The difference between statistically significant evidence and clinically significant evidence lies in their respective meanings and applications. Statistically significant evidence refers to a measurement of data that indicates the results are unlikely to have occurred by chance (Polit & Beck, 2017). It provides an assessment of the reliability and replicability of the results. On the other hand, clinically significant evidence focuses on the practical implications of the research findings when applied to patient care.
Statistical significance is based on the application of p-values, which are used to determine the likelihood of obtaining the same results if the study were to be repeated multiple times. A p-value of 0.05 (or 5%) implies that in more than 95% of the cases, the results will be consistent (Polit & Beck, 2017). This statistical significance is important as it helps researchers determine if the data is leading them in the right direction and if the results can be considered reliable.
However, statistical significance alone is not sufficient for making decisions in clinical practice. Clinically significant evidence takes into account the potential benefits and risks associated with research findings when applied to patient care (EL-Masri, 2016). It requires an informed expert judgment of the practical implications of the research and whether the findings will have a meaningful impact on patient outcomes.
To advance an evidence-based project, both statistically significant evidence and clinically significant evidence play important roles. Statistically significant evidence provides a foundation for determining the reliability and credibility of the research findings. It helps researchers identify if the data supports their hypotheses and if the observed effects are likely to be real rather than due to chance.
Clinically significant evidence, on the other hand, helps bridge the gap between research findings and their practical application in patient care. It considers factors such as the magnitude of the effect, the clinical relevance, and the potential risks and benefits associated with the intervention or treatment being studied. By evaluating the clinical significance, researchers can assess the potential impact of their findings on patient outcomes and healthcare practice.
In an evidence-based project, statistically significant evidence helps researchers determine the direction and validity of their research findings. It provides support for the effectiveness of interventions or treatments being studied and guides decision-making in terms of whether the results are likely to be reliable and replicable.
Clinically significant evidence, on the other hand, helps guide the implementation of research findings into clinical practice. It provides insight into the practical implications of the research and whether the intervention or treatment will have a meaningful impact on patient outcomes. By considering both statistically significant and clinically significant evidence, researchers can ensure that their evidence-based project is grounded in both sound statistical analysis and practical implications for patient care.
