Evaluate and explain how hypothesis testing and confidence intervals are used in healthcare research. Nursing- Hypothesis Testing and Confidence Intervals Provide a workplace example that illustrates your ideas.
Nursing- Hypothesis Testing and Confidence Intervals
Hypothesis Testing and Confidence Intervals
A confidence interval is a range of possible figures/values that can capture an unknown variable given a specific degree of confidence (Perdices, 2017). On the other hand, hypothesis testing allows researchers to draw a conclusion on the possibility of a certain hypothesis using sample data from the population of interest. There are two types of hypotheses: the null and the alternative hypotheses. A null hypothesis states no statistical difference between two variables, while the alternative hypothesis and an alternative hypothesis are the opposite of the null hypothesis (Perdices, 2017). P-values and alpha values can be used to draw a conclusion. Let’ say the alpha value is 0.05. If the alpha value is smaller, the null hypothesis is rejected, and the alternative hypothesis is accepted, but if larger, the null hypothesis is accepted (Shreffle & Huecker, 2021).
There is a relationship between confidence intervals and hypothesis testing. Hypothesis testing and confidence intervals share the same characteristics since they are both inferential techniques. Inferential techniques utilize a sample to test the strength or validity of a hypothesis or estimate a population variable. The difference between confidence interval and hypothesis testing is that hypothesis testing focuses on the null hypothesis variables while confidence interventions focus on the population variables’ estimates. Both confidence intervals and hypothesis testing can be utilized to draw a conclusion.
We used evidence-based medicine to inform our practice and interventions in my workplace. We utilize our clinical judgment to determine the clinical significance of research studies by carefully analyzing study design, sample size, data analysis, hypothesis testing, and confidence intervals. We understand that statistical significance may not be equivalent to clinical significance. We are encouraged as healthcare professionals to use our experiences and knowledge to determine the applicability of study results and draw conclusions by understanding practical implications and not only statistical significance.
References
Perdices, M. (2017). Null hypothesis significance testing,p-values, effects sizes, and confidence intervals. Brain Impairment, 19(1), 70-80. https://doi.org/10.1017/brimp.2017.28
Shreffle, J., & Huecker, M. R. (2021, March 25). Hypothesis testing, P values, confidence intervals, and significance – StatPearls – NCBI bookshelf. National Center for Biotechnology Information. https://www.ncbi.nlm.nih.gov/books/NBK557421/
