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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.
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Answer
Hypothesis Testing and Confidence Intervals
In research, both hypothesis testing and confidence interval are statistical inferential methods. In scientific studies, hypothesis testing is used to determine if a pre-formulated null hypothesis is to be rejected (guide decision on the assumption or theory on the parameter). Confidence intervals give information about a range in which the parameter’s true value lies with a certain level of probability, as well as about the demonstrated effect size. This allows for drawing of conclusions about the clinical relevance and statistical of the findings. The use of both measures in clinical studies is useful and important because they are complimentary in information provision. Essentially, hypothesis testing provides significance of an effect (statistical power), while confidence interval adds to it by providing the magnitude of the effect.
An example of their complimentary use is in determining the level of a certain health practice in a certain populace. Specifically, one would be interested in assessing smoking rates in a certain populace, and assume a certain proportion of the population smoke. Data from a sample can be used to estimate the confidence interval of the proportion based on a probability (confidence level). If the hypothesized proportion does not fall within the estimated range, then hypothesis testing is used to ascertain the exactitude.
Also, one can be interested in determining the number of outpatient clinical visits in a facility within a certain hour to determine service effectiveness. Sample from various hours can be recorded and analyzed, with the hypothesized number being tested. Further, a confidence interval is calculated to expand the study to include margin of error based on a probability (confidence level). This is the case in which the hypothesized mean falls within the confidence interval.
In my workplace, the introduction of a new drug or treatment to curb disease is so essential and has to be grounded on empirical proof. In a research example, a new intervention is administered on the test group and a placebo on the control group. Then the confidence intervals of the effects on each group are calculated and compared. If they do not overall, then there is no difference between the effects. However, it is expected that the new intervention will be more effective, so the confidence intervals are expected to overlap. After confirming the overlap, hypothesis of effectiveness of the new intervention is tested to affirm the plausibility from confidence interval.
References
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Attia, A. (2005). Why should researchers report the confidence interval in modern research? Middle East Fertil Soc J, 10(1), 78-81.
Appendix
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Appendix A:
Communication Plan for an Inpatient Unit to Evaluate the Impact of Transformational Leadership Style Compared to Other Leader Styles such as Bureaucratic and Laissez-Faire Leadership in Nurse Engagement, Retention, and Team Member Satisfaction Over the Course of One Year
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