Hypothesis Testing and Confidence Interval
The credibility of medical research depends on an appropriate selection of the sample population on which the research is centered. Whereas confidence levels utilize data from a sampled population to give an estimate of a population parameter in question, hypothesis testing uses sample data to determine the plausibility of a specific hypothesis. Hypothesis testing and confidence intervals are, therefore, inferential techniques that are dependent on sampling distribution approximations that determine the feasibility of a research question.
Confidence intervals express variations in the estimates of any given population parameter. It is an inferential statistical aspect that gives directions on the likelihood of a decision to be made. Confidence levels estimate unknown population parameters by inserting specific values into them (Izadpanah et al., 2018). Thus, values within the confidence intervals indicate reasonable estimations of that specific parameter, while values outside the interval are not reasonable. Hypothesis testing, on the other hand, focuses on a specific parameter and seeks to determine whether there is a similarity between the expression of a specified parameter in a population and the expression of the same parameter in the sample (Kock, 2016). This probability, also known as statistical significance, reveals deviations in population results from the sample results.
Confidence intervals are used concurrently with hypothesis testing. A hypothesized parameter done at higher confidence levels will always fail to reject the null hypothesis. This is also true for hypothesized parameters at lower confidence intervals, in that they mostly reject the null hypothesis and thus favor the alternative hypothesis (Javanmard & Montanari, 2014). Examples of how these inferential tools can be used include decision-making regarding two efficacious health remedies with different toxicity profiles but similar bioequivalence. Higher confidence regarding their toxicity profile will inform the decision on whether to continue their production.
Despite being statistical parameters used majorly in research, hypothesis testing and confidence intervals are important decision-making tools in routine hospital activities. An example of their usage is a bedside nurse in the general clinic who notices that most of her newly admitted patients present with severe characteristic abdominal pains and dehydration and have a near similar pattern of history, especially of location and lifestyle, and so is worried about whether to report this as a public health concern or not. In this case, the confidence interval will be for (cases with abdominal pains and dehydration)- (cases without abdominal pains and dehydration). Higher confidence intervals will mean higher confidence levels and will inform the nurse’s decision-making. In this case, she will be more likely to report this as a public health concern.
Hypothesis testing and confidence interval values are, therefore, important. Their significance in informing decision-making both in research and daily hospital routines is evident. Medical research requires the concurrent use of these two inferential techniques to inform an important decision on the research question. Their correct evaluation and utility are, therefore, paramount.
References
Izadpanah, F., Nikfar, S., Bakhshi Imcheh, F., Amini, M., & Zargaran, M. (2018). Assessment of Frequency and Causes of Medication Errors in Pediatrics and Emergency Wards of Teaching Hospitals Affiliated to Tehran University of Medical Sciences (24 Hospitals). Journal of Medicine and Life, 11(4), 299–305. https://doi.org/10.25122/jml-2018-0046
Javanmard, A., & Montanari, A. (2014). Confidence intervals and hypothesis testing for high-dimensional regression. Journal of Machine Learning Research, 15, 2869–2909.
Kock, N. (2016). Hypothesis testing with confidence intervals and P values in PLS-SEM. International Journal of E-Collaboration, 12(3), 1–6. https://doi.org/10.4018/IJeC.2016070101
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Question 
Hypothesis Testing and Confidence Interval
Hypothesis Testing and Confidence Interval
Evaluate and provide examples of how hypothesis testing and confidence intervals are used together in health care research.
- The occupation is nursing