Notably, the confidence interval (CI) can be applied to medicine to enhance the quality of clinical decisions. He and Fineout-Overholt state that healthcare professionals may use CI to interpret research based on reliability and validity. Briefly, CI demonstrates the level of uncertainty related to the sample statistic. CI helps healthcare professionals determine the likelihood of expected results by comparing them with research findings.
One of the examples where CI is applied is when clinicians need to collect information about cancer patients. Essentially, it is practically impossible; thus, the data can be obtained from smaller groups, namely samples, within the population. Consequently, clinicians analyze the data from samples using the specific process named inference (He & Fineout-Overholt). The inference process cannot ensure data reliability because errors may appear due to statistics’ differences in various samples. Therefore, specific terms should be used when calculating the CI, such as standard error (SE), probability, and mean. The SE is used in the formula to calculate the CI’s lower and upper limits, which demonstrate where the population variable is anticipated to fall. Moreover, the SE illustrates the sample statistics’ accuracy of the population variable.
In other words, the CI refers to the range of values of a sample statistic within which healthcare professionals may forecast particular results if they replicate the study intervention and protocol. For instance, the CI is applied to evaluate the research data reliability and answer whether clinicians will obtain the same results when using the specific research (He & Fineout-Overholt). Thus, the CI can assist in evaluating the study findings’ correctness. Moreover, healthcare professionals can decide whether to rely on particular results based on the CI when planning patient care.
Work Cited
He, Zhaomin and Fineout-Overholt, Ellen. “Understanding Confidence Intervals Helps You Make Better Clinical Decisions.” American Nurse, 2021.