Hypothesis testing has now been regarded as one of the most widespread means of conducting scholarly research in the sphere of health care. The phenomenon of a hypothesis concerns scholars’ ability to present a verbalized concept they are willing to investigate throughout the research (Shreffler & Huecker, 2021). Later, the hypothesis is either justified or rejected with the help of analyzing empirical sampling data and inference of the sample study results. A similar approach to the data analysis in health care is the notion of a confidence interval. The latter stands for a quantitative range of mean values relevant to an outlined sample (Lock et al., 2021). The most commonly accepted confidence interval (CI) value constitutes 95%, which means that the researchers may be 95% sure that the true value for the population lies within the interval presented.
When speaking of the procedures’ application to health care, it is crucial to account for the presence of hypothesized value in the research hypothesis. For example, when evaluating the disease incidence rate based on a single criterion such as reposting cases of a heart attack in the past, medical researchers may use CI as a single means to identify the probability of a certain health complication within the population with previous heart attack experience when compared to the general population (Lock et al., 2021). However, once the outlined hypothesis secures more details to the population parameter, such as age group (sample participants should not be older than 30 years old), hypothesis testing should be conducted.
In the case of clinicians, it is crucial to combine both CI and hypothesis testing in order to prove the significance of an outlined assumption, as there is currently no vivid indication that one of the two approaches is more empirically significant to the medical practice (Shreffler & Huecker, 2021). When speaking of personal experience and statistical testing, the most common example would concern the evaluation of drug efficacy. Thus, the CI would concern the probability of the drug’s successful intake when compared to other medications of the same group, whereas the hypothesis testing would include the p-values indicating the difference between the successful use among smaller groups with different hypothesized values such as age or gender and social affiliation.
References
Lock, R. H., Frazer Lock, P., Lock Morgan, K., Lock, E. F., & Lock, D. F. (2021). Statistics: Unlocking the power of data[E-book]. Wiley. Web.
Shreffler, J., & Huecker, M.R. (2021). Hypothesis testing, p-values, confidence intervals, and significance [E-book]. In StatPearls (StatPearls Publishing). Web.