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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Introduction:
Hypothesis testing and confidence intervals are two statistical techniques frequently used in healthcare research. Hypothesis testing is used to determine the validity of a research hypothesis, while confidence intervals provide a range of values within which the true population parameter is expected to lie with a certain degree of certainty. In this response, we will evaluate how these techniques are used together in healthcare research and provide an example of their application in the workplace.
Answer:
In healthcare research, hypothesis testing and confidence intervals are often used together to evaluate the statistical significance of research findings. Hypothesis testing involves formulating a null hypothesis, which assumes no significant relationship or effect exists between two variables, and an alternative hypothesis, which assumes the presence of a significant relationship or effect. Researchers use statistical tools like t-tests, ANOVA, and chi-square tests to determine if the null hypothesis should be rejected or not.
Once the null hypothesis has been rejected, researchers can calculate confidence intervals to determine the precision of their findings. Confidence intervals provide a range of values within which the true population parameter is expected to lie with a certain degree of certainty. A 95% confidence interval means that if we repeated the study many times, we would expect the true population parameter to fall within the calculated range 95% of the time.
For example, a healthcare researcher may want to know if a new medication reduces blood pressure. The null hypothesis is that the medication does not reduce blood pressure, while the alternative hypothesis is that the medication does reduce blood pressure. Through hypothesis testing, the researcher finds evidence to reject the null hypothesis. They can then calculate a confidence interval to determine the precision of their findings, demonstrating that the medication lowers blood pressure by a certain range, providing valuable information for doctors and healthcare providers.
In the workplace, healthcare researchers may use hypothesis testing and confidence intervals to evaluate the effectiveness of new treatment modalities, interventions, and health promotion programs. For instance, in evaluating a new exercise program designed to reduce the risk of developing type 2 diabetes, researchers could use hypothesis testing to test whether the program is effective at reducing blood sugar levels. If the null hypothesis is rejected using hypothesis testing, they would then use confidence intervals to determine the range of blood sugar level reduction that may be achieved through the program, guiding policymakers and healthcare providers in developing effective treatment modalities.
