Statistics is the science of collecting, organizing, and analyzing data. Statistics are divided into two main branches namely descriptive and inferential statistics. It is important for healthcare providers to understand the concepts associated with statistics in research reports. The paper explores the role of statistics in healthcare, including the difference between statistical significance and clinical significance and its importance in healthcare.
In the healthcare industry statistics plays crucial roles in resource allocation, quality improvement, needs assessment, and product development. Statistical information is crucial in the determination of the combination of goods the healthcare industry needs to produce, and the kind of resource which needs to be allocated in the production of goods. Therefore, statistics in healthcare is crucial in allocative and production efficiency. The private and public healthcare administrators have a responsibility of providing continuums of care to diverse populations. As a result, they apply statistics in comparing the existing services to the needs of the community and this explains the role of statistics in the needs assessment. Statistics assists healthcare providers in measuring the successes or failures of healthcare institutions which helps in quality improvement. Statistics helped in the development of various products such as innovative medicine. Moreover, statistics influence the pricing of products by describing consumer demands in measurable units.
Statistical significance is not the same as clinical significance. Researchers should look beyond the point outcomes of statistical tests when interpreting clinical results. The range of possible results as defined by confidence intervals should be considered when making clinical recommendations. Investigators often use statistical tests to decide if an outcome is significant. For example, does a medication significantly improve a patient’s health compared to placebo? In literature, the answer to this question is often given as a p-value, a single figure indicating the meaningfulness of the results.
But what happens when the effects of the treatment being considered fail to show a significant result? Should physicians shy away from these therapies? The answer is no because such treatments may still have clinical relevance. Just because a medical treatment does not reach statistical significance does not rule out the possibility of being beneficial or harmful. Therefore, it is important to understand the difference between statistical significance and clinical significance. The researchers are at risk of missing potentially useful results by failing to look beyond the p-values and thus both the statistical significance and the clinical significance really matter in the healthcare practice.
