Provide two different examples of how research uses hypothesis testing, and describe the criteria for rejecting the null hypothesis. Discuss why this is important in your practice and with patient interactions.

Question

Provide two different examples of how research uses hypothesis testing, and describe the criteria for rejecting the null hypothesis. Discuss why this is important in your practice and with patient interactions.

Dan Palmer · Accepted Answer

An example of the use of hypothesis testing is in determining the prevalence of common cold in children who take vitamin C. The null hypothesis would state that the prevalence of flu in children who take vitamin C is similar to those who don’t take vitamin C. the alternative hypothesis would be that children with the uptake of vitamin C have a reduced prevalence of flu in flu seasons. Another example would be research to identify if therapy is more effective than a placebo. In order to reject the null hypothesis, a redetermined number of subjects among the hypothesis test have to prove the alternative hypothesis. The proof will then overturn the original null hypothesis, which will then be rejected. Hypothesis testing is an important aspect of statistics and research as it provides a basis for understanding whether something actually occurred or if certain groups or sets of data are different from each other (Dubois, 2017). Hypothesis testing also helps in identifying if an aspect of the research has more positive effects or if a variable can predict another to form a basis for defining a conclusion. With the help of the calculated probability (p-value), one can easily determine the inclination of the research based on either the null hypothesis of the alternative hypothesis. References Dubois, S. (2017). The Importance of Hypothesis Testing. (2020). Retrieved 18 May 2020, from https://sciencing.com/the-importance-of-hypothesis-testing-12750921.html Mellenbergh, G. J. (2019). Null Hypothesis Testing. In Counteracting Methodological Errors in Behavioral Research (pp. 179-218). Springer, Cham. https://link.springer.com/book/10.1007/978-3-030-12272-0 Topic 3 DQ 2: Hypothesis Testing and Confidence Intervals Hypothesis tests and confidence intervals are related in the sense that they both are inferential methods that are based on an approximated sampling distribution. The hypothesis tests make use of data from a given sample to test the predetermined hypothesis (Sacha & Panagiotakos, 2016). On the other hand, confidence intervals make use of data from the sample to provide an estimate of the population parameter. In this manner, it is evident that the simulation methods that are used in the construction of the bootstrap distribution, as well as the randomization distributions, are identical. Confidence intervals are made up of a range of reasonable estimations concerning population parameters. For instance, a two-tailed confidence interval is applied in a two-tailed hypothesis testing. In health care research, a confidence level of 95 percent is mostly used. The level indicates the significance of health research with regards to being precise and accurate with health care data (Hazra, 2017). For instance, while conducting research on the effect of therapy or medication on patients with mental health conditions. The calculation of the p-value will allow the researcher to achieve the results of the null hypothesis. With a low p-value, a researcher is able to comprehend that there is stronger support for the alternative hypothesis. In the workplace setting, research can be conducted on the impacts of evidence-based practice on patient outcomes. Hypothesis testing will facilitate the identification of educated guesses, while the confidence interval will provide a basis for the statistical confidence level that will be used in the research (Sacha & Panagiotakos, 2016). The research will then be used to provide a recommendation for the viability of the EBP. References Hazra A. (2017). Using the confidence interval confidently. Journal of thoracic disease, 9(10), 4125–4130. https://doi.org/10.21037/jtd.2017.09.14 Sacha, V., & Panagiotakos, D. B. (2016). Insights in Hypothesis Testing and Making Decisions in Biomedical Research. The open cardiovascular medicine journal, 10, 196–200. https://doi.org/10.2174/1874192401610010196 Alternative Expert Answer and Explanation Topic 3 DQ 1 How Research Uses Hypothesis Testing And Criteria For Rejecting the Null Hypothesis Hypothesis testing is a statistical method commonly used in research to give interpretations about populations based on the sampled data. It is crucial to researchers as it determines whether there is sufficient evidence to either reject or support a hypothesis (Stunt et al., 2021). In hypothesis testing, two hypotheses are the null hypothesis and the alternative hypothesis (Bahrampour et al., 2022). The criteria for rejecting the null hypothesis have been explained in the examples of how research can use hypothesis testing. A good example of hypothesis testing is when a pharmaceutical company is testing a new drug with the argument that it is more effective in treating a given medical condition as compared to an existing drug. The hypotheses for the study can be: Null hypothesis: The new drug is as effective as the existing drug. Alternative Hypothesis: The new drug is more effective as compared to the existing drug. Another example of hypothesis testing is psychological research where hypothesis testing can be integral in determining the effects of a particular intervention. A hypothesis can state: Null hypothesis: This therapy does not show a difference in anxiety levels before and after therapy. Alternative hypothesis: The therapy contributed to a reduction in anxiety levels after therapy. To reject the null hypothesis the pharmaceutical company can set a significant level of 0.05 which is mostly used. After conducting the study and after data analysis, if the probability value associated with the test is less or equal to 0.05 then the null hypothesis can be rejected. This will mean that there is statistically significant evidence to support the alternative hypothesis which means that the new drug is more effective. The practice of rejecting the null hypothesis in favor of an alternative is that it can aid in getting better patient outcomes (Emmert-Streib & Dehmer, 2019). Failing to reject the null hypothesis means that the new drug does not have an advantage over the other drugs and therefore not suitable. References Bahrampour, A., Avazzadeh, Z., Mahmoudi, M. R., & Lopes, A. M. (2022). Improved Confidence Interval and Hypothesis Testing for the Ratio of the Coefficients of Variation of Two Uncorrelated Populations. Mathematics, 10(19), 3495. https://doi.org/10.3390/math10193495 Emmert-Streib, F., & Dehmer, M. (2019). Understanding Statistical Hypothesis Testing: The Logic of Statistical Inference. Machine Learning and Knowledge Extraction, 1(3), 945–961. https://doi.org/10.3390/make1030054 Stunt, J., van Grootel, L., Bouter, L., Trafimow, D., Hoekstra, T., & de Boer, M. (2021). Why we habitually engage in null-hypothesis significance testing: A qualitative study. PLOS ONE, 16(10), e0258330. https://doi.org/10.1371/journal.pone.0258330 Topic 3 DQ 2 Examples Of How Hypothesis Testing And Confidence Intervals Are Used Together In Health Care Research Confidence intervals and hypothesis testing are usually used together as complementally statistical tools. In healthcare research, they are useful in drawing meaningful conclusions about the population based on only the sample data. Notably, hypothesis testing is useful in assessing whether a specific hypothesis is supported, and on the other hand, confidence intervals provide a range of values that aim at containing the true population meter (Bahrampour et al., 2022). Hypothesis testing is useful in determining if there is sufficient evidence to either support or reject a specific hypothesis about a population. This is mostly through a null and alternative hypothesis. On the other hand, the confidence intervals provide a range of values that are within the true population meters. A good example is a hospital that is researching to evaluate the effectiveness of a new EHR system to ensure more data confidentiality for patients being attended at the hospital. The null hypothesis can be: The new EHR system does not affect the safety of patients' data. There is no difference compared to the standard surgical technique (Stunt et al., 2021). The alternative hypothesis can be that the new EHR system greatly reduces data vulnerability and patients’ confidentiality is more assured. On the confidence intervals; Researchers collected data on patients who experienced the new EHR system and calculated the sample mean satisfaction level with a 90% confidence interval for the mean (Shreffler & Huecker, 2023). The 90% confidence level means that based on the sample data and the chosen level of confidence (90%) we can argue that we are 90% confident that the true increase in data confidentiality based on the new system will fall within this range. By using the hypothesis testing and the confidence interval it is possible to have a better perspective when implementing the new system. References Bahrampour, A., Avazzadeh, Z., Mahmoudi, M. R., & Lopes, A. M. (2022). Improved Confidence Interval and Hypothesis Testing for the Ratio of the Coefficients of Variation of Two Uncorrelated Populations. Mathematics, 10(19), 3495. https://doi.org/10.3390/math10193495 Shreffler, J., & Huecker, M. R. (2023). Hypothesis Testing, P Values, Confidence Intervals, and Significance. PubMed; StatPearls Publishing. https://www.ncbi.nlm.nih.gov/books/NBK557421/#:~:text=Based20the Stunt, J., van Grootel, L., Bouter, L., Trafimow, D., Hoekstra, T., & de Boer, M. (2021). Why we habitually engage in null-hypothesis significance testing: A qualitative study. PLOS ONE, 16(10), e0258330. https://doi.org/10.1371/journal.pone.0258330

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

Written By: Dan Palmer, RN

Edited By: Ethan Green, MSN-FNP