Provide two different examples of how research uses hypothesis testing, and describe the criteria for rejecting the hypothesis. Discuss why this is important in your practice and with patient interactions.
In order to provide examples of uses for hypothesis testing it is important to understand what a hypothesis is. The hypothesis statements are the blueprint for completing the statistical analysis of the gathered data. (Ambrose, 2018) Testing a hypothesis evaluates if the prediction made on two variables is valid. Gathering data to show correlations is how you test a hypothesis.
The two examples that came to mind when thinking of a hypothesis in health care are the prediction that washing hands decreases infections and following a specific diet will lead to increased health. The hypothesis indicates the lack of relationship between the variables or that there is no effect on the variables. (Ambrose, 2018) The criteria to reject a hypothesis is data proving that the hypothesis is false. When the research is reliable, patterns should occur. When there are patterns, this will permit the researcher to reject the hypotheses. (Ambrose, 2018) Hypotheses are tested using a one or two tailed testing. (Ambrose, 2018) A one-tailed test is used when the researcher is sure that direction of data will go in a certain way. A two-tailed test identifies both the positive and negative differences. (Ambrose, 2018)
As health care professionals, it is important to be able to utilize hypotheses and being able to evaluate them. Nursing interventions and care for patients are products of evidence based practice guidelines, which can be every changing with new research and data. Being able to interpret and utilize these guidelines is important to positive patient care. Nursing improvements and positive patient outcomes can be determined by quality research.
Ambrose, J. (2018). Applied Statistics for Health Care. Retrieved from Grand Canyon University: https://lc.gcumedia.com/hlt362v/applied-statistics-for-health-care/v1.1/#/chapter/3
Predictions of what will happen in a study between two variables is a hypothesis. A hypothesis is needed prior to starting the research to guide the data that is needed to be gathered for the variable. With the data that is collected, research process validates a hypothesis by showing correlation or mutual relationship. Research is always a measure in in a study or understanding differences. Hypothesis testing is used to establish whether a research hypothesis extends beyond those individuals examined in a single study. (Laerd, 2018)
One example is two groups of students virtual and in person. The hypothesis could be that in person students did better than virtual students in test taking, showing in person learning is important for the education of school age students. Another example could be that daily zoom meetings or virtual class has a higher classroom average of the students compared to the students who attends zoom virtual classes biweekly. For both, examples the average of test in grades for the sample represents the population.
In health care it shows findings that can be empowered and shows alternative explanations that may or may not be significant without research and hypothesis testing advancements could be detrimental for care of those that need advanced medication or treatment. Sample of 100 breast cancer sufferers depend on research to have new chemotherapy treatment to eradicate cancer more effectively than current treatment type. Consider the process first involved in what your measuring then outlines your structure. This is important for the patients and given opportunity to have treatment that has evidence to work versus medication that has not worked and no evidence to work.
Criteria for rejecting the hypothesis: the hypothesis indicates the lack of relationship between the variables or that there is no effect on the variables.(Ambrose, 2018) Data should show patterns, allowing the researcher to reject or fail to reject the hypothesis. (Ambrose, 2018) Keep in mind a rejection could be in error. The rejection based on probability results are accurate. If shown effect, rejected. A failure to reject the hypothesis indicates no effect is shown by the evidence. (Ambrose, 2018) For a to be rejected, 95% of values need to set close to the mean.
Ambrose, J. (2018). Applied statistics for health care.Retrieved from https://lc.gcumedia.com/hlt362v/applied-statistics-for-health-care/v1.1
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