypothesis testing is vital. It helps us to determine whether something actually took place, whether certain treatments are effective, whether groups differ from one another, or whether one variable predicts another. For example, hypothesis testing in research is used to evaluate the strength of evidence from the sample. It provides a framework for making determinations related to the population. i.e., it provides a method for understanding the reliability of extrapolating observed findings in a sample to the larger population that the sample was drawn from.

As an example, a jury must use evidence to decide whether a defendant is innocent or guilty in a criminal trial where two possible truths exist. If a jury returns a verdict of not guilty, then it does not necessarily mean the defendant is innocent. The burden of proof does not appear to have been met, in other words. For hypothesis testing, the investigator sets the burden by selecting the level of significance for the test, which is the probability of rejecting the null hypothesis when it’s true It is assumed that the null hypothesis is correct until there is enough evidence to suggest otherwise. After performing a hypothesis test, there are only two possible outcomes. When the p-value is less than or equal to your significance level, the null hypothesis is rejected. The data favor the alternative hypothesis. If statistical analysis shows that the significance level is below the cut-off value that has been set, we reject the null hypothesis and accept the alternative hypothesis.

Hypothesis testing serves an imperative role in empirical research and evidence-based medicine in clinical practice, where there is interaction with patients. A well-worked hypothesis is half the answer to the research question. For this, both knowledge of the subject derived from an extensive review of the literature and a working knowledge of basic statistical concepts are desirable. In some applications, hypothesis testing is used to determine whether two groups are different from each other. A special case of hypothesis testing involves evaluating a group of samples to determine whether a particular standard or other requirement is being met. Therefore, hypotheses have a direct impact on the quality of healthcare and patient outcomes.

References

Banerjee, A., Chitnis, U. B., Jadhav, S. L., Bhawalkar, J. S., & Chaudhury, S. (2009). Hypothesis testing, type I and type II errors. Industrial psychiatry journal, 18(2), 127. https://pubmed.ncbi.nlm.nih.gov/21180491/

Sacha, V., & Panagiotakos, D. B. (2016). Insights in hypothesis testing and making decisions in biomedical research. The open cardiovascular medicine journal, 10, 196. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5054503

Hypothesis testing is used as the framework to build a study. By positing the outcome that one variable will have on another, research aims to show a correlation between the two variables. This allows the research to make an educated assumption about the population that is sampled. Examples of this include the null and alternative hypothesis. The null hypothesis is put forward to show a lack of correlation between tested variables and will be shown to be either true or false. The alternative hypothesis conversely indicates a correlation between the tested variables. These two examples go hand in hand, because if one is rejected, the other is then accepted based on data produced by the study. In order for the null hypothesis to be rejected, certain statistical criteria must be met. The criteria include a confidence interval of 95%. This confidence interval of 95% is important because it determines the probability that the results of the study are not by chance. With this comes the possibility of making either a type I or type II error (Ambrose, 2021).

Understanding the application of hypothesis testing along with the criteria for rejecting a null hypothesis is important to one’s practice and patient interactions for many reasons. First of all, aiming for a full and in depth understanding of the research process allows for more accurate and productive synthesis of research to build and improve evidence-based practice. In a clinical setting when interacting with patients, hypothesis testing is the foundation by which differentials and diagnosis are made, confirmed and treated. By proving or disproving various diseases processes, the clinician can determine the best plan of care. Additionally, as seen in a 2022 study by Taher et al. in which both communication between provider and patient was improved while increasing safety precautions for the provider, hypothesis testing is critical in quality improvement throughout healthcare.

References

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

Taher, A., Glazer, P., Culligan, C., Crump, S., Guirguis, S., Jones, J., Dharamsi, A., & Chartier, L. B. (2022). Improving safety and communication for healthcare providers caring for SARS-COV-2 patients. International Journal of Emergency Medicine, 15(1), 1–8. https://doi-org.lopes.idm.oclc.org/10.1186/s12245-022-00464-y

Hypothesis testing is a very important aspect of statistics; it is the process of evaluating the results of a survey or experiment to determine the strength of the evidence; hence to ascertain if the results are meaningful. McDonald (2019) stated that a null hypothesis is the statement being tested; it is normally a commonly accepted fact. A null hypothesis is deemed correct until proven otherwise, it can be rejected when there is a lack of evidence to prove the hypothesis, or if the accuracy of the null hypothesis is 5% or less probability that the result is correct (Glen, 2020).

Optimal nursing care relies heavily on research to substantiate evidence-based practice. It is important to have reliable data as determines the level of nursing care provided and the improvements that are needed in the provision of care. For example, research has shown the turning patients frequently prevents pressure ulcers, so hospitals have instituted a two-hourly turning schedule.

An example of hypothesis testing is changing central line dressing every seven days reduces the risk of central line associated bloodstream infection (CLABSI). To reject this null hypothesis, it must be proven that central line dressing changes have limited or no effect on preventing CLABSI. This study can be conducted by evaluating the charts of patients in the ICU to determine the dressing change routine and the incidences of CLABSIs in the department.

Another example of hypothesis testing is both exercise and a healthy diet is needed for successful weight loss. To reject this hypothesis, it must be proven that successful weight loss can be achieved by exercising only or consuming a healthy diet. This can be achieved by having one group of participants doing both exercise and eating a healthy diet and a control group doing only exercise or eating a healthy diet.

References

McDonald, J. H. (2019) Handbook of Biological Statistics (3rd ed.). Sparky House Publishing, Baltimore, Maryland.16-23

Glen, S. (2020). Hypothesis Testing. Elementary Statistics for the rest of us. https://www.statisticshowto.com/probability-and-statistics/hypothesis-testing.

Hypothesis testing is an important tool for nursing and healthcare research, as it allows for evidence-based practice to be established. One example of this is the study conducted by Owolabi et al. (2020) which sought to examine the impact of educational intervention on the knowledge of nurses on the prevention of central line-associated bloodstream infections (CLABSI). The null hypothesis was that educational intervention would have no effect on the knowledge of nurses on the prevention of CLABSIs. The study found that educational interventions significantly improved the knowledge of nurses in the prevention of CLABSIs, thus allowing the null hypothesis to be rejected.

Another example of hypothesis testing in nursing is a study conducted by Smith et al. (2020) which sought to examine the impact of an exercise and dietary intervention program on successful weight loss. The null hypothesis was that successful weight loss could only be achieved through exercise or dietary interventions. The study found that a combination of exercise and dietary interventions was the most successful way to achieve successful weight loss, thus allowing the null hypothesis to be rejected.

These two studies are good examples of the importance of hypothesis testing in nursing and healthcare research. Without it, evidence-based practice would be impossible and healthcare professionals would be unable to provide the best possible care to their patients.

References

Owolabi, O. A., Olatunde, O., Abiodun, O., Bamgbopa, T. O., & Kuye, O. (2020). The impact of educational intervention on nurses’ knowledge of central line-associated bloodstream infection prevention. BMC Nursing, 19(1). https://doi.org/10.1186/s12912-020-00494-w

Smith, K., De Souza, M. J., Schuna, J. M. Jr., Lefevre, M., & Champagne, C. M. (2020). Exercise and dietary intervention program: Impact on successful weight loss. Nutrition & Dietary Supplements, 12(1). https://doi.org/10.2147/NDS.S247159

Hypothesis testing is an important part of research and is used to evaluate if the research results are reliable. One example of hypothesis testing is the chi-square test, which is used to test whether the observed frequencies of categorical variables are different than the expected frequencies. In this test, the null hypothesis states that there is no difference between the expected and observed frequencies. The criteria for rejecting the null hypothesis is determined by the p-value, which is the probability of obtaining the observed results if the null hypothesis is true (Khan & Othman, 2020; Bianco & Pezzullo, 2020). If the p-value is less than the predetermined significance level (usually 0.05), then the null hypothesis is rejected.

The importance of hypothesis testing in practice and patient interactions is to ensure that the research results are reliable and can be trusted. By rejecting the null hypothesis, researchers are able to conclude that the results of their research are statistically significant and can be trusted. This is important in patient interactions because it helps to ensure that the data used to make decisions is reliable and that the decisions being made are based on evidence-based practices. For example, if research shows that a certain treatment is effective in reducing symptoms, then healthcare providers can trust the results of the research and use the treatment in their practice.

Example of hypothesis testing is the t-test. The t-test is used to compare the means of two groups in order to assess whether they are significantly different. The null hypothesis in this test is that there is no difference between the means of the two groups. The criteria for rejecting the null hypothesis is determined by the t-statistic and the p-value. If the t-statistic is greater than the critical t-value and the p-value is less than the predetermined significance level, then the null hypothesis is rejected(Khan & Othman, 2020; Bianco & Pezzullo, 2020).

The importance of hypothesis testing in practice and patient interactions is similar to that of the chi-square test. By rejecting the null hypothesis, healthcare providers can be sure that the results of their research are reliable and can be trusted. This is important in patient interactions because it helps to ensure that the data used to make decisions is reliable and that the decisions being made are based on evidence-based practices. For example, if research shows that a certain treatment is effective in reducing symptoms, then healthcare providers can trust the results of the research and use the treatment in their practice.

In conclusion, hypothesis testing is an important part of research and is used to evaluate if the research results are reliable (Khan & Othman, 2020; Bianco & Pezzullo, 2020). Examples of hypothesis testing include the chi-square test and the t-test. The importance of hypothesis testing in practice and patient interactions is to ensure that the research results are reliable and can be trusted. By rejecting the null hypothesis, healthcare providers can be sure that the results of their research are reliable and that the decisions being made are based on evidence-based practices.

References

Khan, A., & Othman, A. (2020). Using Chi-Square Test of Independence to Analyze Categorical Data. International Journal of Computer and Communication Engineering, 8(1), 18-21.

Bianco, A., & Pezzullo, A. (2020). A simple guide to understand the t-test. International Journal of Exercise Science, 13(2), 486-499.

The hypothesis is a prediction or assumption based on limited evidence. The idea of hypothesis testing is to do further investigation to find out if the prediction or assumption is true or false. Typically every research starts with a hypothesis, the researcher makes a prediction based on limited evidence and then proceeds with hypothesis testing to find out if the hypothesis/claim is true or false. Hypothesis testing is a statistical procedure for testing whether chance is a plausible explanation of an experimental finding (Online Statistics Education) and it is an important activity of empirical and evidence-based medicine(Banerjee, 2009).

One way hypothesis testing is used by researchers is in a hypothetical experiment to determine the difference between variables – it is used to evaluate the new treatment and compare it to the ones already being used. For example, in patients with cancer different cancer treatments are used to determine which treatment is more effective or has a better outcome.

Another example of how hypothesis testing is used by researchers is in finding how variables affect each other – independent variables and dependent variables. For example, the researcher will use hypothesis testing to determine the relationship between the consumption of fast food and obesity in children.

The rejection of the null hypothesis is pretty much stating the opposite of what researchers are predicting. For example in the case of the hypothesis of the relationship between fast food and obesity in children, the null hypothesis says that there is no effect or correlation between fast food consumption and obesity in children. However, if the null hypothesis is proven incorrect or rejected then the alternative hypothesis is accepted.

“Hypothesis testing is a fundamental process in making a decision about populations of interest in research” (Pereira, 2009). Hypothesis testing allows healthcare workers to provide evidence-based care because providers rely on evidence-based medicine to make decisions. For example for a patient with asthma, the provider needs to make his decision about which treatment will be the more effective for the patient.

References

Ambrose, J. et al. (2021). Applied Statistics for Health Care. (n.d.). Clinical Inquiry and Hypothesis Testing; lc.gcumedia.com. Retrieved February 14, 2023, from https://bibliu.com/app/#/view/books/1000000000581/epub/Chapter3.html#page_31

Banerjee, A., Chitnis, U. B., Jadhav, S. L., Bhawalkar, J. S., & Chaudhury, S. (2009). Hypothesis testing, type I and type II errors. Industrial psychiatry journal, 18(2), 127–131. https://doi.org/10.4103/0972-6748.62274

Online Statistics Education: A Multimedia Course of Study (http://onlinestatbook.com/). Project Leader: David M. Lane, Rice University.

Pereira, S. M., & Leslie, G. (2009). Hypothesis testing. Australian critical care: official journal of the Confederation of Australian Critical Care Nurses, 22(4), 187–191. https://doi.org/10.1016/j.aucc.2009.08.003

HLT 362 Provide two different examples of how research uses hypothesis testing, and describe the criteria for rejecting the null hypothesis