The Use of Hypothesis Testing and Confidence Intervals in Healthcare

The Use of Hypothesis Testing and Confidence Intervals in Healthcare

The Use of Hypothesis Testing and Confidence Intervals in Healthcare

In biomedical research, investigators rely on hypothesis testing and confidence intervals to draw relationships among several aspects, collect data to test those relationships, and attempt to conclude the relations based on the collected data. Specifically, hypothesis testing is used in medical research to assess the strength of evidence from a specific sample and offer a mechanism for establishing determinations that relate to the population (Rana & Singal, 2015). Besides, hypothesis testing allows for understanding the extent of reliability one can deduce observed results in a study sample to the larger population that provided the example. Lolan et al. (2015) present that confidence intervals complement hypothesis testing in medical research as it allows for assurance (confidence) concerning how precise the data are. Concisely, confidence interval entails a numerical range to describe research data assisting in establishing the reliability of the research data. The confidence level width often shows data reliability. A full critical interval depicts a lower precision and less credible values, while a narrow critical range indicates the reverse.

One example that illustrates hypothesis testing and confidence level at the workplace may relate to examining the patients that are likely to have hypertension. For instance, the medics have decided on the average figure of 95% as confidence. A given sample of patients with hypertension may have a mean blood pressure of say 120mmHg. That mean can be considered to fall within the confidence level of 95%, especially when the calculated range was between 110 and 130mmHg. In this case, it can be reported that patients with hypertension have a mean of 120mmHg. This indicates that other samples from the same population are of the population are likely to fall within the confidence level.

Reference

Rana, R., & Singhal, R. (2015). Chi-square test and its application in hypothesis testing. Journal of the Practice of Cardiovascular Sciences, 1(1), 69.

Ionan, A. C., Polley, M. Y. C., McShane, L. M., & Dobbin, K. K. (2014). Comparison of confidence interval methods for an intra-class correlation coefficient (ICC). BMC medical research methodology, 14(1), 121.