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

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

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A scientific hypothesis is a tentative, testable explanation for a phenomenon in the natural world. It’s the initial building block in the scientific method . Many describe it as an “educated guess” based on prior knowledge and observation. While this is true, a hypothesis is more informed than a guess. While an “educated guess” suggests a random prediction based on a person’s expertise, developing a hypothesis requires active observation and background research.

The basic idea of a hypothesis is that there is no predetermined outcome. For a solution to be termed a scientific

hypothesis, it has to be an idea that can be supported or refuted through carefully crafted experimentation or observation. This concept, called falsifiability and testability, was advanced in the mid-20th century by Austrian-British philosopher Karl Popper in his famous book “The Logic of Scientific Discovery” (Routledge, 1959).

A key function of a hypothesis is to derive predictions about the results of future experiments and then perform those experiments to see whether they support the predictions

Encyclopedia Britannica. Scientific Hypothesis. Jan. 13, 2022. https://www.britannica.com/science/scientific-hypothesis

Karl Popper, “The Logic of Scientific Discovery,” Routledge, 1959.

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.

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### 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