Researchers conduct testing that establishes statistical significance, whereas clinically significant evidence should be proven by experts in the field. They determine whether a statistical significance is essential for clinical practice. Therefore, the researcher should carefully interpret whether the results are clinically significant.
Statistical significance is one of the main aspects of hypothesis testing. It includes testing involving a sample from a population group, especially a sample of patients with a specific disease. During an experiment, there might be an occasion when the difference in the sample from the entire population (sampling error) determines the observed effect (McShane et al. 2019). The result is considered statistically significant on the premise as long as its receipt is provided without any sampling error. Clinical significance is the term that depicts the changes in the patient’s condition and laboratory parameters that can be associated with the medication testing. It points to the importance of making decisions regarding the further management of the patient. Statistical significance can not imply importance or practical value (Schober et al., 2018). For indication, the functional importance of the medical response to the concept of clinical significance is used.
For the usage of statistically significant evidence and clinically significant evidence, first of all, it is necessary to understand and interpret the distinction between them accurately. Considering only statistical significance, researchers can report results that, in practice, have no value. The “P” value, often used to measure statistical significance, represents the likelihood that the study results do not give an accurate account. Statistical relevance is highly dependent on the study sample; with large sample sizes, even minor treatment effects that might be clinically unimportant take on special significance in statistical reports.
References
Schober, P., Bossers, S. M., & Schwarte, L. A. (2018). Statistical significance versus clinical importance of observed effect sizes: what do P values and confidence intervals really represent? Anesthesia and analgesia, 126(3), 1068.
McShane, B. B., Gal, D., Gelman, A., Robert, C., & Tackett, J. L. (2019). Abandon statistical significance. The American Statistician, 73, 235-245.