# Provide feedback or respond to each answer.

Provide feedback or respond to each answer. The question is 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.

respond 1The hypothesis predicts what will happen between dependent and independent variables, and the research process will test the hypothesis to determine what is confirmed by analysis of the data gathered (Grand Canyon University, 2018). An epidemiologic hypothesis is a relationship between the exposure (person, time, and place) and the occurrence of a disease or condition” (Grand Canyon University, 2018, para 9). The hypothesis becomes the forecast of what will occur between the independent and the dependent variable. At least one hypothesis is needed, although the study can have as many as the researcher feels is necessary. In most instances, both the null and alternative hypotheses are written to direct the investigation. The null hypothesis indicates the lack of relationship between the variables or that there is no effect on the variables. The data will show the null hypothesis to be either true or false. (G.C.U 2018).Let’s take, for example, breastfeeding and breast cancer. The simple hypothesis will be breastfeeding reduce the risk of breast cancer. The null hypothesis will be there is no relationship between breastfeeding and breast cancer. After research proves that breastfeeding reduces breast cancer risk, the null hypothesis will be rejected. There is a lot of research done on breastfeeding and breast cancer. The one that interested me is the National Library of Medicine’s research titled “Breast cancer and breastfeeding: collaborative reanalysis of individual data from 47 epidemiological studies in 30 countries, including 50302 women with breast cancer and 96973 women without the disease”. This study was done on women from 30 countries. The research proves that the longer women breastfeed, the more they are protected against breast cancer. Therefore, the lack of or short lifetime duration of breastfeeding typical of women in developed countries contributes to the high incidence of breast cancer in these countries. (NIH, n;d)Another example is vitamin C and the common cold. Hypothesis people who take vitamin C supplements are less susceptible to the common cold; null hypothesis vitamin C does not help prevent the common cold. After reading several articles regarding these hypotheses, I found that a lot of research had been done and proved that people who took vitamin C recovered faster from common cold than those who did not. National Library of Medicine did the study in the article “Vitamin C in the Prevention and Treatment of the Common Cold.” Conclude although there is a definite physiological effect from regular vitamin C supplementation on typical cold duration and severity, the practical significance of these findings is not very convincing. It does not seem reasonable to ingest additional vitamin C outside of dietary intake throughout the year if the only benefit is the potential for a slightly shortened cold duration and lessened symptoms. N;, B. A. W. (n.d.). After getting all the data, the null hypothesis failed to be rejected if the p-value was greater or equal to 0.05. Therefore, the hypothesis that vitamin C helps prevent catching a cold is erroneous.Nurses need to know the differences between the null hypothesis and alternative hypothesis concerning a specific topic when providing care to patients so nurses can provide evidence-based practice care. It will help the nurse not provide care based on shared knowledge but on the one that has been proven by research and scientists. Healthcare relies on statistics and evidence-based practice to improve patient care.; (n.d.). Breast cancer and breastfeeding: Collaborative reanalysis of individual data from 47 epidemiological studies in 30 countries, including 50302 women with breast cancer and 96973 women without the disease. Lancet (London, England). Retrieved April 4, 2022, from https://pubmed.ncbi.nlm.nih.gov/12133652/N;, B. A. W. (n.d.). Vitamin C in the prevention and treatment of the Common Cold. American journal of lifestyle medicine. Retrieved April 6, 2022, from https://pubmed.ncbi.nlm.nih.gov/30202272/Grand Canyon University (Ed). (2018). Applied statistics for health care. Retrieved from https://lc.gcumedia.com/hlt362v/applied-statistics-for-health-care/v1.1/

respond 2Hypothesis testing allows one to assess the data gathered from a sample of a population to determine if the correlation between the expected outcome and the proven outcome are so high that the results cannot be considered to have happened by change, and therefore, are a good indicator that the hypothesis would apply to the entire population (Banerjee et al., 2009).2 ways that research uses hypothesis testing:

1. Example 1: Research utilizes hypothesis testing when conducting clinical trials on new drugs. The researcher will measure patients’ symptoms prior to starting the new drug and then measure the patients’ symptoms again after they has taken the medication for 30 days to see if there are improvements in the patient’s symptoms. If their symptoms improved, it could be hypothesized that the new drug was effective in improving the symptoms

2. Example 2: Research also utilizes hypothesis testing to improve upon health care professionals’ workflows. The researcher could hypothesis that by placing all IV start supplies in one location, the nurse would be less apt to forget any needed supplies and it would take less time to start the IV because the nurse would only need to go to one location to retrieve every supply they needed. The researcher would conduct a time study and log each time the nurse forgot an item needed to start an IV for the 30 days prior to making any changes in practice, then would centrally locate all the IV supplies and collect the same data for another 30 days. The researcher would then compare the two time studies and logs of forgotten items to see if the length of time to start an IV decreased and the number of forgotten items decreased after the change in practice, to determine the validity of the hypothesis.

The criterion for rejecting the null hypothesis is based on the p value, or the probability of finding/not finding the sample result if the null hypothesis were true (Price et al., 2015). If the probability of finding the sample result is unlikely if the null hypothesis were true, meaning the value of p is low, then the null hypothesis is rejected, and the alternative hypothesis is accepted (Price et al., 2015). The value of p is considered low based on the a (alpha), or level of significance, which is usually set at .05, which means that if there is less than a 5% chance of finding the sample result if the null hypothesis were true then the null hypothesis is rejected (Price et al., 2015).

It is important as health care professionals to always be reviewing our practices, looking for ways to improve on patient outcomes and safety (Ambrose, 2018). Utilizing hypothesis testing, we can assess or compare what we currently know to be true (the null hypothesis) against what we think may have changed (the alternative hypothesis). If the findings are significant for proving that what we currently know to be true does not produce the sample result greater than 5% of the time, then we can assume that we need to change our practice (Ambrose, 2018).

References

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

Banerjee, A., Jadhav, S. L., & Bhawalkar, J. S. (2009). Probability, clinical decision making and hypothesis testing. Industrial psychiatry journal, 18(1), 64–69. https://doi.org/10.4103/0972-6748.57864

Price, P., Jhangiani, R., & Chiang, I-Chant. (2015, October 13). Understanding Null Hypothesis Testing – Research Methods in Psychology. Opentextbc.ca. https://opentextbc.ca/researchmethods/chapter/understanding-null-hypothesis-testing/

respond 3A hypothesis is the basis of all scientific research, as it proposes a testable statement, and in terms of healthcare, it is the relationship between the exposure and occurrence of the disease (GCU, 2018). The hypothesis will also indicate the independent and dependent variable, and their relationship to one another (GCU, 2018). At the end of the trial, the researcher should be able to either refute or deny their hypothesis using various methods of testing. To determine if there are differences or effects that occur in a population, a null and alternative hypothesis are stated as well (Laerd, n.d.). A null hypothesis is used to disprove the hypothesis and usually states that something equals zero while the alternative hypothesis is the opposite of the null, and is typically the main hypothesis of the study, which they are trying to prove (Laerd, n.d.). To test a hypothesis, researchers can either use a one-tailed or two-tailed test. If researchers are simply trying to prove one certain direction of data, versus a two tailed test determines differences between two groups being compared (GCU, 2018). Ultimately, the statistical data collected determines whether the null or alternative hypothesis are supported.In healthcare, we rely on data and statistics to improve patient care. When creating a study regarding patient care, a sample of a specific population is chosen to test the hypothesis. Depending on the study, it may be more appropriate for a two-tailed versus a one-tailed prediction. While a one-tailed prediction strives to prove one direction of the hypothesis, a two-tailed prediction allows for the result to be either negative or positive (Laerd, n.d.). For example, if the researchers were looking to test the positive effect of handwashing and reducing CLABSIs in a hospital, this would be a one-tail prediction since the direction has already been determined to be positive. If the hypothesis was changed to “Handwashing has an effect on CLABSI rates,” implies the outcome could be either negative or positive (Laerd, n.d.). No matter the hypothesis, it is important that all data collected is consistent so that it does now skew results, and thereby impacting patient care (GCU, 2018).References:Grand Canyon University (Ed). (2018). Applied statistics for health care. Retrieved from https://lc.gcumedia.com/hlt362v/applied-statistics-for-health-care/v1.1/

Laerd Statistics. (n.d.). Hypothesis testing. Retrieved from https://statistics.laerd.com/statistical-guides/hypothesis-testing-3.php

respond 4Hypothesis testing is a statistical procedure that the researcher designs to use the collected data, which aids in validating or invalidating a particular claim or question. This claim is usually about the population or a parameter of the population. The different research fields use inferential statistics through the test of hypothesis, which aids them in making inferences on the sampled data concerning the whole population. We can use different criteria to reject the Ho. We can use the p-value methods, where if the p-values are less than sure chosen levels like 0.01, 0.05, and 0.10, we reject the Ho. This concept is essential because it aids in deciding if something happened or not. And more so, determine if they are statistically significant at chosen level (Ferrill et al., 2010).Sekeyia

ReferenceFerrill, M. J., Brown, D. A., & Kyle, J. A. (2010). Clinical versus statistical significance: interpreting P values and confidence intervals related to measures of association to guide decision making. Journal of pharmacy practice, 23(4), 344–351. https://doi.org/10.1177/0897190009358774.

Question on #5 is 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.

respond5In health care, we can apply the hypothesis test to validate our results and draw a conclusion from the sampled data on the whole population. Confidence intervals aid the researcher in computing the possible range of values and, after that, estimate the precisions of the parameter values. The following is an example of hypothesis testing and the use of confidence interval in Health care research;Assuming we want to assess if the mortality rates in hospitals are different from the population parameter (µ=0). I will use a sample of 10 deaths to conduct hypothesis testing and construct the confidence interval. I will choose a significance level of 0.05 and a 95% CI. Here I will run a hypothesis, compute the 95% confidence intervals, and draw conclusions.Sekeyia

ReferencesRIGBY, J., CHUKWUKELU, G., PINEDA MENDOZA, J., & YEOW, J. (2021). Health Innovation Manchester as AHSS — the Test of a Hypothesis. International Journal of Integrated Care (IJIC), 21(3), 1–4. https://doi-org.lopes.idm.oclc.org/10.5334/ijic.5837

last questionIn your own language, give a synopsis of the relationship between a Hypothesis Test and a Confidence Interval.

all respond or question need atleast 1 reference.